BYTE the Cloud

Chris 0:00
All right, let's dive into a service that's, well, I think it's become pretty essential for cloud engineers, yeah, especially anyone who deals with real time data, and that's Amazon, Kinesis.

Kelly 0:10
It's a it's become really critical, especially as we see more and more data being generated in real time. Yeah, you know, it's no longer about just storing data, but about extracting insights from it and then being able to actually act on it as it happens immediately.

Chris 0:24
Yeah. So for our listeners who might not be as familiar, could you give us a quick overview of what Amazon Kinesis is exactly sure? So

Kelly 0:31
at its core, Amazon Kinesis is a fully managed service by AWS, okay, and it makes it actually surprisingly easy to collect, process and analyze streaming data at any scale. We're talking massive volumes of data from sources like, you know, like what? Give us some examples, website click streams, IoT, sensors, financial transactions, wow, really, anything that generates a continuous flow of information. So

Chris 0:58
it's kind of like that nervous system processing everything in real time. Okay, so I can see how this could be powerful for cloud engineers. But can you give us some concrete examples, like, how are people actually using it out in the real world?

Kelly 1:11
Oh, absolutely. So imagine you're building a platform for a FinTech company they need to analyze stock market data in real time to make those super fast trading decisions. Yeah, Kinesis can actually be the engine that ingests that super high velocity data and feeds it right to your algorithms.

Chris 1:29
That's intense. What about something maybe a little less Wall Street, okay, sure.

Kelly 1:34
Let's say you are working on a gaming platform. So with Kinesis, you can capture every single player action every game event. Wow. This allows the developers to then monitor the player behavior, you know, in real time, identify any potential issues, or even balance gameplay as it's happening live. Oh, wow. So

Chris 1:53
we're talking about being able to react instantly to what's happening in your application. Yeah, exactly. I like it. Okay, so we get the what and the why of Kinesis. Now let's get a little more technical. What are some of the core features and benefits that make Kinesis So, I guess, attractive to cloud engineers? Well, I

Kelly 2:12
think first and foremost, you have to talk about scalability. Kinesis is built to handle those truly massive data volumes, and it can automatically scale up or down based on demand, so you don't have to worry about over provisioning your resources or your data pipeline like crumbling under pressure, right?

Chris 2:29
Because who can predict how much data they're going to get at any given time, exactly. So scalability is taken care of. What about reliability? How does Kinesis protect our valuable data?

Kelly 2:39
Well, reliability is kind of baked right into the DNA of Kinesis. It replicates the data across multiple availability zones, which means that even if one zone goes down, you know, which happens, your data is safe and sound. Oh, that's good. Peace of mind, exactly. Plus the service is designed with fault tolerance in mind, so it can kind of gracefully handle those hiccups, those little errors, and just keep chugging along. I

Chris 3:03
like it. So we've got scalability, reliability. What else? Well,

Kelly 3:08
easy integration is another big one, Kinesis. It plays well with other AWS services. Oh, right, you know, things like Lambda, S3, Redshift. Oh, of course, yeah. And even machine learning services, if

Chris 3:19
you want to get you know, fans, yeah. So it's kind of like the central hub in our data pipeline. Yeah, exactly, bringing everything together precisely. Now, while Kinesis sounds great, are there any, I guess, limitations or things we should be aware of before jumping in head first,

Kelly 3:37
right? So, I mean, no, service is perfect, right? One thing to be aware of is the cost Kinesis. It can become expensive if it's not configured properly, or if your data volumes aren't managed. Well,

Chris 3:48
yeah, cost is always a factor. Oh, absolutely. Like choosing the right size pipes for your house

Kelly 3:53
Exactly. If they're too small, you get bottlenecks too large, you're paying for capacity you just don't need,

Chris 3:59
Okay, any other gotchas we should be aware of? Well,

Kelly 4:03
another thing is the complexity setting up and managing Kinesis applications can be a bit complex. It requires a good understanding of data streaming, architectural best practices and those little nuances of the

Chris 4:17
service itself. So maybe not a set it and forget it kind of service, probably not for

Kelly 4:21
most use cases, but the payoff in terms of that performance and scalability, it can be really worth the effort, yeah,

Chris 4:27
especially if you're serious about real time data processing, absolutely. So we've talked about what Kinesis is, why it's important. Now let's see how it fits into that broader AWS ecosystem, right? We've mentioned it integrates with other services. But how does that actually work?

Kelly 4:43
Sure. So let's imagine you have a network of IoT sensors. Great. They're collecting temperature readings from a factory floor. Okay, yeah, those sensors are constantly sending data to Kinesis data streams, which is kind of the entry point for our data pipeline. From there you. Can actually use AWS Lambda to process that incoming data in real time. Oh, maybe performing some calculations or triggering alerts, if you know the temperature goes above a certain level.

