Once you decide to move your application to a cloud platform, you need a way to manage your infrastructure and resources. If you choose Microsoft’s Azure as your cloud computing service, you have two options. You can write your Azure Resource Manager (ARM) templates using either JSON or Bicep for resource provisioning.
In this article, we will focus on why you should use Bicep and how you can use it to strengthen your infrastructure.
What is Bicep?
Bicep is a language designed specifically for writing Resource Manager templates. It lets you declaratively deploy Azure resources. For example, if you try to use JSON, it might complicate things as it requires complicated expressions. You can avoid that with Bicep.
For example, check the Bicep and JSON templates below.
The first one uses Bicep, while the second uses JSON. Notice the additional syntaxes used in JSON template. To get a better understanding, we need to know how Bicep works.
How Bicep works
Bicep works in two ways.
Azure’s Resource Manager, just like other resource managers, still requires a JSON template. However, writing one requires additional work, as shown in the image above. To make this process, easier Bicep has developed its own set of syntaxes, which can be easily translated to JSON. Bicep uses a technique called Transpilation, during which your Bicep template is converted into a JSON template. The transpilation process happens automatically when you submit the template. You can run this process manually as well.
Bicep allows you to define expressions, parameters, variables, strings, logical operators, deployment scopes, resources, loops, and resource dependencies. It also allows you to reference resources easily and output properties from a resource in the template.
You can further compare JSON and Bicep syntaxes here.
If you already have some ARM templates and you are now deciding to use Bicep, you can easily convert your old ARM templates using Decompilation. For this, you need to use the Bicep CLI.
Once converted, you might notice that the original ARM template which was in JSON and the newly converted template, has different syntaxes. This is caused by converting an ARM template that was initially in JSON to Bicep and again back to JSON. However, when deployed, the converted templates also produce the same results. You might also notice some best practice issues in the converted template. So it is better to do some revisions to the template to incorporate those best practices.
There are a lot of pros to using Bicep for template writing. We have listed them below.
Bicep is open source and free. You can find the Bicep repo here. It is also supported by Microsoft.
Bicep provides a simpler syntax for writing resource manager templates. You can declare and reference variables and parameters directly without writing any complex functions. Bicep templates are easier to read and understand as opposed to JSON templates. Additionally, you do not need prior knowledge of programming languages.
If your Bicep template seems complex, you can always break it down into separate modules and add them to your main Bicep file when you need them. Modules enable you to simplify your code and manage it easily.
Integration with Azure services
Bicep comes integrated with other Azure services such as Azure template specs, Policy, and Blueprints.
Automatic dependency management
Bicep comes with a built-in mechanism to detect dependencies among your resources. This comes in handy during template authoring.
Support for all resource types and API versions
Bicep supports all preview and general availability (GA) versions for Azure services. Therefore, As soon as a resource provider introduces new API versions and resource types, you can use them.
No state or state files to manage
All state is stored in Azure. You can use the what-if operation to preview changes before deploying the template. This allows you to deploy changes confidently.
Bicep provides extensions for Visual Studio Code includes rich syntax validation and IntelliSense for all Azure resource type API definitions. This feature enhances the authoring experience.
You can easily convert your existing ARM templates to Bicep ng the decompilation to decompile JSON files to Bicep.
Cons of using Bicep
Like many languages, Bicep also has cons. Lucky for us, there are only a few of them.
Bicep is only for Azure
Currently, Bicep is only available for Azure, and there are no plans to extend it. However, they are planning to provide extensibility points for some APIs that are outside of Azure.
No support for apiProfile
An apiProfile is used to map a single apiProfile to a set apiVersion for each resource type. Bicep, however, does not support the concept of apiProfile.
Bicep is newline sensitive
You cannot write code snippets such as if conditions in multiple lines. You have to always write it in a single line which causes readability issues.
No support for single-line objects and arrays
Bicep does not support single-line objects and arrays such as [‘a’, ‘b’, ‘c’].
No support for user-defined functions
Bicep does not support custom user-defined functions.
Since Bicep is native to Azure, it’s easier to use it inside Azure. Other cloud providers such as AWS do not support Bicep. In such cases, you might have to look for other options such as open-source tools like Terraform. If your company uses only Azure, then Bicep is the best solution. It will automatically support all the new features from Azure as soon as they become available. It is also fully integrated within the Azure platform. Azure provides the Azure portal to monitor your deployments. Since it’s built specifically for this task, it is easy to write ARM templates with it.
In the previous article, we talked about what Kafka is and its ecosystem. We also talked about how Kafka works and how to get started with it. In that article, we also discussed several important concepts like Message, Producer, Topic, Broker, and Consumer. These are the basic concepts of Kafka and will be needed for all our future sessions.
Moving on, in today’s article, we’re going to discuss the administration of Kafka. The goals of this session would be Configuration files, Scalability, Performance introduction, and Topics administration. Like the previous lesson, we will discuss another set of essential keywords: Consumer group, Partition, Segment, Compaction, Retention, Deletion, ISR, and Offset.
As you remember, we discussed Consumer and Producer in our previous session. In it, I’ve mentioned that Producers and Consumers are working together to achieve a common goal. To understand how the Consumer and Producer would work and scale together, we should first understand the Consumer group.
A consumer group is composed of multiple consumers. They have a set of workers who share the same goal with cooperation to consume data or read messages from topics. Usually, your application handles the customer groups, and they are intelligent enough to cooperate together and to be coordinated into the way that you can scale them. They will read the messages from one or more partitions.
For example, Let’s assume that we want to consume from the bank account topic and the bank account topic has ten partitions. Suppose we create a consumer group that has a set of consumers, which we will read from the topic. So, if we have multiple Consumers inside the same group, they will be able to read from the same topic, but divide the number of partitions among the number of consumers in the same group.
How Partitions are tied with Producers and Consumers
As shown in the figure above, multiple producers (left side) can write into one partition or the same topic (Topic partitions). The consumer groups (right side) can read from the topic partitions. The topic partitions are divided among a consumer group.
This means that every single consumer in a group can only read from one partition.
You can see in the above-given figure that each arrow is straightly pointed to one partition. Two consumers reading from the same partition is not possible according to the architecture.
Hence, when choosing a number of partitions, developers have to keep in mind how much they want to scale their consumers.
The most important concept that you must know is that whenever you want to write to a partition or write a message, that message comes in order. For example, if you write on multiple partitions, the messages will be written in a timely manner, which means that they are sorted according to the timestamps.
Note that when there are multiple partitions, they are ordered by the time within the same partition. Hence, when you read the topic as a whole, it is never guaranteed that the messages are read in the written order.
How a message ends up in a partition
The producer is in charge of sending a message to the partition. The basic function of a partition is to grab the content, put it in a message, and send it to the topic partition. There are methods to find the corresponding partition for a message. Among them are delegating Kafka libraries and using a “producer partitioner.” A producer partitioner maps each message to the corresponding topic partition. Hence, no worries to the developer about where the message is sent as long he uses a producer partitioner.
Why partitions are shipped with Kafka
Partitions shipped with Kafka libraries always guarantee that the same kind of message (sharing the same non-empty key) is mapped to the same partition. The function used to achieve this task is explained as follows;
There will be a key inside the message. Once the message is sent to a sensor and entered, the key (For example, the key is 123). The partitioner will hash the key using the algorithm “murmur2” and divide it by the number of partitions (where the message with key 123 ends up in partition number 5).
Likewise, every message that shares the same key will end up in the same partition. Therefore, it is important to use something that changes over time as the key. For instance, you can add the timestamp as the key.
Note that all the messages over time will spread among the partitions because it is important to understand as a concept that partitions are logical parts of a topic. Being a logical part, we should expect that topic into wall is divided in size by the number of partitions. In other words, if our topic is 100 GB with ten partitions, over time, each partition will be around 10 GB in size. This way, the load can split among the brokers.
