BYTE the Cloud

Chris 0:00
Hey, cloud engineers, welcome back for another deep dive. Today, we're gonna be looking at Amazon. MSK, okay. And you know, you might be thinking, Oh, isn't that just a managed Kafka service, yeah. But by the end of this deep dive, I think you'll see it can be a real game changer for your projects. It is. So without further ado, let's, let's dive right in.

Kelly 0:21
All right. So Amazon, MSK, basically it lets you use the power of Apache, Kafka, which, if you're not familiar, is a leading open source platform for real time data streaming. But the really cool thing is, you don't have to manage the infrastructure yourself. Oh, okay, AWS takes care of all that complex stuff, nice.

Chris 0:37
So it's all managed, yeah, all right. So can you give me, like, a real world example, you know, something that would really click for, you know, us cloud

Kelly 0:45
engineers Sure. Imagine, like you're building a financial application, and it needs, like, a really, really fast fraud detection system. Okay, so every transaction has to be analyzed, like in real time. Wow, in real time. Yeah, to cash any fraudulent activity before it can even happen, yeah? Okay, and that's where Ms Tay steps in. Okay, it can handle these huge data streams. We're talking like, millions of transactions every second, yeah? So you can, like, identify and stop fraud right away. Well, that's cool, and that's just one example. You could also use it for, like, real time dashboards, tracking user behavior on websites, yeah, okay, even like analyzing IoT data from all those connected devices. Oh, wow. So it's

Chris 1:24
a lot of different use cases, yeah. So it's like, this super efficient pipeline that can handle anything we throw at it exactly all right? So what are some of the key features of MSK that make it so powerful? So

Kelly 1:38
one of the biggest advantages is, like it's high availability and scalability. Okay, your data is replicated across multiple availability zones, right? So even if one zone goes down, your MSK cluster stays up. That's good. And when it comes to scaling, MSK can automatically adjust to handle those like unexpected traffic spikes or data surges, so you don't have to worry about your system crashing, okay, during those peak hours, yeah, or when you launch that new feature that everyone loves, right? And everybody's trying to use it at the same time, that

Chris 2:09
peace of mind is definitely a great benefit, yeah? So how does MSK fit into the larger AWS ecosystem?

Kelly 2:18
It actually plays really well with other AWS services, okay, good. For example, you can integrate MSK with S3 right to store your data long term, okay, or use it with AWS glue to, like, transform and process your data streams. Okay, it's like having all these different Lego blocks that you can snap together. That's a good analogy to build, like, a complete data solution.

Chris 2:40
Okay, so it seems like a pretty powerful tool, but I'm sure it has its limitations, right? You're

Kelly 2:46
right. It's not a magic bullet, okay, it's really designed for Apache Kafka, right? So if you need a more general messaging system, something like SQS might be a better fit, right? Okay, and while MSK takes care of a lot of like the heavy lifting, there are still some management tasks you need to be aware of, okay, such as, for example, you'll need to configure topics, manage consumer groups, yeah, and make sure that your data is securely stored and accessed, okay, so it's not completely hands off. Yeah, you still have some responsibility. Okay, that makes sense. Now for the moment, we've all been waiting for the exam prep. Let's imagine you're in the exam room and you get this question, what are the different types of scaling policies in Amazon? MSK? Okay, so MSK offers three main types of scaling policies, yeah, dynamic scaling, okay, scheduled scaling, okay, and predictive scaling, okay. So let's break down each one. Yeah, let's do it. Dynamic scaling reacts to changes in like, real time metrics such as CPU utilization or message throughput. Okay, so if things start to get busy, MSK will, like, automatically add more resources to your cluster, okay, to keep everything running smoothly. So

Chris 3:57
dynamic scaling is like an autopilot, yeah, that adjusts the cluster based on real time conditions exactly, all right. What about scheduled scaling?

Kelly 4:04
So scheduled scaling is more about planning ahead, okay, you define specific times when you want your cluster to scale up or down. Okay, so maybe you know you'll have, like, a big marketing campaign next week, uh huh, that will generate a surge in traffic, right? You can use scheduled scaling to, like, proactively adjust your cluster size, okay, to handle that like anticipated load. So it's

Chris 4:25
like setting a timer exactly to scale your cluster up for those like predictable events. Exactly. Very clever. Yeah, all right, now tell me about predictive scaling.