Chris 5:09
So Lambda is kind of acting like our real time data processing engine here, exactly,

Kelly 5:13
responding to each data point as it comes in from Kinesis. I

Chris 5:18
got it. What happens to that data after Lambda has done its thing. Well,

Kelly 5:22
it really depends on what you need it for. You could use Kinesis Firehose to continuously pump that process data into S3 for long term storage, for analysis, or you could feed it into Redshift for those more complex data warehousing tasks. Got

Chris 5:38
it so we have data streams for the real time ingestion, Lambda is doing the processing, Firoz is taking care of the delivery, exactly. It all works together pretty seamlessly, like a well oiled machine. I like it. And this is just one example, right? Just one you can mix and match all sorts of services. Oh, yeah, create custom data pipelines for your specific needs. That's the beauty of AWS, right? Exactly. Now, I'm sure our listeners are starting to understand how powerful Kinesis can be, but let's be honest, a lot of them are probably thinking about those AWS certification exams too.

Kelly 6:08
Of course, that's a big part of being a cloud engineer.

Chris 6:12
Absolutely and understanding Kinesis is, well, pretty much essential for those exams. It is. So let's get into some of those exam style question, shall we see how this knowledge translates into real world test scenarios? All right, let's do it. Okay. First question, you're tasked with capturing real time click stream data from, let's say, a high traffic e commerce website, okay, and sending it to S3 for storage and analysis. Which Kinesis service would you choose? And why? All

Kelly 6:42
right, so we've got high volume real time data needs to be reliably delivered to S3 sounds like a perfect use case for Kinesis, firehose. Okay,

Chris 6:50
firehose. Why not data streams? They both deal with streaming data, right? They

Kelly 6:54
do. But data streams is really better for real time processing. It gives you that full control over the stream. You can tap into it at any point and perform, you know, custom processing, right? But fire hose is optimized for efficiently delivering data to a destination like S3 Ah,

Chris 7:10
okay, so fire hose is our delivery guy, exactly. Data streams is more for when we want to manipulate and process the data in real time. Makes sense. Okay. Okay. Next question, what is a Kinesis shard and why is it important for a performance?

Kelly 7:23
Okay, so a shard, think of it like a lane on a highway. Oh, more lanes, more cars can flow through at the same time, more data, exactly. So more shards mean more capacity for your Kinesis data stream to ingest and process that data. Got

Chris 7:38
it so if we need to handle more data, we just add more shards. Simple, well, not

Kelly 7:43
so fast. Choosing the right number of shards, it's actually crucial for balancing performance and cost. Oh, right, of course, cost, yeah, too few shards, and your stream might become a bottleneck. Too many, and you are paying for capacity that you're not using.

Chris 7:57
So it's a balancing act. It is all right. Let's move on to another key concept, security, okay, how can you ensure only authorized users and services can access your Kinesis streams?

Kelly 8:08
Security is absolutely critical. So the key here is IAM, identity and access

Chris 8:12
management, right? IAM our old friend for managing permissions in AWS, exactly. So how do we use IAM to protect our Kinesis streams. With

Kelly 8:21
IAM, you can create very specific policies that define who can do what with your streams. So you can say which users or services are allowed to read data, write data, or even manage the entire stream. So

Chris 8:34
for example, we could have one policy that only allows a certain Lambda function to write data to the stream, but then have another policy that lets our data analysts read the data for analysis

Kelly 8:45
Exactly. It's all about creating those boundaries, you know, keep your data safe, love it. Well,

Chris 8:50
this has been incredibly helpful. I think our listeners are starting to get a real feel for Kinesis, yeah, how to use it and what to expect on those AWS exams,

Kelly 8:59
absolutely.

Chris 9:00
So let's take a quick break. All right, we'll be back soon to delve even deeper into the world of Kinesis. Sounds good. All right, welcome back to our Kinesis Deep Dive. Yeah, let's jump right back in. Perfect. Here's a scenario you might see on the solutions architect exam. Okay, you have a Kinesis data stream, and it's processing real time sensor data from a fleet of delivery trucks, suddenly, the volume of data doubles because of a huge unexpected surge in deliveries. How would you handle that increased load without disrupting the stream?

Kelly 9:31
Okay, so this is where understanding shard management really comes in handy. Right shards, each shard has a fixed capacity for you know, how much data it can ingest and process per second, okay? When that data volume increases like that, you need to scale your stream horizontally by adding more shards.