We will see slightly a bit more about what happens when brokers don’t add the same amount of data; that might lead to disasters. It is our responsibility to keep an eye on topics, the size, the wall, and the partition size per each topic and broker size.
What is a Kafka Segment
Each partition represents a segment in Kafka. So basically, we will have active segments as many as partitions by topics.
Now let’s consider Kafka installation with only one topic, which is ten partitions. When Kafka starts, it will basically create ten segments. So there will be ten open files. Every time we write into one of those files, Kafka will send the data to that specific segment. The segments grow in size over time.
At some point, when we close a segment or archive it and we create a new segment. This is very important for Kafka because Kafka only keeps on writing on the active segment. The old segment will be considered closed. If you want to read the old information from the topic and partition, Kafka will also retrieve the old segment. But, Kafka doesn’t consider it is handy to keep a segment open forever. The reason for this is a performance reason and logical reason for Kafka. Basically, every time Kafka has to clean up the data, it will look in old segments.
Why you might want compaction
The concept of segments is important to be understood as it is tied with the concepts of compaction and deletion. If you are familiar with the database, we can relate it to an update of a record. Basically, what’s happening is that sometimes we might want our applications or our users to have the visibility of the last value of a record. We don’t want to record all the historicals but only to see the last state.
For example, if we have an appointment with our doctor, we call and set the appointment for tomorrow. When we want to change the appointment, we just call again to change it to the day after tomorrow. So, there will be two messages created for Kafka; the first is the “Appointment_no1” and the second is “Appointment_no1_Update”. Now the user is not interested in knowing when was the first appointment. He is just interested in when he has to go to the doctor (the day after tomorrow).
To know how compaction relates to this when the second message is generated, Kafka will check whether it already had a message like that. If the answer is a “Yes”, the older message is marked as deleted, and the new message is kept.
Why you might not want compaction
What we have to consider is that compaction is not for everybody and not for old situations. For example, if we want to record the temperature of a sensor, we don’t want compaction. Because we need the historical data of the sensors. Hence, if your topic is recording any historical data, then never use compaction.
If you want only the latest status, use compaction.
Note that compaction is triggered every number of minutes, which is configurable, and the records that are deleted are not the ones in the active segment. As mentioned earlier, Kafka only writes on the active segment, and once it is archived, it won’t be active any longer. When we do compaction, we can write to a file that is not opened. So it is difficult for Kafka to delete records within the same segment.
So when you consume your messages from scratch, you will still see the last record. So the application will always be written in a way that only the last message is kept.
Deletion is similar to compaction when it comes to non-active segments. But the concept is different. The deletion is an expiration of your messages. Going back to the last example of sensors, we only need to keep the data on the temperature of the last month. So, Kafka deletion will be configured to be triggered after one month of data. So today, we will be going for one month + one day ago of messages and so on.
More on compaction and deletion will be discussed in the near future.
ISR stands for In Sync Replica.
What is a replica in the first place?
A replica is where your data is replicated. For example, if we have 3 brokers and 1 topic with 1 partition, we can tell Kafka, ‘do replica 3’. Then every bit of the topic will be replicated over the 3 brokers. This is very important because in case 1 broker goes down, we still have 2 copies of the data.
Now, when every time we are going to write to this topic, the data will be physically replicated to the other two brokers. So at every point in time, there will be 3 copies of our data given that the brokers can keep writing and they are up and running.
When we are going to query the status of our data, Kafka will tell us that there are three in sync replicas. There is the first itself, which is considered as the in sync replica, and then there are two more copies. If one broker goes down, then the in sync replicas will only be two.
There is also the possibility that one broker cannot keep up with the rise because it has a slower disk, network failures, or the network is insufficient between the main broker and the replica, so replicas won’t be in sync any longer. That is an important concept to understand because if a replica is not in sync, that means that we don’t have 3 copies of our data but less.
ISR – From the official documentation
The ISR official documentation says about In Sync Replica as follows;
“As with most distributed systems, automatically handling failures requires having a precise definition of what it means for a node to be “alive”. For Kafka, node liveness has two conditions.
A node must be able to maintain its session with ZooKeeper (via ZooKeeper’s heartbeat mechanism)
If it is a follower, it must replicate the writes happening on the leader and not fall “too far” behind.
According to the above-given points, they need a program to understand when a node is live and when it is not. So they introduced the mechanism where every broker keeps a session with a ZooKeeper via its heartbeat. Let’s say it’s a few seconds by default, and the broker will let ZooKeeper know that it is alive via messages.
If one of these messages is lost, then the ZooKeeper starts to question whether the broker is alive. If a number of heartbeats are lost, then the ZooKeeper will mark that broker as not alive and is not in sync any longer.
In another condition where the broker is marked “not in sync” is when it is too far behind.
More replicas mean we can have a better performance. Because more producers are spread over multiple replicas, we can fish data over more brokers. If one broker goes down, another one can take over, so we reach high availability by just setting a replica more than one. So we have 3 brokers, and if one broker goes down, then we will have another broker taken to replace the other.
Every partition will be replicated a number of times. One of the three replicas will be the leader; that is, we call it the “main replica”. So, when you are going to write, you will be writing to the leader. The leader will forward the writes to the followers, the other replicas.
For example, if we have Kafka 0,1 and 2, and the leader is 0. Every time we write to 0, the write is also sent to 1 and 2. Assume that Kafka 0 goes down, so the leader is unreachable. There will be one In Sync Replica and a replica that is not marked by ZooKeeper and is up-to-date with the latest data, then one of them will be the new leader.
If we have the same number of replicas and brokers, and when one broker goes down, you are left with 2 brokers. For example, let’s assume that we have 5 brokers and 3 replicas. When our leader or one In Sync Replica dies, there will be a rebalancing. The rebalancing is the operation of spreading the data again among the survivors.
So, if our partition is replicated three times on Kafka 0,1, and 2, and one of the three dies, then Kafka 3 will take over. When it takes over, it has to grab all the data that is missing. This means that the very Kafka instance will have a very intensive write on the disk, and the instance that you are reading from will have intensive reads. So, the disks will be busy, which results in a Denial of Service.Page Break
Every time when a message is written on Kafka, the message has a bit of metadata together with this message. Metadata is some data that serves the real data. Metadata, in this case, is an offset, so do is a timestamp. The timestamp is the exact time when a message is received by Kafka.
The timestamp is important because one feature of Kafka is to easily be able to serve the messages and syncs the specific timestamp. This feature in Kafka is in the architecture, and it is possible to achieve with a database. Every time a consumer consumes the messages, it writes the offset of the message consumed last inside an internal topic, which is called the “__consumer_offsets”
Configuration files: Best Practices
Always refer to the official documents.
Keep all envs equal in resources is a big advantage.
Make sure your changes are applied (hey, k8s?!): write or do a Functional Test (FT)
Follow DTAP – Development Testing Acceptance and Production (Different environments used in software development in the IT industry)
Configuration files – Kafka
The configuration file is called by default as Server.properties
The syntax of the configuration file is key: value
The configuration file will create settings only for automatically created topics. The manually created ones are set by the developers.