Kelly 4:34
So predictive scaling, as the name suggests, is all about predicting a future demand. Okay? It uses machine learning algorithms to analyze historical data and forecast how much capacity you'll need in the future. Wow. Okay, this is great for workloads that follow predictable patterns like seasonal trends or recurring events. Okay,

Chris 4:56
so we have dynamic scaling for real time adjustments so. Scheduled scaling for planned events, yeah, and predictive scaling for those predictable patterns, okay, so that covers the different types of scaling policies. But what about securing access to these clusters?

Kelly 5:09
Okay, so securing your MSK clusters is very important, of course, yeah, the first line of defense is IAM roles. Right? These allow you to define fine grained permissions for users and applications controlling who can access what. Okay. Next, we have security groups, right, which act like virtual firewalls, controlling network traffic to and from your MSK cluster. Okay? And finally, encryption is crucial, of course, yeah, for protecting your data, both at rest and in transit, right? MSK integrates seamlessly with KMS, so you can encrypt your data and rest assured that it's protected. Okay?

Chris 5:48
That covers access control encryption, but we also need to make sure our MSK clusters are performing as expected, right? So what are some essential monitoring tools and techniques? So

Kelly 5:57
when it comes to monitoring, CloudWatch is your best friend. Okay, it provides a wealth of metrics about your MSK cluster, like throughput, latency and consumer lag. You can also set up CloudWatch alarms to notify you if any of these metrics breach predefined thresholds, allowing you to be proactive and address any issues before they become big problems.

Chris 6:19
So CloudWatch is our go to for monitoring? MSK, exactly. Okay, cool. So we've covered scaling security and monitoring. We're making some great progress, but before we move on, are there any common pitfalls or points of confusion that you've seen people trip up on when they're taking the AWS exams? Yeah,

Kelly 6:40
one common mistake is not fully understanding the shared responsibility model. Okay? While MSK is a managed service, you're still responsible for certain aspects, like configuring topics, managing security settings and handling consumer groups. Okay, so don't fall into the trap of thinking that managed means you don't have to do anything, right? Also, remember that MSK is a powerful tool, but it's not the solution for every problem. Sometimes a simpler service like SQS might be more appropriate, so understanding the strengths and limitations of each service is essential. Yeah, that's

Chris 7:11
a great point. It's important to choose the right tool for the job. Yeah, okay, well, those are great tips. It's always helpful to know about those common pitfalls so we can avoid them on exam day Exactly. All right? Well, on that note, let's take a little break. When we come back, we'll delve into some more advanced MSK concepts and get you even more prepared for those challenging exam questions.

Kelly 7:32
So let's talk about how MSK handles these huge amounts of data. One of the key aspects here is storage. Okay? And MSK uses AWS, EBS volumes for this. And the cool thing is, you have choices depending on your performance needs. So if you need, like, ultra fast data access, yeah, you'd go with provisioned IOPS, SSDs, okay, but if you're working with data that's not as latency sensitive, then the more cost effective. GP, two volumes might be a better fit. Okay, it makes sense. And here's a little nugget that might pop up on the exam. Okay, I like these nuggets. EBS, volumes used by MSK are replicated within an availability zone, okay, adding an extra layer of durability to your data. Okay, good

Chris 8:16
to know. So that makes sense for storage. But what about connectivity and networking. How does MSK integrate with, like, the rest of our AWS infrastructure? So

Kelly 8:26
MSK clusters are actually deployed within your VPC. Okay, so you have like, really granular control over network security. You can use security groups to act as virtual gatekeepers, right determining which IP addresses and ports can communicate with your MSK brokers, okay, and here's a tip that often trips people up, okay, MSK supports both public and private access, okay, so if you need to connect from outside your VPC, you can set up public endpoints, right? But if security is your top priority, private endpoints using VPC endpoints are the way to go, okay, and this keeps all your MSK traffic within the AWS network. Got it. And for even more secure and private connectivity, MSK integrates with AWS private link, okay, which provides a dedicated network path for your data streams.

Chris 9:15
Okay, so lots of options for connectivity, yeah, all right. Um, so we've talked about storage, networking security. Can you give me some like, specific, real world examples of how companies are actually using MSK to solve their problems? Sure.