Chris 9:48
So we can't just keep adding more and more data to the same shards,

Kelly 9:52
right? Exactly? Each shard has a limit. Got it. If you exceed that, you could run into throttling or performance issues. So

Chris 9:58
resharding is the solution. Yeah, to accommodate that extra load. But how do we actually reshard a stream? Do we need to, like, stop it completely to add more shards? Thankfully,

Kelly 10:07
no. You can reshard a stream dynamically without actually interrupting the data flow. Okay, you can either split an existing shard into two or merge two shards into one, depending on your needs. So we

Chris 10:18
can adjust on the fly, exactly flexible, right? Very cool. Okay, let's shift gears a bit. Sure. This is a question that combines Kinesis with another AWS service. You are using Kinesis data streams to ingest social media posts, let's say, related to your company. Okay, you need to analyze the sentiment of those posts, you know, figure out how people are reacting to your brand, and you need to do it all in real time. What service could you integrate with Kinesis to make this happen? Ooh, sentiment

Kelly 10:49
analysis. That's a good one.

Chris 10:51
Yeah, any ideas? Well, we've

Kelly 10:52
talked about Amazon comprehend before,

Chris 10:53
right? The service that can understand the emotional tone of text exactly.

Kelly 10:58
You could use comprehend to analyze the sentiment of those social media posts right as they flow through your Kinesis stream. Oh, that's

Chris 11:04
a great idea. So Kinesis captures the posts, then we use a Lambda function to send that data over to comprehend Yeah, you got it, and boom, real time sentiment analysis. It's

Kelly 11:12
a really powerful combination.

Chris 11:14
Very cool. All right, here's a question that I think trips a lot of people up, okay, what is the key difference between Kinesis data streams and Kinesis firehose,

Kelly 11:23
ah, yeah, they both handle streaming data, right? So what's the deal? It's an important distinction, though. Data streams, it's all about real time processing. Oh, it gives you full control over the stream. You can tap into it at any point perform custom processing. Fire Hose, on the other hand, is more about getting that data to its final destination quickly. Okay, it's not really focused on real time processing. Got it.

Chris 11:47
So if we just need to, like, capture data and store it somewhere, Firehose is the way to go, exactly. But if we need to do something with that data as it's coming in, then we'd use data streams. Makes sense, right? Absolutely. All right. Let's get a little more technical. Okay, hit me. How can you control access to your Kinesis data streams? Make sure only the right people and services can read and write data. Okay,

Kelly 12:08
so this is where we lean on our good friend IAM. Again, I am

Chris 12:13
identity and access management. Yes, exactly

Kelly 12:15
you use IAM to define those very specific permissions for your Kinesis streams.

Chris 12:23
So we're talking about creating IAM policies that say exactly who can do what,

Kelly 12:27
precisely you can allow one Lambda function to write data but not read it. Or you can give your data scientists read only access for analysis while your developers have full read write access. So it's

Chris 12:40
very granular control. It is okay. Let's walk through a real world scenario. All right. I like these. You are building a system to process financial transactions. Okay? In real time, you need to make sure no transactions are lost and that the system can handle a massive volume of transactions. What Kinesis features or best practices would you implement to meet those requirements? Okay,

Kelly 13:04
so this is mission critical, right? Financial transactions Absolutely you can't afford any data loss. First and foremost, you gotta leverage the durability of Kinesis data streams,

Chris 13:13
right? Meaning the data is replicated across multiple availability zones Exactly.

Kelly 13:17
So even if one zone goes down, your data is still safe in another

Chris 13:22
makes sense. But how do we handle that high volume of transactions? Right? That's

Kelly 13:26
where sharding comes in. Again.

Chris 13:27
Okay, shards our friends gotta

Kelly 13:28
choose the right number of shards to handle that load.

Chris 13:31
So if we are expecting a huge number of transactions, we need to start with more shards.

Kelly 13:36
It's always better to have a few extra than to run into those performance issues. Okay,

Chris 13:40
so durability and scalability are covered. Anything else we should be thinking

Kelly 13:44
about monitoring is absolutely essential.

Chris 13:47
Ah, right, of course. Monitoring, you

Kelly 13:50
need to keep a close eye on those Kinesis streams make sure they're healthy and performing well.

Chris 13:55
What tools do we use for monitoring?

Kelly 13:57
CloudWatch is your best friend here?

Chris 13:59
Okay? CloudWatch or trustee monitoring service Exactly. It

Kelly 14:03
lets you track all sorts of metrics like data volume, shard utilization, processing latency, so we can see if any bottlenecks are forming, yeah. And you can set up alarms in CloudWatch to notify you if anything goes wrong. So

Chris 14:15
we can be proactive, not reactive. Exactly. You'll get an alert

Kelly 14:19
if there's a spike in transactions or a drop in throughput, and you can take care of it right away.