The configuration file consists of the following configurations.
broker.id = unique id per Broker
Log.dir = Where your data files are located (If you move a disk, you must be aware that you should move the disk with the Log.dir to save your files)
zookeeper.connect = IP:Port (for each zookeeper instance)
min.insync.replicas = minimum number of ISRs you can have. When you write data on a Kafka, it then gets synced to the minimum number of replicas (minimum no = 2). If this number is not fulfilled, data cannot be served.
default.replication.factor= only for automatically created topics
num.partitions = only for automatically created topics (If it is not specified, it will be innovated.)
default.replication.factor= only for automatically created Topics
offsets.topic.replication.factor = topics containing offsets are helpful to the consumer.They are replicated and handled by Kafka internally. The suggestion is to set this number to 3.
transaction.state.log.replication.factor = used by the producer to guarantee exactly one’s production. Whenever there’s a network issue or a problem in the broker, the one who is producing the data always knows that the data is always written once.
auto.create.topics.enable = Enables you to create topics by themselves.
unclean.leader.election.enable = The leader is in charge of hosting the data. When the leader dies, Kafka needs to tell the clients that there’s a new leader, so there has to be an election to decide who that would be. The problem here is that do we need to serve the data anyway if the data is old or not because there might be no In Sync Replicas. (Suggestion is not to)
delete.topic.enable= should be disabled on Production. Enables you to manually delete topics.
replica.lag.time.max.ms = how behind an LSR can be? Don’t keep this setting too high because having a replica with a high time is not very helpful.
Note that settings about compaction and deletions will be explained in the next session.
Be careful with some settings
Something to know about the settings such as;
log.retention.ms is used to set how long you want to keep the data before it is deleted. There are two settings, one in milliseconds, minutes(log.retention.minutes), and hours (log.retention.hours). For how long you want it. Milliseconds cannot be set to big numbers like years, so you have to use hours.
If someone sets the log.retention to milliseconds, then milliseconds will take precedence.
No, stackoverflow.com, please.
Time invested in reading docs is not time wasted
The best place to refer to is the Official Documentation:
TickTime = the basic time unit, measured in milliseconds. It is used to regulate other settings.
The default value is 2000.
MaxSessionTimeout = default is 20 (Ticks).
InitLimit = large zookeeper installation requires larger values.
Kafka and ZooKeeper use the Log4j file to log. It is not the most intuitive logging facility for operations usually. It is a Java-based logging utility used by both Kafka and Zookeeper and comes with the default install. Don’t use INFO for production. It is too verbose, and you will lose sight of what’s happening.
A bit of hands on.. – Kafka-topics.sh
Kafka topics are the main tool we have to understand what is going on. It comes with the default installation. You need ‘–zookeeper’ or ‘–bootstrap-server’ to know where zookeeper or Kafka installation is. For that, you can have Kubernetes or call the Kafka client, which has all the tools. As you can damage the cluster, you should handle it with care. If in doubt, run in dev first.
You can try Kafka topics yourself on your cluster. If you are using Kubernetes, it comes as kafka-topics (without a .sh). Run the syntax and try to connect to the cluster.
kafka-topics.sh –zookeeper 220.127.116.11:2181 –list
I was explaining the main parameters. The first thing that might be handy to know is how many topics you need.
Topic – Topic name. You’ll see one line for each topic.
PartitionCount– How many partitions this topic is divided into. So you will see the partition count equal to the number of partitions you set in the configuration file if your topic is not overwriting this.
ReplicationFactor – How many times your topic is replicated over your cluster. If you have 3 brokers, the replication factor is 3 in the settings. If nobody overwrote it, you would see a replication factor of 3.
Configs – settings in configuration files.
For each topic, you will see one line per partition. Here, the topic has only one partition, and the first partition is always zero (they count from zero). So, topic 1 in partition zero has a leader who is broker 2. If you are running Kubernetes, your brokers are named from 0 to 2.
The number of replicas is 3, and we have the replicas displayed as 2,0 and 1. The order here is important because if a leader 2 dies, the next in line to take over will be 0, and the next in line will be 1.
These configurations can be changed by the administrator by passing a command in Kafka topics.
After replicas 2,0 and 1, you also have In Sync Replicas that are the most important bit of information that you need to acquire from your cluster. In Sync, Replicas must be under normal operations equal to the number of the replication factor. So basically, Replicas and InSync replicas must have the same values.
If we have 3 replicas planned, but In Sync Replica only has 2 items (let’s say 2 and 0). That means in that particular topic, broker number 1 is not up-to-date. We have to monitor In Sync Replicas and check and sync replicas. It’s up to you to find out what’s going on. It might be a slow disk, a bottleneck in the network, etc.
This topic has 50 partitions. That’s the default number of partitions for the internal topics. For example, where the custom offsets are stored, we see 50 lines from 1 to 49. The leader changes from partition to partition. The partitions are spread evenly among all the topics. It is important that partitions have fairly similar amounts of data in each of them.
Kafka-topics.sh can be used for:
Describe – specify how many partitions you want
Show Topics configured in a particular way (–topics-with-overrides)
Show what is not properly configured currently.
Tips and tricks
Delete can return OK, but nothing happens if delete is not allowed…
Delete does not actually delete, not immediately…
Homebrew monitoring? Kafka-topics.sh is a good ally.
A database is an organized collection of information (data). You can store data in a database and retrieve them when needed. Retrieving data would be faster than traditional data storing methods (Information written on paper etc..). Data stored in a database in key: value pairs.
Modern Databases follow the ACID standard. It consists of,
Atomicity: Method for handling errors. It guarantees that transactions are either committed fully or aborted/failed. If any of the statements in a transaction failed, then the transaction would be considered failed and operation will be aborted. Atomicity provides a guarantee on preventing the desired updates/modifications over the database partially.
Consistency: Guarantee data integrity. It ensures that transactions can alter the database state, only if a transaction is valid and follows all defined rules. For example, a database for bank account information cannot have the same account number for two people.
Isolation: Transactions do not depend on one another. In the real world, transactions are executed concurrently (multiple transactions read/write to the database at the same time). Isolations guarantee concurrent transactions are treated the same way if they happen sequentially (one transaction needs to complete execution for the next one to get executed).
Durability: Guarantees the once committed transaction, will remain committed even in case of a system failure. This means committed/completed transactions will be saved in permanent non-volatile memory. (Eg: Disk drive).
What is Kafka?
According to the main developer of Apache Kafka, Jay Kreps, Kafka is “A System optimized for writing”. Apache Kafka is an Event Streaming Platform, a fast, scalable, fault-tolerant, publish-subscribe messaging system. Kafka is written in programming languages Scala and Java.
Why the need for yet another system?
At the time that Kafka was born, which is 2011. SSDs were not common and when it comes to database transactions, disks were the first bottleneck. LinkedIn wanted a system that is faster, scalable, and highly available. Kafka was born to provide a solution for big data and vertical scaling.
When the need arises to manage a large number of operations, message exchange, monitoring, alarms, and alerts, the system should be faster and reliable. Vertical scaling is upgrading physical hardware (RAM, CPU, etc..) and it’s not ideal always and it may not be cost-effective. Kafka lets the system scale horizontally, coupled with a proper architecture.
LinkedIn later made Kafka open-sourced which results in improving Kafka over time. Before Kafka, LinkedIn used to ingest 1 billion messages per day, now it’s 7 trillion messages, 100 clusters, 4000 brokers, and 100K topics over 7 million partitions.
Strengths of Kafka
Decouple producers and consumers by using a push-pull model. (Decouple sender and consumer of data).
Provide persistence for message data within the messaging system to allow multiple consumers.
Optimize for high throughput of messages.
Allow for horizontal scaling.
What is the Publish/Subscribe model?
A message pattern that splits senders from receivers.
All the messages are split into classes. Those classes are called Topics in Kafka.
A receiver(subscriber) can register into a particular topic(s), and the receiver will be notified about the topic asynchronously. Those topics are generated by Senders/Publishers. For example, there is a notification for new bank account holders. When someone creates a new bank account, they will automatically get that notification without any prior action from the bank.
The publish/Subscribe model is used to broadcast a message to many subscribers. All the subscribed receivers will get the message asynchronously.
Core concepts in Kafka
Message: The unit of data within Kafka is called a message. You can see a message as a single row in a database. For example, We can take an online purchase transaction. Item purchased, the bank account used to pay, and information delivered can be called a message. Since Kafka doesn’t have a predefined schema, a message can be anything. The message is composed of an array of bytes and messages can be grouped in batches. So we don’t have to wait until one message to get delivered to send another message. A message can contain metadata like the value ‘key’ which is used in partitioning.