Kelly 9:29
So let's take like the world of E commerce. Okay, imagine a large online retailer processing like millions of orders every day. They can use MSK to build a real time order processing system, okay, so as soon as a customer hits that buy button, yeah, the order information is streamed through MSK, okay, instantly triggering inventory updates, payment processing even like personalized recommendations, wow, this creates, like, a seamless and efficient experience for both the customer and the retail. Dollar, yeah, that's

Chris 10:00
a fantastic example. It shows how MSK can handle these high volume real time data streams exactly that are so common these days. Yeah. Do you have any other examples? Sure.

Kelly 10:09
Let's look at like the financial industry. Okay, banks are using MSK to detect fraudulent transactions in real time. Okay, by analyzing transaction patterns and user behavior as they happen, they can identify and flag suspicious activity almost instantly, preventing financial losses, right? And protecting their customers. Okay,

Chris 10:30
yeah, that's super important. I can see how MSK is a game changer in these industries where real time insights are so important, yeah, all right, let's shift our focus back to exam preparation. So what kind of questions might they ask about topics, partitions and consumer groups?

Kelly 10:46
Okay, so let's break them down, one by one. Yeah, let's do it first up topics. Think of a topic as like a category or a feed for your messages. Yeah, producers send messages to a specific topic, right? And consumers subscribe to that topic to receive those messages. Okay, now each topic is further divided into partitions, which are like the key to scalability. Okay, each partition is handled by a different broker in your MSK cluster, right, allowing you to distribute the load and handle massive amounts of data, okay, without any bottlenecks. Okay,

Chris 11:19
that explains topics and partitions. But what about consumer groups? How do they fit in?

Kelly 11:23
So consumer groups are essential for ensuring that messages are processed efficiently, okay, and without duplication, right? A consumer group is a set of consumers that work together to process messages from a specific topic, okay, each consumer within the group is assigned to a specific partition, right, guaranteeing that each message is only processed by one consumer. Okay,

Chris 11:47
that makes sense. So we have producers sending messages to topics which are divided into partitions for scalability, and then consumer groups ensure that each message is processed efficiently by one consumer, exactly, all right. So how do you choose the right number of partitions for a topic?

Kelly 12:03
So choosing the right number of partitions is a bit of a balancing act, okay, too few partitions and you limit your throughput, right? Too many, and you increase complexity and overhead. Okay? As a general rule, the number of partitions should be at least equal to the number of consumers in your consumer group, okay, this ensures that each consumer has a dedicated partition to work with, right, maximizing parallelism and throughput, yeah, but you also need to consider future growth, right? If you anticipate needing more consumers down the road, you might want to start with a higher number of partitions to avoid having to, like, reconfigure your topic later. Okay,

Chris 12:39
good advice. So it's about finding that sweet spot between current needs and future scalability Exactly

Unknown Speaker 12:47
Okay.

Chris 12:47
Let's talk about data retention, message ordering and exactly once semantics. Okay, so

Kelly 12:53
let's start with data retention, okay, by default, MSK will keep your messages forever, okay, which might not be ideal for storage costs or compliance reasons, right? But you can configure retention policies, okay, to automatically delete messages after a certain time or when they reach a certain size, okay?

Chris 13:10
That makes sense. So we have control over how long we keep those messages around, exactly, all right? What about message ordering? Is there any guarantee that messages will be processed in the order they were sent.

Kelly 13:21
So the answer is, it depends. Okay, Kafka guarantees that messages within a partition will be delivered in order. However, if you have multiple partitions, yeah, there's no guarantee of order across those partitions. Okay, this is something to keep in mind when you're designing your applications, especially if the order of messages is really important for your business logic. Yeah, that's

Chris 13:41
a good point. Yeah. Okay, so order within a partition is guaranteed, but not across multiple partitions, right? Okay, now, what about exactly once semantics?

Kelly 13:51
So exactly once semantics is like the Holy Grail for a lot of streaming applications, okay? It guarantees that each message is processed only once, okay, even if there are failures, okay, achieving this requires careful coordination between producers, consumers and your application logic. MSK provides mechanisms to help you achieve this. Okay, but it's a topic that deserves its own Deep

Chris 14:15
Dive. Yeah, it sounds pretty complex. It

Kelly 14:17
can get quite complex, okay, well, let's

Chris 14:19
focus on some final tips and strategies for the AWS exams,

Kelly 14:23
okay, my advice is, don't just memorize features and definitions. Okay, focus on understanding the why behind each concept. Okay, why

Chris 14:31
are partitions important? How do they impact performance and scalability, right? Why would you choose one scaling policy over another? Yeah, okay, good point. The more you understand these principles, the better you'll be able to answer those scenario based questions that AWS loves to ask. Yeah,

Kelly 14:50
that's great advice. Any other tips?

