Hey, what's up, everyone? My name is Luv Aggarwal and I'm
a Data Platform Solution Engineer for IBM.
Data warehouses. Their prevalence across enterprises has grown significantly
over the past 20+ years. But with multiple modern advancements,
the numerous options out there are now much more complex.
So, let's talk about what an enterprise data warehouse, or "EDW", is. So, first and foremost,
there's often confusion between "data lakes" and "data warehouses" and even "data marts".
So, I like to think of a data warehouse as being more purpose-specific than a data lake. So,
while a data lake is a great place to dump all sorts of raw, structured and unstructured data
in a quick way to clean and organize later, a data warehouse, on the other hand, is a large
collection of organized and clean business data, ready to help an organization make decisions.
And a data mart is like a subset of a data warehouse that's more specific to a
particular business domain. So, for example, you could have a finance data mart.
But for today, let's focus on the data warehouse.
So, we'll get rid of data lakes and data marts, and we'll make this a little bit bigger.
But for today, we'll focus on the data warehouse. So, let's get rid of data lakes and data marts,
and make our data warehouse a little bit bigger.
So, the data warehouse serves as the single source of truth for an organization across multiple
knowledge domains. And data in the warehouse comes from multiple different source systems.
And is transformed from raw data to high quality data,
optimized for analytics via various different ETL, or "Extract, Transform and Load" tools.
So, as I mentioned, data that's in our source systems can be in
different types. It could be transactional systems, it can be relational databases,
and they can cover a wide variety of business domains.
So, the data could cover things like customer data from our CRMs. We could have sales data.
We could have data from our ERP systems. We could even have supply chain data.
And the list goes on and on. Right.
So, once data has been cleaned, transformed and
loaded into our data warehouse, it's now ready for us to expose to our users,
who can then start to take it and do analytics and machine learning on these data sets.
So, who are our users? Our users can be folks like business analysts. We can have data
scientists. We could even have data engineers. And these folks can now start leveraging these data
sets, either using the built-in analytics tools in the data warehouse or using a variety of different
business intelligence or predictive analytics and machine learning platforms.
OK, so now that we know what an enterprise data warehouse is,
let's talk about the different ways in which it can be implemented.
So, three common ways in which a data warehouse can be deployed.
The first way is on-premises. Now, a couple different ways in which an
on-prem data warehouse can be configured, we could have our data warehouse running on
commodity hardware. Now, this could be set up and structured using either MPP, or "Massively
Parallel Processing", architecture where we just add more compute nodes as our workload grows,
or using SMP, or "Symmetric Multi-Processing", architecture where, typically, we have a
tightly coupled, multi-CPU system that shares resources from one common operating system.
Now, the other way is through a purpose-built appliance format.
Now, this is typically an integrated stack of CPU, memory storage software,
all purpose-built and optimized for a data warehouse workload from a single vendor.
So, what are some of the benefits of having an on-prem data warehouse?
Well, first you get to maintain complete control over the entire tech stack, right?
Second, you can leverage your local network speeds and perhaps avoid some bandwidth challenges
typically associated with the cloud. You can also leverage high availability, and we can maintain
strict governance and regulatory compliance, but on the other hand, an on-prem data warehouse does
come with an upfront investment and the need for ongoing support and maintenance.
Now, the other way in which a data warehouse can be deployed
is through a cloud-based data warehouse, where our data warehouse is delivered as
a managed to SaaS offering via the multiple public cloud providers.
So, moving data warehouses to the cloud is the next frontier for a lot of enterprises
and for valid reasons. Some of the benefits include being able to free up resources
to focus on other high value analytics tasks, right, instead of just managing systems.
Another benefit can also be the ability to scale easily. Right,
because we don't have to go out and procure new hardware
and we get to leverage automatic upgrades. Right. Now, on the other hand, oftentimes a cloud-based
data warehouse can take a performance hit due to how it's fine tuned for that specific workload,
and there can be some unanticipated high costs due to how cloud data warehouse is scaled.
OK, the third option is actually a hybrid approach. So, this takes the best of on-prem
and cloud and brings them together. And a lot of enterprises choose to run both their on-prem
and cloud data warehouses in conjunction. And this can be done for a couple of different reasons.
So, one benefit can be that this allows us to explore new use-cases. Right. So as an enterprise,
we may have certain data sources that were born in the cloud. So, it can be
beneficial to start leveraging a cloud data warehouse for analytics against those use-cases
while still maintaining their mission critical workloads on-prem.
Another benefit can be for a disaster recovery and backup scenario.
This is where we would use both our environments in conjunction for DR and backup reasons.
So, if we take a step back, we can see that we've barely started to scratch the surface of
enterprise data warehouses and how they fit into an overall enterprise architecture. But I hope
this video has given us a good idea of how data warehouses fit in and what they're used for. Thank
you. If you have any questions, please drop us a line below. If you want to see more videos like
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of the IBM data solutions we've discussed today, please feel free to check out the link below.