Producer: The application that sends data to Kafka is called the Producer. A producer sends and Consumer pulls data, while they don’t need to know one another. Only they should agree upon where to put and how to call the messages. We should always make sure that the Producer is properly configured because Kafka isn’t responsible for sending messages but only for message delivery.
Topic: When the Producer is writing, it’s writing in a Topic. Data sent by the Producer will be stored in a Topic. The producer sets the topic name. You can see Topic as a file. When writing into Topic, data will be appended and when someone is reading, they read from top to bottom. Topics starting with _ are for internal use. Existing data cannot be modified as in a database.
Broker: Kafka instance is called the Broker. It is in charge of storing the topics sent from the Producer, serving data to the Consumer. Each broker is in charge to handle assigned topics, and ZooKeeper holds who is in charge of every Topic. A Kafka cluster is a multitude of instances(Brokers). The broker itself is lightweight, very fast, and without the usual overheads of java like garbage collectors, page & memory management, etc… The broker also handles replication.
Consumer: Reads data from the topic of choice. To access data Consumer needs to subscribe to Topic. Multiple consumers can read into the same Topic.
Kafka and Zookeeper
Kafka works together with a configuration server. The server of choice is Zookeeper, which is a centralized server. Kafka is in charge of holding messages and Zookeeper is in charge of configuration and metadata of those messages. So everything about the configuration of Kafka ends up in Zookeeper. They work together ensuring high availability. There are other options available, but Zookeeper seems to be the industry choice. Both are open-sourced and they work well together.
Zookeeper is very resource-efficient and designed to be highly available. Zookeeper maintains configuration information, naming and provides distributed synchronization. Zookeeper runs in the cluster and Zookeeper cluster must be an odd number (1,3,5..). 3 and 5 guarantees high availability. 1 instance does not because if 1 instance goes down, we lose Zookeeper. Commonly Zookeeper is installed on separate machines than Kafka works because Zookeeper needs to be highly available. There are official plans to abandon Zookeeper to be used with Kafka and in the future, only use Kafka.
The downloaded tarball comes with all the binaries, configuration files, and some utilities. System d files, PATH variables adjusted (added to PATH), and configuration for your requirements are not included.
Kafka stores data as Topics. Topics get partitioned & replicated across multiple brokers in a cluster. Producers send data to topics so Consumers could read them.
What is a Kafka Partition?
Topics are split into multiple Partitions and parallelize the work. New Partition writes are appended at the end of the segment. By utilizing Partitions, we can write/read data with multiple Brokers, speeding up the process and thus reducing bottleneck and it adds scalability to the system.
A Topic named topic name is divided into four partitions and each partition can be written/read using a different Broker. New data will be written at the end of each Zpartition by its respective Broker.
Multiple Consumers can read from the same Partition simultaneously, When a Consumer reads, it will read data from the offset. Offset is basically like a timestamp of a message provided by the Producer saved in metadata. Consumers can either read from the beginning or read from a certain timestamp/offset.
Each Partition has one server called Leader(That’s one Kafka Broker which is in charge of serving that specific data), and sometimes more servers act as Followers. Leader handles all read/write requests for the Partition while Followers passively replicate Leader. If a Leader fails for some reason, one of the Followers automatically becomes the Leader. Therefore Leaders can change over time. Every Broker can serve as the Leader for some data. Each Broker is a Leader for some Partitions and acts as a Follower to other Partitions, providing load balance within the cluster. Zookeeper provides information on which is the Leader for a certain part of data.
The whole world have advanced into the digital age and as a lot of appliances, gadgets, and accessories now depend on internet for their operation. These devices are designed with state-of-the-art technology so they can communicate smoothly at any time, and have now become so popular that they outnumber the human population. There are approximately 7.62 billion people around the world but surprisingly, we have 20 billion IoT devices which are all connected to the internet.
New IoT devices emerge every day; we see home automation systems, smartwatches, smart gadgets, smart vehicles and a long list of other things that makes your life easier and more fun in today’s world.
Through my work in Innovation at Gunnebo Business Solutions, I get to work on quite a few cutting edge projects to bring Gunnebo into the connected future. The GBS team strives to develop a scalable collaboration platform that supports each business units digitalization and software offering. Our main focus is actually to lead Gunnebo’s business units into the digital future of Software Services and enable product as a service sales.
I am currently working on a really exciting project with our Safe Storage Business Unit. We are working on a brand new smart-safe, which can easily be integrated into different parts of the home – kitchen, bedroom or bathroom – to store your valuables. The safe is designed to suit everyday needs and it can be used for storing valuables such as car keys, jewelry, credit card, visas, passports or any other thing important to you.
The safe is designed to be a simple and convenient solution that can be accessed by customers around the world. Anyone interested in getting the best security for their valuables would try out this option. Not only does the safe keep your valuables safe, but it’s also aesthetically appealing and made from the best technology which is only even more attractive.
As any smart device, this safe will of course be easily be connected to the owners mobile phone and send telemetry to the cloud. This is where I come in. I am working with our team in Markersdorf on merging classic and mechanical parts of a safe securely with modern IoT technlology.
To make sure that our new IoT device deliver to its potential, it is developed with state-of-the-art technology, both physically and on the firmware and software side, that makes it reliable and easy to use.
To ensure the efficiency of our operations we work with agile partners like Microsoft, 3H, Polytech Software and others to help fusing entrepreneurial spirit with professional development of the product. Through their involvement, we have been able to achieve optimal results.
As mentioned earlier, the Internet of things (IoT) is a system of interrelated computing devices, mechanical and digital machines. This means that it can be just anything from your television to your wristwatch. Over time, the scope of IoT devices has changed from what it used to be due to the convergence of multiple technologies, real-time analytics, machine learning, commodity sensors, and embedded systems.
An IoT device exposes its users to a number of impressive benefits which include increased interaction between devices, allows great automation and control, easier to operate, saves time, saves money, increased efficiency and time saving and so on. But it still has a few drawbacks of its own such as may easily become highly complex, may be affected by privacy and security breach, reduced safety for users and so on.
The market for IoT devices is expanding every day and becoming more popular as its number of users also increases. This might be the first IoT device from Gunnebo, but it is definitely not the last.
Having worked as a product owner for years, I finally decided to take things to take the next level with a certification training known as a Certified Scrum Product Owner.
A CSPO course is an interactive course that would last for two 8-hour days. During this course, we learned basic things about the scope of Scrum and the functions of a Scrum Product Owner. We were taught using case studies, exercises, and discussions. More importantly, topically treated included how to identify user needs, backlog, how to manage stakeholders, an overview of sizing in Scrum and how to create, maintain and order a product.
The CSPO Training was conducted by Chris Sims. He’s a certified scrum product owner, agile coach and C++ expert that helps companies run efficiently and happily. He’s also the founder of Agile Learning Labs and a co-author for two best-sellers, namely; The Elements of Scrum and Scrum: a Breathtakingly Brief and Agile Introduction.
The CSPO training session was held in Silicon Valley, midway between San Francisco and San Jose, at the Seaport Conference Center. The facilities here were perfect for the setting of the training, and as a bonus, we got to see the towing of a drug houseboat (that was our theory at least).
A Scrum Master works to help an inexperienced team get familiar with the operations and effects of Scrum. In comparison, a Product Owner Owner priority is to make sure that customers are satisfied with the quality of service they get and usually helps to create the product vision or order for a Product Backlog.
At the end of the training, a CSPO is equipped with the skills to serves as a product owner in a scrum team. The role of the product owner is vital to make sure that the product can offer the desired amount of satisfaction to the customer when required. This is possible for him in a number of ways if you consider the resources available at his disposals such as the team, business stakeholders and the development process adopted by the organization.
