Strategic Data Management

Rise of the data lakehouse

Data lakehousing is an innovative approach to data warehousing that’s been gaining a lot of attention lately. But what is it, exactly? How does it compare with more traditional data warehousing? And where is this all headed?

Because 460degrees Experts have worked in the IT industry for what seems like eternity, we’ve watched the entire evolution of data warehousing unfold. So, let’s have a quick glance in that rear-view mirror before we dip our toe in the data lake.

Outgrowing data warehouses

Honestly, the tools that drove traditional data warehousing functioned quite well. They satisfied clients requirements, the production environments were robust, stable, and reliable, and there were few, if any, issues with reporting.

The only real problem was scalability.

Any significant growth meant an increase in overheads and maintenance of servers i.e. patching, added storage and outages, and all the other integrated services required to support data warehousing.

Things like adding storage impacted the overall performance, and patching the OS impacted the installed applications. Change often had to be rolled back, and the overall impact to business and its users was, let’s say, less than ideal.

Enter big data

These issues with storage and computation led to the birth of ‘big data’ – a term that strolled into our lexicon around 2012-2013 and made itself at home.

And it had a lot of great things to offer. But people started comparing big data processing with traditional data warehousing – one of those apples and oranges situations that causes major misunderstandings.

Big Data is best suited for unstructured data, log data and the speedy processing of very big files. Introducing it into traditional warehousing ecosystems created problems with data volume, velocity, variety, integration, scalability, governance, security, analytical capabilities, and skills. Organisations were scrambling to adapt their existing systems, processes, and talent pool in an attempt to harness the potential big data promised.

To handle the processing requirements of big data, companies also began purchasing additional on-premises clusters (computer systems) and deploying them alongside their existing infrastructure. But thanks to increased overheads and operational costs, companies began limiting the processing of data within their big data clusters.

Bleak stuff. But, fortunately, one silver lining came out of all this: cloud computing.

Looking for answers in the cloud

Cloud pioneers like Amazon and Microsoft revolutionized the IT landscape with their subscription-based model and user-friendly approach to cloud computing. Businesses could now leverage the processing power of the cloud without the need for costly server maintenance. They gained access to affordable big data storage and computational capabilities, unlocking new possibilities for data-driven insights.

But despite the benefits, cloud databases still aren’t as economical as storage and have certain processing limitations. For a long time, the conventional approach was to store data in a data lake in the cloud, then load it into a database using a cloud orchestrator. But this added to the overall cost and complexity of data warehousing and processing.

Which is why data lakehouses have fallen into favour. So, what are they?

A warehouse on the lake with a view

A data lakehouse is a flexible and scalable platform where organisations store all their data in its raw form without the need to structure or organise it upfront. You can think of it as a virtual “lake” into which data flows from all sources.

Multiple tools or engines can swim in the data lake, processing data as needed. Think of them as submarines that navigate the deep waters, extracting the most valuable information and revealing insights to support the best possible decision making.

Building a lakehouse that’s sure to last

Data lakehouses are here to stay and clearly offer plenty of advantages. But there are also challenges to keep in mind. With key concerns including data governance, security, and compliance, organisations must have strong practices in place to ensure optimal data privacy, data quality, access controls, and data encryption.

That’s why it’s smart to tap into the knowledge and wisdom of a team of Experts who can help your organisation navigate challenges and mitigate risks. If you’d like to know more about leveraging the power of data lakehouses in your business, our Strategic Data Management Experts can help you out.