5 Key Differences Between Data Warehouses and Data Lakes

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Key Differences Between Data Warehouses and Data Lakes

Are you someone who rules the IT field like a King, having managed to build a magnificent empire for yourself and yet turn a blind eye towards the “nerdy” stuff in the field?

Well, stop doing it right away, because you always stand on the precipice and need to project yourself precocious in order to preserve your place and stand out from your competitors in this field!

It really is crucial to upgrade oneself by learning the intricacies (which always seem trivial) as one might be dethroned by a single wrong move, and nothing will be left but for a quick drop and a sudden fall.

One such pivotal (yet seemingly trivial) piece of knowledge is figuring that there is a thin line of difference between Data Warehouses and Data Lakes which can also be the cloud platforms like Microsoft Azure designed to support big data analytics.

So, get ready to become a know it all in 3…2.1. Go!

Data Warehouses Vs Data Lakes

Both Data Warehouses and Data Lakes are used for storing “Big Data” – a term that is certainly known to the data and analytics practitioners – but are not interchangeable.

To highlight the core difference, Data Warehouses are central repositories of structured, filtered and integrated data from one or more disparate sources whereas Data Lakes are vast pools of raw data.

The following list of properties will help you juxtapose the two even better.

Properties of a Data Warehouse

  • It represents an abstracted picture of the business organized by subject area.
  • Data here is highly processed and compartmentalized.
  • Data is loaded only after the use for it has been defined.
  • Data is stored within the complex structures.

Properties of a Data Lake

  • All data is loaded from source systems. No data is turned away.
  • Data is stored at the leaf level in a raw or unprocessed format.
  • The purpose of the data doesn’t have to be defined.
  • Schema is applied to fulfil the needs of analysis.

1. Data Structure

The greatest and key difference is that Data Warehouses store data which are already processed for specific purposes. One can find only the refined and segregated data here and they are capable of eliminating the unwanted data to keep things organized.

While Data Lakes store (virgin) data which are unprocessed and for this they require comparatively much larger storage capacity. There is also the danger of data lakes becoming data swamps without proper data quality and data governance.

As for the perks of data lakes, raw, unprocessed data is malleable, can be quickly analyzed for any purpose (using Microsoft Azure PaaS), and is ideal for machine learning. When it comes to data warehouses, though they are pricey, their processed data can be easily understood by a larger audience.

2. Explicit Purpose

The purpose of a Data Warehouse is to house the processed data which already got its specified destiny. The ultimate goal of these warehouses are to channelize the data stored towards their designated usage. And these warehouses are very much currently in use.

As for the Data Lakes, the unprocessed data which are sometimes stored with a specific future use in mind and sometimes just to have on hand don’t really stand for an explicit purpose. It can be safely said that the purpose of data lakes are undetermined still.

If this is too thick to learn about, one can always seek the hands of help desk services outsourcing and the cloud platforms like Microsoft Azure to make things easy for you with a swish and a flick!

3. Specified Users

As the data lakes contain clusters of unfiltered data, it will, for the newbies, feel like drowning into the deep cold ocean when trying to navigate and they will find it tedious to resurface like an acorn.

Hence, data lakes are only meant to be used by the experienced hands, such as the Data Scientists. But however, there is a growing momentum behind the data preparation tools that render self-service access to the information stored in data lakes.

And this is where the on-premises, hybrid, multicloud, or at the edge—create secure, future-ready cloud solutions from Microsoft Azure comes in (**drum roll**)

While looking at data warehouses, the highly processed and compartmentalized data demands nothing but the basic knowledge of a topic from the personnel. These assorted data are used in charts, spreadsheets, tables, and more, so that most, if not all, of the employees at a company can assimilate it.

So, data warehouses can be used by anyone and everyone!

4. Accessibility

Accessibility refers to not just the content but the repository or crucible as a whole. Data lake architecture has got no compartments or whatsoever for someone to navigate through the router.

Hence, technically, data lakes are compatible and easy to access as the users are empowered to go beyond the structure of the warehouse to explore data in novel ways. Plus, one can update changes in data from time to time without sweat.

Comparatively, one of the consistent issues found in data warehouses is that it takes a considerable amount of time to make changes in the data. This happens because the complex structures built to house the data cannot be easily altered.

Moreover, businesses are doomed to wait for the data warehouse team to adapt their system to answer them (no matter how trivial the case is) which evidently makes things go haywire.

On the other hand, in data lakes automation and reusability comes handy and a more formal schema can be introduced with the help of software like Microsoft Azure at ease when there’s a need to extend the results to a broader audience.

5. Prompt Insights

When data warehouses are seen to consume time for every milestone, data lakes are known for their tendency to provide swift insights.

Data lakes allow the users to take the driver’s seat to explore and use the data as they see fit. They give them the license to access data before it has been metamorphed, refined and shelved which enables users to get to their results faster than the traditional data warehouse approach.

However, not all companies want to take the driver’s seat, risking it all. This is where the Gen Z cloud computing services of Microsoft Azure is proven to be highly efficient.

And the changes that are cast over the data here, exist primarily as metadata that sits over the data in the lake rather than physically rigid tables that require a developer to change.

Data Warehouse or Data Lake: which is right for me?

Now, that’s a tricky question to answer.

If you already have a well-built data warehouse, we cannot ask you to throw all that work behind your back and start from the scratch, all over again, no.

But what you can do is that, in order to rectify the issues that your traditional warehouse, you may choose to implement a data lake ALONGSIDE your already existing warehouse.

This helps you keep your warehouse alive and breathing while you can fill the lake up with the new data available.

Plus, you can also use it for an archive repository which collects the data that are shaved off from your warehouse, which is proven impressive, especially when your warehouse becomes fit for death.

And, of course, a hybrid approach can also be considered in the future!

P.S If you are just starting down the path of building a centralized data platform, we urge you to consider the pros and cons of both approaches to go for a customized decision.

In a nutshell,

Though these two terms sound just the same, they differ heavily when their format, purpose, users, flexibility and insights are considered.

It is crucial for a company to pick the right one to suit their needs and demands as saving the “Big Data” cannot be compromised, at any point.

A well-informed businessman knows the importance of making well-informed decisions.

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