Chris 14:24
Love it. Well, this has been a fantastic deep dive into Kinesis. Yeah, we covered a lot. I think our listeners are well equipped to tackle those AWS exams. I hope so, and build some awesome, real world solutions Absolutely.

Kelly 14:36
All right, welcome

Chris 14:37
back to our Kinesis Deep Dive. You know, we've talked a lot about the features and capabilities of Kinesis, how it integrates with other services, even tackled some tricky exam questions, yeah, but there's one crucial aspect we haven't really dug into yet, and that's monitoring,

Kelly 14:51
right? Monitoring super important, especially when you're dealing with real time data, absolutely.

Chris 14:56
So how do we actually keep an eye. On our Kinesis streams make sure everything is running smoothly. What tools do we need?

Kelly 15:05
Well, the good news is, AWS gives us a whole bunch of tools for monitoring our Kinesis infrastructure. Okay, one of the, I guess main tools is Amazon, CloudWatch. Cloud

Chris 15:14
watch, right? Our go to for all things monitoring in AWS, exactly. But how do we use it, specifically for Kinesis streams. Well,

Kelly 15:21
CloudWatch lets you collect and track a ton of metrics from your streams. Okay, like what kind of metrics, things like data volume, how much data is flowing through, shard utilization, processing, latency, even error rates.

Chris 15:34
So it's like having a dashboard for our Kinesis streams. Yeah, you got it see how everything is performing at a glass

Kelly 15:40
Exactly. But it gets even better. You can also set up alarms in CloudWatch. Oh, right, alarms. So let's say you want to know if the data volume suddenly spikes, or if the processing time starts to slow down. You can configure CloudWatch to send you an alert, maybe an email or a text message,

Chris 15:58
okay, so we can catch those issues before they become big problems. Exactly, be

Kelly 16:03
proactive, not reactive.

Chris 16:05
I like it now. We've talked about Kinesis data analytics before. It's that service that lets us run real time analytics on streaming data, right? But can it also help us with monitoring?

Kelly 16:16
It can, while its main purpose is analytics, it can also give us some really valuable insights into the data that's flowing through our streams. Okay? It can help us identify patterns, anomalies, potential bottlenecks, so

Chris 16:28
we can use it to spot things like unusual spikes in data, maybe data that doesn't fit our expected format exactly,

Kelly 16:34
or even errors that are happening during the processing.

Chris 16:38
Got it so it's like having an extra set of eyes watching over our streams. You got it now, I know a lot of our listeners are probably studying for those AWS certifications, and one thing that often comes up on the exam is monitoring best practices. Right? Any tips for us? Absolutely.

Kelly 16:53
One of the key best practices is to establish what we call baseline metrics for your

Chris 17:00
streams baseline metrics. What does that mean? It

Kelly 17:02
basically means understanding how your streams behave under normal conditions, okay, what's the usual data volume you see? What's the typical processing time? Yeah, once you know that, you can easily spot anything that deviates from that norm, right?

Chris 17:16
So if something suddenly spikes or drops dramatically, we know something might be wrong,

Kelly 17:21
exactly. Another important best practice is to set up effective alerting.

Chris 17:26
Oh, right, we talked about CloudWatch alarms. Yeah,

Kelly 17:29
but you got to be smart about it. Don't set up alarms for every little thing,

Chris 17:33
okay, so only alarm on the metrics that really matter,

Kelly 17:35
right? You don't want to get bombarded with alerts for minor fluctuations,

Chris 17:40
makes sense. So we've got CloudWatch Kinesis, data analytics, some best practices, anything else we can use to really get a deep understanding of our Kinesis applications.

Kelly 17:50
Well, if you really want to dive deep and troubleshoot those tricky issues, AWS X-Ray is a fantastic tool.

Chris 17:56
X-Ray the service that lets us trace requests through our application. Exactly with

Kelly 18:01
X-Ray, you can see how requests are flowing through your Kinesis infrastructure, identify any bottlenecks, Spot latency issues.

Chris 18:07
So it's like getting an x ray view of our Kinesis applications precisely.

Kelly 18:11
You can see exactly where those performance problems are hiding. Very

Chris 18:14
cool. Well, this has been an amazing deep dive into Kinesis. We've learned so much about the service, how to use it, how to monitor it, and how to ace those AWS exams. Absolutely, it's

Kelly 18:26
a powerful service with a lot to offer. It is so here's a final thought

Chris 18:29
for our listeners. We've talked about how Kinesis lets us process huge amounts of data in real time, but what are the ethical implications of that? How do we make sure this technology is used responsibly? That's something for all of us to think about as we continue to build these data driven applications. It's an important question for sure. Thanks for joining us on this deep dive into Amazon, Kinesis. Until next time, keep learning, keep exploring and keep building amazing things in the cloud. Absolutely, it's.