Hi, everyone.
Today I'm going to be talking about transactional versus analytical workloads.
Transactional and analytical workloads are really two different ways to describe data.
I'm sure all of us have heard the term workload before, probably in reference to our own job.
Our manager asks us what our workload looks like.
We either tell them what we've been doing or how much we've been doing, and it's really the same with data.
So like I said, it's two ways that we can describe data.
We have transactional workloads that is more a day-to-day operational information
and analytical workloads, which is geared more towards analysis.
And diving into them, we can really see the different properties of both of them.
So let's start with transactional workloads.
This is also called OLTP, or Online Transaction Processing.
So a few examples of this are a customer making a purchase, or
retrieving your bank balance or even making a hotel reservation.
This is all day to day operational information, right?
So that is all OLTP.
We can start by really diving into one example of this.
Let's look at a customer making a purchase.
Like I said, since this is day-to-day information, it is transactional
and there are a few different properties that really define it as being that.
Firstly, we're focused on one customer, right, on one single entity.
So all transactional data is focused on a single entity, a single piece of data, right?
And then it's also very important for this to be real-time.
So imagine you are going into a store, you are making a purchase, and it's not in real-time.
It's taking 15 minutes just for you to have your transaction processed.
You're probably not going to be a very happy customer,
and the business isn't going to be very happy because they're losing customers or their customers aren't happy.
So it's very important for OLTP databases to support real time processing.
With that too, we also want to make sure that we're supporting concurrency.
So this is the ability for multiple users to access the database at the same time.
So, imagine again you are at a store, you're checking out and there's only one register open.
This again, isn't very efficient, right, and you're probably also not very happy in this situation.
So, it's important to make sure that multiple users are able to access the database,
and we're not waiting around for each person to process their transaction before the next transaction goes through.
So this really looks at some of the key properties of OLTP
and a few of the operations that are supported are called CRUD operations.
So we have Create, which is creating a record.
If you're a first time customer at a restaurant, then they are creating a record of you.
We also have Read, which is reading information from the database and returning it back to you.
So let's say you want to know what your bank balance is, that's being read from the database and returned back to you.
And then we also have Update.
If you moved addresses, you want to update that information in any database that your information is stored in, right?
So OLTP databases, support updates.
And also Delete.
Let's say you're getting a lot of information from one store that you shop at
and you don't really want to be getting their newsletters every day or every week.
So you delete your information, OLTP databases support that operation.
And now switching gears, let's look at OLAP data stores, or analytical data stores.
So like I said, another word for this is OLAP, Online Analytics Processing.
And this looks more at data as an aggregate.
Whereas before we were focused on a single entity making a single purchase,
now we are focused on data as an aggregate.
And we focus on data as an aggregate
to really understand different trends that are happening in an organization
and better make business decisions.
So, whereas the OLTP database is focused on real-time processing,
this could be a little bit slower because we're looking at vast amounts of information
and making smarter business decisions from that.
But there is a close tie between OLTP databases and OLAP data stores.
We can take the information that is in our OLTP database
and bring that down to our OLAP data store.
We have multiple OLTP databases in one company normally.
So we might have a database for sales,
we might have one for inventory, and another one for shipping.
All this information is aggregated together for our OLAP data store.
And this is normally an enterprise data warehouse (EDW).
And like I said, this gives you a holistic view into your organization.
It lets you see all the data that's coming in and make smarter decisions from that.
So why don't we take this from start to finish, right?
We start with our customer.
Our customer goes into a store.
They make a purchase.
That information is stored in an OLTP database.
Then all the information in this OLTP database goes to an OLAP data warehouse,
and from there, a business user accesses it to make data driven decisions for the company,
that ultimately go back to benefit the customer.
So transactional information really describes what is happening in an organization.
It's all this data that is being generated to really describe the processes.
Whereas analytical information is more geared towards analysis.
And both of these work together to drive data driven insights.
Thank you.
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