A CSPO is trained to take on the role of the product owner in a scrum team. The product owner is a vital element in ensuring that the product can offer optimal value to the customer in a timely manner. He can achieve this in a number of ways if you take factors such as the team, business stakeholders and the development process of the organization.
The responsibilities of a CSPO
The first is the development and writing of the product vision. To do this, he’s to work with a clear mind about the functions and benefits of the product to the consumer. It also includes writing a list of product features. Basically, product features are product requirements written from the user’s perspective. These features are usually written as a detailed description of the capability of the product in the hands of the customer.
The CSPO also helps to compile a list of features into the Product Backlog. It’s important that the product owner has the ability to make the team understand the scope of the project and work together to get things done. He also reviews, tests, and assesses the final product. A CSPO can also request changes to the product if there are any issues with it.
Getting a Certified Scrum Product Owner® (CSPO®) certification exposes anyone to a lot of benefits. Firstly, the CSPO certification will expose you to more career opportunities and it becomes easier to work in different industry sectors that adopt the use of Agile. This will expose any expert to different companies and occupational positions. Also, it shows that your expert in Scrum. This way, it’s easier for you to let your employees and team members know of your capabilities.
On another note, the certification will teach you the history of the Scrum Foundation and the role of a Product Owner. The classes to train you for the certification will orientate you on the roles and duties of a product owner. It also takes you into close contact with Agile practitioners who want to improve their skill level. A CSPO certification is a sign of a product owner’s reliability.
Scrum teams operate at a level of efficiency and speed that may be a problem for traditional product management. Learn about the skills adopted by product owners to lead their team and achieve optimal results. Anyone who takes part in a CSPO training will be a part of exercises and simulations related to Business value estimation, Product strategy, an overview of product owner role, Release planning, Effective communication with stakeholders, story splitting, acceptance criteria, user stories, product strategy, lean product discovery and Artifacts including burn charts.
Working with Scrum for quite a few years now, I have assembled a set of methodologies and syntaxes on how to write good requirements for your team. Below I will share the requirement format and lifecycle I use in my daily work, and I hope it will help you too working in an Agile team.
Software development teams work on very complicated projects. It is crucial to understand every requirement and feature required by the customer.
An epic is a large body of work broken down into several tasks or small user stories. It always denotes a high-level descriptive version of the client’s requirements. As epic is the description of the user’s needs, its scope is expected to change over time. Hence, Epics are always shipped in the form of sprints across teams. While, Epics often encompass multiple teams on multiple projects, and can even be tracked on numerous boards. Moreover, epics help the team break down a main project’s work into shippable pieces without disturbing the main project’s delivery to the customer.
For a<persona>who<has a painpoint>the<product or solution>is a<type of solution>that<solves an issue in a certain way>unlike<the old solution or competitor>our solution<has ceirtain advantages>
Success criteria<>Acceptance criteria<>In scope<>Out of scope<>
An Epic can only be created and moved into the backlog by the Product Owner. When all sub-tasks are Resolved, the Epic can be resolved. When the functionality of the Epic is delivered to the end customer, the Epic will be Closed. It is a complicated task to create an Epic. The following steps should be followed to develop an agile epic.
They are starting with the user Recording / Reporting, which includes drafting the epic for project managers and the team. Second, comes the Description where the process of achieving the proposed project is described. Next is the Epic Culture, which denotes the epic team’s size based on the company culture. Finally, the most important one is the Timeline or Time Frame, where the team decides on how long they take to complete the project.
When a developer team develops one extensive software system, there will be lots of requirements gathered from the customer to understand what is precisely the customer’s requirement. The customer might not have an understanding of how the gathered requirements are used, but the development team knows that these requirements are finally the features of the system being developed.
A feature is a small, distinguishing characteristic of a software item, which is also a client-valued function. Features are small and typically can be implemented within a sprint. When we describe a feature, we use the same format as a User Story, but with a broader scope.
As a<particular class of user>, I want to<be able to perform/do something>so that<I get some form of value or benefit>
A Feature can only be created and moved into the backlog by the Product Owner. When all sub-tasks are Resolved, the Feature can be resolved. When the functionality of the Feature is delivered to the end customer, the Feature will be Closed.
A feature can be added to a system as per the customer’s requirement even after development is completed or during the development phase. The user creates a feature, and the features are added to the features inbox. The product team sorts the features and adds them to a feature list for the feature team for elaboration. The feature manager contacts the appointed teams to start inspections. After implementing the feature by the engineering team, it is added to the release tracking page, and once it is completed, the QA team will carry out the final testing. The feedback team starts feedback gathering, and the feature moves to Aurora and Beta. Finally, the feature is released.
When working on a complex project, the development team must ensure that they have fully understood the customer’s requirements.
In software development and product management, a user story is an informal, natural language description of a software system’s features. User stories are often written from the perspective of an end-user or user of a system. Furthermore, user stories break down the big picture into epics that are more user-focused and in a way that the engineering team clearly understands the product requirements.
As a<particular class of user>, I want to<be able to perform/do something>so that<I get some form of value or benefit>
Given<some context>When<some action is carried out>Then<a particular set of observable consequences should obtain>
A User Story can only be created and moved into the backlog by the Product Owner. When all sub-tasks are Resolved, the User Story can be resolved. When the functionality of the User Story is delivered to the end customer, the User Story will be Closed.
The stakeholder gives the idea in the form of a change request or new functionality, captured by the product owner as a business request, and creates the user story. Then the user story is added to the backlog, and with the help of the sprint team, it is groomed by the product owner. The user story is then broken down into acceptance criteria for prioritization. However, whether the owner accepts or rejects the story depends on the acceptance criteria. Finally, the user story is recognized as complete and closed and returned to the backlog for future iterations.
The Task Story work item is more technical than an agile User Story. Instead of forcing the User Story format, it is better to use a Feature-driven development (FDD) process, describing what is expected more technically. FDD blends several industry-recognized best practices into a cohesive whole. These practices are driven from a client-valued functionality perspective where its primary purpose is to deliver tangible, working software repeatedly on time.
Example: Send the Push Notification to a Phone
Given<some context>When<some action is carried out>Then<a particular set of observable consequences should obtain>
A Task Story can only be created and moved into the backlog by the Product Owner. When all sub-tasks are Resolved, Task Story can be resolved. When the functionality of the Task Story is delivered to the end customer, the Task Story will be Closed.
Any software development team can come across faults in the product they are working on, and these faults are identified in the testing phase.
Errors, flaw, or fault in a computer program or system that causes it to produce an incorrect or unexpected result or behave in unintended ways is called a software bug. The process of finding and fixing bugs is termed “debugging” and often uses formal techniques or tools to pinpoint bugs, and since the 1950s, some computer systems have been designed to also deter, detect or auto-correct various computer bugs during operations.
Found in<module>summary<short description>reproduced by<reproduction steps>result<what happened>expected<what was expected to happen>
The Bug work item can be created by anyone but is usually made by QA or Operations via a customer. When the bug is fixed, it should not be closed until confirmed by the creator.
There are six stages in the bug life cycle. When the bug is created and yet to be approved, it is in its New stage. Next, it is Assigned to a development team. Now the development team starts to work to fix the defect. When the developer fixes the bug by making necessary changes to the code and verifying them, it can be marked as Fixed. When the code is in the fixed state, it is given to a tester to retest until the tester tests the code in a state called the Pending Retest. Once the tester has tested the code to see if the developer has successfully fixed the defect, the status is changed to Retest.
Although we have epics and user stories to break down complex projects and make it understandable to the engineers, there can still be confusion.