Chris 14:52
Practice, practice, practice, of course, yeah, there are tons of resources out there, right, including practice exams, hands, online. And even free tier accounts where you can experiment with MSK, right? The more you work with it, the more comfortable you'll become, yeah, and the better you'll be able to apply your knowledge hands

Kelly 15:10
on. Experience is invaluable, yeah, and don't forget the AWS documentation, of course, it's a great resource. Yeah, always up today. Okay, so we've covered a lot of ground in this deep dive. But before we wrap up part two, I want to leave our listeners with something to think about. Okay,

Chris 15:25
so we've talked about MSK as a powerful tool for building real time data pipelines, but

Kelly 15:32
it can do so much more than that. Okay, imagine the possibilities in like machine learning and artificial intelligence. Oh, wow. MSK can be used to stream huge data sets for training ml models, crack model performance in real time and even deploy models for real time inference. Okay, it's a key enabler for building intelligent applications that learn and adapt as they go. That's

Chris 15:57
a great point. It really highlights how MSK is at the forefront of innovation in the cloud, yeah. So as you continue your cloud journey, think about how MSK can help you build the next generation of data driven solutions. Exactly. So we're back for the final part of our Amazon MSK Deep Dive. Okay, I'm excited to wrap this up. So let's sit back for a second and see how MSK fits into, like, the big picture of data architecture in the cloud. So,

Kelly 16:23
yeah, MSK doesn't exist on its own, right. It's a key part of, like, a larger data ecosystem. And one common pattern we see a lot is integrating MSK with data lakes, built on Amazon S3 okay, yeah, that makes sense. So you can use MSK to, like, continuously stream data from all sorts of sources, okay, directly into your S3 data lake, okay, and then that data is available for analysis, machine learning, all that good stuff. So

Chris 16:49
MSK is like a high speed pipeline feeding our data lake with all this information, exactly, right? That's cool, but once it's in the data lake, how do we actually make sense of it all?

Kelly 16:58
So that's where AWS glue comes in. Okay? Glue is a serverless Data Integration Service, right? That works really well with MSK. It can consume the data coming in from MSK, transform it into a usable format, okay, and then load it into other data stores like Amazon, Redshift or Athena, okay, and this allows for some really powerful analytics and insights. Yeah, that makes sense. So imagine like, building a real time analytics dashboard that's constantly updated with fresh data from your MSK pipelines. Wow.

Chris 17:30
Okay, that's pretty powerful. So we have MSK feeding our data lake glue helps us prepare and analyze all that data, and we can even see it all happening in real time. Pretty amazing, yeah. But the clouds always evolving, right? So what's next for MSK? Like, are there any new trends coming up? Oh, yeah, the

Kelly 17:48
cloud never stands still. That's for sure. One of the most exciting trends is the rise of serverless technologies, and MSK is right there on the leading edge. Nice. So AWS recently introduced serverless MSK, okay, which lets you create and manage your MSK clusters without having to worry about provisioning or managing any server. So

Chris 18:09
it's even more manageable. Yeah? Serverless MSK, that sounds pretty awesome. It takes away a lot of the overhead, yeah, exactly. So we can really just focus on building those applications. Yeah? Well, this has been a really great deep dive. We've learned a lot about Amazon MSK, but for those who are still learning, do you have any advice? My

Kelly 18:26
advice would be to learn by doing. Okay, there's no substitute for getting your hands dirty, right? Setting up your own MSK clusters, okay, configuring topics and streaming some data. And here's a pro tip. I love pro AWS offers a free tier for MSK, Oh, wow. Okay, so you can experiment and learn without spending any money. That's great.

Chris 18:45
All right, so don't be afraid to experiment. Exactly. Don't be afraid to make mistakes. Yeah, that's how we learn. That's how we learn. And don't forget the power of the community. Oh,

Kelly 18:53
yeah, there are tons of resources, online forums, blogs, all that. Lots of

Chris 18:57
places to go for help, exactly. All right. Well, that wraps up our deep dive into Amazon MSK, from the fundamentals to real world examples and exam tips, we've covered it all. We did it. So keep exploring, keep experimenting, and keep pushing the boundaries of what's possible in the cloud. And keep on clouding until next time.