BYTE the Cloud

Chris 0:00
All right, let's dive deep into AWS Data Pipeline today. Yeah, it's

Kelly 0:03
a service you probably see, but maybe don't know super well, definitely, more than just moving data around Exactly. And you know, pretty important for mid level cloud engineers for

Chris 0:12
sure, and maybe even that Solutions Architect Associate exam could be, yeah. So, hey, hey, Kai, is data pipeline like, really? It's

Kelly 0:19
like a an automated data what's the word conductor? Okay, moving data between services reliably, even on premises useful,

Chris 0:27
but lots of AWS services move data. Why this one?

Kelly 0:32
It's all about when it moves and how scheduled reliable. And that's data pipeline, batch processing,

Chris 0:38
you know. So nightly backups to S3 that sort of thing,

Kelly 0:41
exactly. Or transfers from your EC2s for analysis. Or you gotta get those ETL jobs, yep, pulling from everywhere into your data warehouse.

Chris 0:48
All automated, not real time stuff then, like Kinesis, no. Data pipeline

Kelly 0:53
is all about that preset schedule, no manual work each time makes

Chris 0:56
sense. So say I'm moving data to Redshift nightly for analysis, okay, yeah, how would data pipeline help there specifically? So

Kelly 1:04
picture this a visual designer. You literally

Chris 1:09
drag and drop. No way like building the pipeline visually. Yep.

Kelly 1:12
Pick your sources, your transformations, where it lands, the schedule, bam. So 1am every night, it just does it exactly. And for common stuff, AWS has pre built templates even easier, right? Saves so much time.

Chris 1:24
But what if it fails? One of those transfers, I gotta fix it manually.

Kelly 1:29
This is where it's great, built in error handling. Retries, no way it'll try again or tell you if something's really wrong. No more headaches. Peace of mind is priceless. Ah for sure. But yeah, no, service is perfect. Got to know the limits, right? What are data play planes, downsides? Not for real time. Like we said, Kinesis is your friend there, gotcha. And even with the designer, some learning curve for complex stuff. So Kinesis

Chris 1:53
for real time and maybe deeper dive needed for tougher pipelines, makes sense. But how does it fit with the rest of AWS? Does it play nice?

Kelly 1:59
Oh, yeah. Big strength is that integration, S3, EC, two, you name it, even on premises, yep. And secure. Two uses IAM, roles, policies for data transfer. So

Chris 2:10
secure, scheduled, reliable, visually, designed, works with everything

Kelly 2:16
you got it. And honestly, that's just the start, I

Chris 2:19
bet, ready for those exam questions. Now,

Kelly 2:21
let's do it. See how the service gets tested. Hit me with them. All right. Picture this exam scenario. You got to move data, okay, from on premises database to S3 bucket every night at 2am What do you use based

Chris 2:35
on what we've talked about? Data? Pipeline sounds right, schedule, transfer and all you

Kelly 2:38
got it perfect fit. Define that pipeline, source to destination makes sense. Set the schedule. Boom, automatic. No manual. Work nice.

Chris 2:47
But exams love to be tricky. What if they throw in Snowball?

Kelly 2:50
Ah, good point. Similar words, but totally different use, right? Someone skimming might mix 'em up. Exactly? Snowball is huge. EE, data offline, like petabytes, physical shipping. Whoa, yeah, very different from data pipeline, right? Data pipeline is regular, automated over the network, smaller scale,

Chris 3:06
so Snowball's one time. Data pipeline is the recurring thing.

Kelly 3:09
Nailed it. Now another one they love. Difference between data pipeline and Kinesis. Ah,

Chris 3:13
this is where batch versus real time comes in again, right? Bingo.

Kelly 3:17
Data pipeline. It's batches, intervals. Kinesis is constant stream. Got it. One's like a freight train, the other's a fire hose. Perfect analogy. So exam question, theta pipeline equals batch processing. Remember, that

Chris 3:30
will do but what about complex pipelines, multiple steps? What if one fails? Great

Kelly 3:36
question. This is where error handling shines. Retries,

Speaker 1 3:39
remember? Oh, yeah, you can configure it. Try again a few times exactly, or custom logics, send

Kelly 3:44
a notification, whatever you need. So not

Chris 3:46
just moving data. It's doing it smartly.

Kelly 3:48
You got it. But let's go deeper. What if they ask about the pipeline itself? Okay?

Chris 3:52
Like, what's inside a data pipeline? Pipeline?

Kelly 3:55
Think of it this way. The core is the pipeline definition, okay, it's a JSON document. Outlines the whole thing, source, destination, schedule, all of it. So it's

Chris 4:03
like the instruction manual for the data exactly,

Kelly 4:05
and within that you have activities, more jargon, no worries. Think of it as the actions. What happens? Copy activity moves data. Squack activity runs queries, shell command activity for custom scripts. So

Chris 4:20
each activity is like one task in the bigger process, precisely.

Kelly 4:24
But to do those tasks, you need resources, like, what kind of resources? Think infrastructure, easy to instance, to run a script, S3, bucket for storage, that sort of thing. So

Chris 4:35
if the definition is the plan activities and the jobs, resources are the tools you got

Kelly 4:39
it and to make sure it all runs in order, we have preconditions. Okay, I think

Chris 4:44
I see where this is going. They define dependencies, like this must happen before that. So one activity might rely on another finishing

Kelly 4:51
first exactly, and then data nodes, those represent the data itself, where it comes from, goes to.