A Spike aims at gathering information to sort out the unclear sections the team comes across in the user stories. A spike can be known as research, architectural, or refactoring spike. When the group comes across such confusing situations, they have to create a functional or technical experiment to evaluate. It can be any type of research the team does, the final goal is to solve unclear requirements.
In order to<achieve some goal>a<system or persona>needs to<perform some some action>
Example: In order to estimate the “push notification” story a developer needs to research if Azure services meets the requirements.
A Spike can be created by anyone, but can only be moved into the backlog by the Product Owner. The print team has the responsibility to create acceptance criteria. When Spike’s goal is met, it can be Resolved or Closed, depending on the owner’s decision.
Stories are written in a way that is easy to understand by the customer, and there are no technical terms or instructions related to development. Now the story has to be converted to a detailed instruction list that is easy to understand by the developer.
A Task is a piece of work for the developers or any other team member. It gives the developer an idea about what should be done during development, such as creating tests, designing something, adding codes, the features that should be automated, etc.
There is no specific format for a task, it can be written in the format of a note or a todo list.
A task can be created by anyone, but it is typically created by a developer as a child to a User Story or a Task Story.
A New task can be Created as a user action or part of process execution, and Candidates are set to groups of people. Next, individuals are directly Assigned as a part of process execution or if requested by API. Sometimes an assignee might want to Delegate a part of the work. Once the requested work is resolved the assignee will want to pass the work back to the original owner. Finally, the task is Completed.
An Issue is a description of an idea or a problem. It also can be outlined as an improvement that should take place in the product. If resolved, it would increase the value of the final product or reduce waste in development time.
There is no specific format for an issue, it is more like a note and can be written in the format of a User Story or Spike.
Anyone can create an Issue, but only the Product Owner can convert it into a User Story or a Spike and put it into the backlog. The life cycle of work can be defined by setting an issue workflow as follows:
When an issue is created, the time is taken to resolve, it will be decided to depend on the issue’s size. When an issue is created, it is in its Open state. Usually, a QA will create an issue and assign it to a developer who can solve it. When the programmer is working on resolving the issue, it is in its In Progres state. After the issue is solved, it goes to the Resolved state. An issue can go to its Closed state only if the creator is happy with it. However, when an issue goes to its closed stage, it does not mean that it is completely solved, but there can be chances for it to arise again. Then the issue is Reopened, and the same process takes place to figure out the issue and fix it.
Concluding this post, I want to say that Chris’ training skills were at the top level and all of his stories about Silicon Valley, how he started Agile Learning Labs, and his career as a product owner, engineering manager, scrum owner, software engineer, musician, and auto mechanic – and there were impressive lunchtime discussions.
All good things come to an end and LEAP is no exception. It was a great week full of interesting and enlightening sessions. Day 5 was a fitting end to the week with its focus on Design with best practices.
Let’s get to the sessions; the day began with a keynote by Derek Martin on the topic Design for failure. Derek is a Principal Program Manager and spoke about what not to do when designing a product. He spoke about building Azure and how the lessons learned can be used to understand and anticipate challenges.
The focus was given to managing unexpected incidents not only in the application environment but also in the cloud as a whole.
Brian Moore took over with his keynote on Design for Idempotency – DevOps and the Ultimate ARM Template. He is a Principal Program Manager for Azure. The focus of the session was on creating reusable Azure Resource Manager Templates and language techniques to optimize deployments on Azure. The intention of these reusable templates is to introduce a “Config as code” approach to DevOps.
He took his time to explain “the Ultimate ARM Template” and other key points about the template. Brian Moore explained that the Ultimate ARM Template utilized utilised any language constructs to increase the impact of minimal code. The template simply looks to simplify all of your work. It also offers a variety of benefits for all of its users to enjoy. To guarantee the efficiency of ARM, he explained the practice to avoid. It’s a template which provides you with the best options for the most effective results and lack nothing essential.
Alexander Frankel, Joseph Chan, and Liz Kim conducted their join keynote on Architecting well-governed environment using Azure Policy and Governance after the morning coffee break.
They illustrated real-life examples of how large enterprises scale their Azure applications with Azure Governance services like Azure Policy, Blueprints, Management Groups, Resource Graph and Change History.
The next session was on Monitor & Optimize your cloud spend with Azure Cost Management and was conducted by Raphael Chacko. Raphael is a Principal Program Manager at Azure Cost Management.
The keynote’s main focus was optimizing expenditure on Azure and AWS through cost analysis, budgeting, cost allocation, optimization, and purchase recommendations. The main features of Azure Cost management were highlighted.
It was right back to business after a quick lunch break. Stephen Cohen took over with his session on Decomposing your most complex architecture problems.
Most of the session was spent on analyzing and coming up with answers to complex architecture-related problems raised by participants. It was a very practical session and addressed many commonly faced issues.
The next session was conducted by Mark Russinovich, the CTO of Microsoft Azure.
Day 5 had a shorter agenda and was concluded with Derek Martin returning for another keynote on Networking Fundamentals. Derek spoke about Azure Networking Primitives and how it can be used to leverage the networking security of any type of organization using Azure environments. Azure Networking Primitives can be used in a flexible manner so that newer modern approaches to governance and security protocols can be adopted easily.
And that was it. The completion of a great week of LEAP. I hope all of you enjoyed this series of articles and that they gave you some level of understanding about the innovations being done in the Azure ecosystem.
I just left Microsoft Headquarters after another interesting day at LEAP. Today’s topics were quite interesting, especially DevOps, because of all the innovations that are being made. I’m actually a little emotional that there’s just one more day remaining.
Jason Warner began the day’s session with his keynote on From Impossible to Possible: Modern Software Development Workflows. As the CTO of Github, Jason shared much of his experience regarding the topic.
The underlying theme of the keynote was on creating an optimal workflow that leads to the success of both the development process as well as the team. He pointed out the inevitable nature of modernization and said its important that the company does not become a mediocre or get worse.
Before he went on to the topic of the day, Jason spoke about himself. He also didn’t hesitate to share some valuable history and information about his life. Jason Warner introduced the audience to some brief insight into the capabilities of GitHub and the success they have managed to achieve so far.
According to Jason, to ensure proper modernisation must have a workflow that consists the following; automation, intelligence and open source. Next, he identified GitHub’s ability to produce the best workflows to improve company efficiency. It didn’t end there as he continued by talking about the benefits of workflow inflation
Abel Wang continued with the next session and his keynote was on Real World DevOps. Abel is a Principal Cloud Advocate for Azure. This session was truly valuable as it covered the full process of a production SDLC and many other important areas such as infrastructure, DNS, web front ends, mobile apps, and Kubernetes API’s.
At the start of his presentation, Abel Wang introduced us to his team and gave a run down on some vital information about DevOps. Why do you need DevOps? Well, they are solution providers, support any language and boast a three-stage conversation process for results.
After a much-needed coffee break, we embarked on the next session on Visual Studio and Azure, the peanut butter and jelly of cloud app devs. The speaker, Christos Matskas is a Product Marketing Manager at Microsoft.
The session focused on explaining how well Azure and Visual Studio support development, live debugging, and zero downtime deployments. Christos also spoke about leveraging integrated Azure tools to modernize .Net applications.
The goal of those at Visual Studio are committed to providing developers with the best tools available. It supports all types of developers and redefines their coding experience. The great thing about Visual Studio is that they don’t rest on their laurels and are constantly in search of innovation. It even comes with a Visual Studio Live feature that allows developers share content with each other in real-time.
Evgeny Ternovsky, Shiva Sivakumar jointly conducted the next session on Full stack monitoring across your applications, services, and infrastructure with Azure Monitor. Many demonstrations were performed to overview the capabilities of Azure monitor. The demos included monitoring VMs, Containers, other Azure services, and applications. In addition, setting up predictive monitoring for detecting anomalies and forecasting was also discussed.
Azure has a full set of services which it uses to oversee all your security and management needs. They have all the tools you need and are built into the platform to reduce any 3rd party integration. As if not enough, Azure managed to develop a set of newer features; partner integration, monitor containers everywhere, new pricing option, trouble shoot network issues later.
Subsequent to lunch, I joined the alternative session, which was on Artificial Intelligence and Machine Learning. The session was on the use of Azure Cognitive Services and using it with optimized scaling in order to optimize the customer care services provided organizations such as telecoms and telemarketers. Then we were back at another joint session by Satya Srinivas Gogula and Vivek Garudi and the keynote was on the topic Secure DevOps for Apps and Infrastructure @ Microsoft Services.
The speaker spoke about the wide adoption of DevOps practices and Open Source Software (OSS) and the vulnerabilities they introduce. The latter part of the session focused on best practices for secure DevOps with Azure.
The next keynote was on Transforming IT and Business operations with real-time analytics: From Cloud to the intelligent edge. It was jointly delivered by Jean-Sébastien Brunner and Krishna Mamidipaka and focussed on the challenges faced by IT and Business teams trying to understand the behavior of applications. The speakers explained the benefits of Azure Stream Analytics to ingest, process, and analyze streaming data in order to enable better analytics.
A good example of when Azure is at its best is that it can be used for earthquake and storm predictions.
Taylor Rockey concluded the day with his keynote on MLOps: Taking machine learning from experimentation to production. MLOps is an integration between machine language and DevOps. MLOps has proven to have numerous benefits including; scalability, monitoring, repeatability, accountability, traceability and so on. This platform had impressive features that make it a first-choice for many developers. The problems that many organizations face is the lack of proper understanding and tooling to use Machine Learning for production applications. The session focussed on the use of Machine Learning for production applications with the use of Azure Machine Learning and Azure DevOps.
And that’s a wrap. Don’t forget to tune into tomorrow’s article.
Day 3 of Microsoft LEAP was just completed. It was a day packed with many interesting keynotes regarding improving the availability and recoverability of Azure applications. By now, you know the drill, check out my notes on Day 2 here.
Mark Fussell and Sudhanva Huruli co-hosted the opening keynote on the topic Open Application Model (OAM) and Distributed Application Runtime (Dapr). Mark has been with Microsoft for nearly 2 decades and is now a Principal PM Lead. Sudhanva is a Program Manager. Both of them work on the Azure Service Fabric platform. The open application model was discussed in detail and the focus was on separating operational needs from development concerns.
Mark Fussell started by describing the topology of applications which many users utilised. He also stated that developers write each application to interact with different services. Then, Mark spoke about the reason behind the creation of Dapr. It was a designed as a solution to tackle the problems of microservice development. Dapr would allow the building of apps using any language on any framework. Microsoft is already onboard to tap into benefits which it offers. It offers the benefits of enjoying stateful microservice in any language.
Sudhanva Huruli’s speech on OAM was intriguing and revealing. According to him, the OAM was a platform agnostic specification to help define cloud native applications. Users can trust it’s quality because it was built by the largest teams at Microsoft and Ali Baba. It can be applied in a number of ways. It’s benefits include encapsulating application code, offering discretionary runtime overlays, discretionary application boundaries and defines application instances.
The program is fully managed by Azure, so that you can focus on applications.
The opening session was followed by another join session by Muzzammil Imam and Craig Wilhite who hold the positions of Senior PM and PM respectively. This keynote was on the topic of Windows Containers on AKS and it detailed the process of converting a legacy application into a cloud application and hosting it on a Windows container on an Azure Kubernetes service.
Their presentation showed that a lot of on-premise workload is done on windows; about 72%. There seems to to be a light at the end of the tunnel as there have been numerous good reviews about the Windows Container. It’s adoption is even growing steadily and there is room for more improvement. Microsoft containers will keep getting better with continuous innovation.
Kubernetes is a great option in Azure. It’s a vanguard in the future of app development and management and it can help you ship faster, operate easily and scale confidently. Azure Kebernetes Services will help you handle all the hard parts and give room a better future.
After the coffee break, we were back for the next session conducted by Brendan Burns on Securing software from end-to-end using Kubernetes and Azure. Brendan is a Distinguished Engineer at Microsoft. This session focussed on continuous delivery with Kubernetes. Some of the sub-themes were continuous integration with GitHub Actions, Access Control in Kubernetes, and Gatekeeper for Kubernetes.
The last session before lunch was conducted by Jeff Hollan, a Principal PM Manager for Microsoft Azure Functions. The keynote was on Serverless and Event-Driven functions for Kubernetes and beyond. To put it simply, they seem just like the features of Kubernetes.
The focus was on stateless event-driven serverless computing which is enabled by Azure functions. Many new hosting and programming models that enable new event-driven scenarios were discussed.
When used with severless, it allows developers focus on what really matters; their code. There are a variety of applications which it can be used as. Kubernetes also does well when dealing with event-driven applications
Next to speak was Kirpa Singh. He was a manager at Microservices and Performance Tuning. During his session, he spoke on what makes microservices a better option. He went on by speaking about the benefits of microservice architecture for projects. It was designed for large applications that require a high release velocity, complex applications that need to be highly scalable a, applications with rich domains or subdomains and so on. It offers users agility, focus, technology and isolation.
After lunch, we saw more of the Microsoft campus. Then it was back to the next session. The session after the lunch break was the OSS Architecture Workshop conducted by Jeff Dailey, Patrick Flynn, and Terry Cook. One of the core themes of the workshop was Open Source stacks. They spoke about building Hybrid resilient data pipelines and infrastructure using open source. This was done through a breakout session at which the attendees were separated into groups and drafted architectures that supported both on-premise and cloud deployments.
During this session, they discussed about Open Source. But why open source? It allows easier migration, deliver poly cloud options via APIs, drives Azure consumption and so on.
Mark Brown conducted the next session on Building high-performance distributed applications using Azure Cosmos DB. He is a Principal PM in the Azure Cosmos DB Team. The session’s key theme was building globally distributed cloud applications with high availability while ensuring extreme low latency. Many real-world demos were explored during the session and these will help us, developers, to tackle these issues in our own projects.
Hans Olav Norheim, a Principal Software Engineer, concluded the sessions for the day with a keynote on Designing for 99.999% – Lessons and stories from inside Azure SQL DB. The session focussed on building applications with almost 100% uptime while covering design choices, principles, and lessons learned that can be used in our own projects to overcome uptime issues.
Thus were the proceedings of Day 3. I conclude my note while looking forward to the next set of sessions with the theme Design for efficiency & Operations & DevOps. I’ll be publishing another article tomorrow.
I’m at Microsoft for LEAP and we just wrapped up another day of interesting discussions. If you missed my update regarding day 1, make sure to have a look at it here.
Today’s theme was Design for Performance and Scalability. Many legacy applications are being replaced because they are not performance-oriented and scalable at their core. This is something that has to be introduced right from the design stage. Today’s speakers covered many of the core areas which need to be optimized to enable both performance and scalability.
Vamshidhar Kommineni took us right from breakfast to using Azure storage for the data storage needs of Azure applications and how it can be used to enhance performance. Vamshidhar spoke about the innovations in the storage services layer made in the year 2019. He also shared briefly that plans for 2020.
Corey Newton-Smith was next and focused on IoT applications. Corry has been with Microsoft since 2003 and currently functions as the Principal Group PM for IoT Central. She shared the current state of IoT and Microsoft’s plans for the near future highlighting their vision.
Corey explains that Azure IoT represented a new era of digitization amongst industries. It was an innovation that allowed brands the ability to do so much more. The objective behind the production of this platform is enabling a digital feedback loop. She discussed that Microsoft had done so much to make the IoT better. Now, it was capable of bidirectional communication, can be scaled to suit enterprise of any size and provides end-to-end security. Microsoft was planning an improvement that would allow it to support scenarios that are not currently cloud feasible. What’s more? Everything can be tailored specifically to the exact solutions that you need.