Chris 4:56
So preconditions for order, beta nodes, for tracking the data's journey. You're. Getting

Kelly 5:00
it. Mastering these is key for the exam. They'll make you analyze stuff. This

Chris 5:03
is making more sense now. But what about like, data pipeline, working with other AWS services? Oh, absolutely. They

Kelly 5:09
love those scenarios, combining services. Okay, give me an example, a complex All right, imagine data from three places on premises, database, Kinesis, stream, Andy S3, files. Oh, wow. That's a lot to handle, but data pipeline can do it. First, copy activity from the database to S3 centralize it.

Chris 5:28
Okay, so S3 becomes like the staging ground,

Kelly 5:31
exactly. And for the Kinesis stream, we use Kinesis data firehose. That's the one

Chris 5:34
that's good with streaming data, right? Yep. It buffers,

Kelly 5:36
batches it up, delivers it nicely to S3 so

Chris 5:40
all our data different sources, it's all ending up in S3 ready to go precisely.

Kelly 5:44
Then we can use M activity, spin up an EMR cluster for processing EMR, that's the big data stuff you got it. It can handle S3 data, nd, the Kinesis stream, using Spark or whatever you need.

Chris 5:55
So data pipelines, like the manager telling each service what to do exactly. It's orchestration, and this is just one example. There's tons of ways to combine it. Oh

Kelly 6:04
yeah, Lambda for serverless, Glue for ETL, even DMS for database migrations. So

Chris 6:08
data pipeline is really versatile, central to all this, exactly,

Kelly 6:11
and knowing these integrations, that's key. Ey for the exam. Gotcha, they'll

Chris 6:16
give you a goal. You got to pick the right services.

Kelly 6:18
You got it now, before we do more scenarios, any common mistakes with data pipeline? Oh, good point. Things People mess up. Number one, IAM, roles and permissions. Gotta set those Right,

Chris 6:28
right. Data pipeline needs access to other services to do its job

Kelly 6:30
Exactly. Otherwise, things break. No one's happy. So credentials are crucial. What else testing gotta test those pipelines thoroughly. Don't just assume they

Chris 6:39
work. Yeah, like with any code, testing catches errors early.

Kelly 6:43
Data pipeline has a preview feature, test individual activities before launching the whole thing smart

Chris 6:47
so build it, test it, then run it, and don't

Kelly 6:51
forget monitoring. Once it's live, keep an eye on it,

Chris 6:53
right? Use CloudWatch. Set up alarms, be proactive.

Kelly 6:57
You got it, security, testing, monitoring, those are the keys to success. Yeah,

Chris 7:01
I'm feeling way more confident about data pipeline entity exam. Now, awesome. Now,

Kelly 7:05
let's shift gears a bit see some real world use cases. Ooh, yeah, let's

Chris 7:09
see data pipeline in action out there. Okay, real world stuff. How are people actually using data pipeline out there?

Kelly 7:14
One big one is data lakes. You know, those massive data stores, yeah, tons of data from everywhere, exactly. And companies need to get it all into, say, S3 that's usually the base. So data pipeline helps get it all there. It's the ingestion, pro, collecting, transforming, loading, all that.

Chris 7:32
So it's literally the pipeline into the data lake, yep.

Kelly 7:34
And for the huge GE volume, we use Kinesis Data Firehose too. That's

Chris 7:39
the one that handles streaming data

Kelly 7:41
well, right exactly buffers. It batches it sends it off to S3 efficiently.

Chris 7:45
So are data lakes always getting fresh data all prepped

Kelly 7:47
precisely, nd, once it's in S3 data pipeline can trigger other services like what Glue, for example, to catalog the data, make it searchable. So it's

Chris 7:58
not just moving data, it's prepping it for analysis later.

Kelly 8:01
Exactly key part of making a data lake that's useful. Okay, that's cool, but what

Chris 8:05
about, say, moving an old ETL process to the cloud,

Kelly 8:09
like something running on premises. Now, yeah, can data pipeline help with that migration? For sure, we've got the shell command activity. It can run scripts on EC2, so

Chris 8:18
we can basically recreate the old process in the cloud,

Kelly 8:21
kinda lift and shift, then over time, you can modernize it using

Chris 8:24
more cloud native services, I bet. Yep,

Kelly 8:27
Glue, EMR, Lambda, whatever fits data pipeline's the bridge.

Chris 8:31
So we get the benefits of cloud without starting totally from scratch. Precisely.

Kelly 8:35
It's all about that gradual improvement.

Chris 8:38
This is really showing me how versatile data pipeline is

Kelly 8:41
for sure, but I gotta ask any weird ways people are using it, stuff that surprises you? Ooh, I like where this is going. So think real time systems, like fraud detection, analyzing transactions live. But data pipeline isn't real time. We've said that, right, but it can hllp those systems, okay, how so it can pre P the data, think, aggregating historical transactions, cleaning it

Chris 9:05
up, so it's doing the background work, making the real time analysis easier. Exactly.

Kelly 9:08
It stores it in a way that's fast to query for the live system using Kinesis analytics or something. So

Chris 9:15
data pipelines like the sous chef doing the prep, so the main chef can focus on cooking.

Kelly 9:19
Perfect analogy. It's all about using the right tool for the job. This has been awesome. I'm way more comfortable with data pipeline now. Glad to hear it. Remember it's a powerful tool. Be creative with it. And

Chris 9:29
for all our listeners, whether you're studying for the exam or just curious,

Kelly 9:34
dive deeper into data pipeline. It's got so much potential. Great

Chris 9:37
advice, lots of possibilities for managing data better, keep learning,

Kelly 9:41
keep experimenting. Cloud skills are always valuable. Couldn't

Chris 9:45
have said it better myself, and that's a wrap for this deep dive. Thanks for joining us. We'll see you next time bye.