The next session began after some light mingling during the coffee break. It was back to business with Jose Contreras and his keynote on decomposing Monoliths into Microservices.
Enterprise applications have made a gradual transition from being monolithic to being Microservice based. Jose explained strategies that can help with this process focussing on Memory, Computing, and Schema.He then discussed migrating existing monolith applications into Microservices without affecting ongoing operations. He focussed on the design, execution, and DevOps aspects.
Jose spoke on a number of factors to really prove the usefulness of transforming monolith to microservices. As part of his talk, he highlighted the factors to consider in making use of this service, differences between private and shared cache and considerations for using cache.
Interestingly, he moved on and started talking about Azure Compute. He listed all of their available services and gave detailed information on its hosting models, DevOps Criteria, Scalability criteria, and other criteria.
Clemens Vasters’s keynote focussed on how messaging is shaping enterprise applications. Importantly, he spoke on how Microsoft Azure could make all of it better. He is a Product Architect at Microsoft and highlighted how open standards and messaging can be used to move applications to the cloud. Some of the areas he touched on were Event Hubs, Service Bus, Event Grid, and CNCF Cloud Events, and Relay with web sockets.
According to him, users can use a series of options to connect a range of devices. Ease of connectivity is guaranteed by the use of intelligent edge or intelligent cloud. Basically, it can be applied to varying scales and still work well with Telco 4G/5G. Despite all of this, cloud services can be applied to create automotive and smart cities, support industrial automation and speed up processes.
Clemens continued by clearing the air on the standards which the cloud service operated on. Everything is built according to standards and designed to be secure. Such was the level of quality in display.
After a quick lunch break, an alternative session was conducted for those who were already familiar with the campus. This session on Messaging Guidance was conducted by Francis Cheung and was related to session 4. However, Francis focused more on how we could assess if some of those tools were a good fit for our projects. He also touched on managing and versioning message schemas.
Next was David Barkol’s session focusing on Designing an Event-driven Architecture on Azure through a workshop approach. He challenged attendees to solve problems related to messaging in a group setting. As a Principal Technical Specialist for Azure, David used his vast experience to reinforce the existing knowledge of attendees about Azure messaging services. He really had a lot of interesting things to say.
Using a few simple statements, he was able to highlight the problems of the customer, identify their needs and how to solve them with the use of event-driven architecture. As a platform, the event-driven architecture will eliminate any bottlenecks and allow for easier transmission of information. Azure messaging services will solve all of the demands identified by the consumer. He also mentioned that Event Hubs GeoDR will also provide a backup or secondary region.
Derek Li conducted his keynote next. He focussed on Serverless platforms based on Azure Functions and Logic Apps. Derek is a Senior Program Manager. His keynote focused on how serverless technologies have impacted how applications are built. He also spoke on how Azure Functions and Logic Apps can be used to speed up delivery.
The last session was after a very welcome Cola Zero break. It refreshed us for Rahul Kalaya’s keynote on deriving insights from IoT Data with Azure Time Series Insights. Rahul spoke about design choices, principles and lessons learned with regards to maintaining the highest possible uptime of cloud databases and servers. Many stories from his experiences with Azure SQL made the keynote even more interesting. And that was it. The completion of a day of meaningful sessions.
I look forward to sharing my next article on Day 3: Designing for Availability and Recoverability.
This year is already off to a fantastic start! I am so excited to be here at the LEAP conference at the Microsoft Headquarters in Redmond Seattle. LEAP is a perfect way for me to keep up to date with new technology and how to apply it here at Gunnebo.
The focus of the day was to Design for Security. The threat of cyber attacks and hackers is still as pressing as ever, so the need for cloud security is crucial. Although technological advancement has triggered an evolution in cloud security over the years, keeping the right level of visibility and control over their applications is still a challenge to many organizations. This means that finding a balance between cloud security and ease of use is a hard nut to crack. Today’s program discusses how Azure can cope up with this issue. Also, speakers are expected to introduce new and updated features Azure brought recently to improve the security of cloud applications.
The highlight of today’s program consists of five great keynotes. The first on the list was Scott Guthrie, the executive vice president for Microsoft’s Cloud. He is an incredible orator and kept the audience thrilled with his in-depth explanations on how Azure helps organizations to deliver product innovation and better customer experience securely. It was frankly impossible to have been there without taking away more than a few vital points and a better understanding of Azure.
Then Stuart Kwan, who is a principal program manager at Microsoft, was the next in line. He backed up Scott Guthrie with a great keynote on how authentication works on today’s applications. Stuart has a wealth of experience under his belt, and he has worked on identity and security-related technologies since joining Microsoft in 1996. Few people have more experience in that field. He is the guy to listen to on topics like Active Directory Federation Services and Windows Identity Foundation. The main focus was on OAuth, Open ID Connect, and SAML. OpenID Connect is a simple identity layer built on top of the OAuth 2.0 protocol. OAuth 2.0 defines mechanisms to obtain and use access tokens to access protected resources, but they do not define standard methods to provide identity information. OpenID Connect implements authentication as an extension to the OAuth 2.0 authorization process. It includes information about the end-user in the form of an id_token that verifies the identity of the user and provides necessary profile information about the user.
When Yuri Diogenes took control of the stage, everyone knew that his talk would be primarily based on how cloud security is evolving and becoming more mature. Yuri is a Senior Program Manager at Microsoft for Cloud and AI Security.
Before Yuri moved on to talk about Azure security, he provided some insights into the problematic scenarios that many companies find themselves. According to him, security hygiene has to be taken seriously or any cloud-based infrastructure would suffer. Basically, organizations have to protect themselves against modern-day threats. He carefully explained that Azure Security Center is a unified infrastructure security management system that strengthens the security posture of your data centers, and provides advanced threat protection across your hybrid workloads in the cloud – whether they’re in Azure or not – as well as on-premises. In simple terms, Azure security is the new security hygiene which you need.
Yuri went further to explain the benefits of Azure security center and Azure Sentinel. It provides all-round security and also affords a degree of customizability. According to him, Azure is capable of protecting Linux and Windows VMs from threats, protecting cloud-native workloads from threats, detecting file-less attacks, cloud workload protection for containers and so on.
The next person on stage was Nicholas DiCola who was a Security Jedi at Microsoft. He thrilled the audience with his discussions on the Azure Sentinel. He explained to everyone how the Sentinel functions as a cloud-native SIEM for intelligent security analytics for an entire organization. It offers limitless cloud speed and could be used at any scale. It also provides its users with faster threat protection and will easily integrate will all existing tools.
According to him, the Azure Sentinel was designed to collect visibility, helps in detecting analytics and hunting, investigates any incidents and respond automatically to them. Azure Sentinel gets data to function from numerous sources such as Linux Agent, Windows Agent, cloud services, custom app, appliances, azure services and so on. After collating all necessary data, it’s analytics scan for any possible threats. Then, you will now be able to monitor your data and activity.
Last but not least we had a session with Sumedh Barde and Narayan Annamalai. They opened a fascinating discussion on how to secure certificates, connection strings, or encryption keys and new networking capabilities of Azure. Sumedh Barde is Program Manager on the Azure Security team, and Narayan is the leader of the SDN product management group in Microsoft Azure that focuses on virtual networks, load balancing, and network security.
These two gave us great insight into the Azure Key Vault. They explained to us how it functions as a tool for securely storing and accessing secrets. From what I learned from the conference, the secret to tightly controlling and securing access on things API keys, passwords, or certificates is to use a vault. A vault is your very own logical group of secrets.
It was a great day here in Redmond and an excellent opportunity to brush up my knowledge of cloud security. I’m actively looking forward to tomorrow.