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Building the Next Phase of the Data Lake


Building the Next Phase of the Data Lake

by Jan 17, 2022Data Quality

Introducing Modern’s Newest Series: Data Lakes Aren’t Dead

Data Lakes are dead. We desperately need data lakes. Gartner declares data lakes are over. Data lakes are surging.

It wasn’t too long ago that everyone was excited to build a new type of centralized data storage—one that would maximize availability. The movement attempted to construct a data reservoir that could right the wrongs of data swamps, but now it’s time to reexamine some assumptions everyone had about data lakes.


Data is too big to move

The original catalyst for building data lakes is the advent of big data. Since data was deemed too big and too expensive to move, data lakes provided a key repository. Now, data virtualization and streaming allow for greater movement between tools and storage systems.

Companies can make data more readily available for instant insights. However, organizations will require a system that can control governance, security, and movements.


Making raw data available will speed up insights

People were sick of how long it was taking to write ETL but cleaning data still took up time even when raw data was available. It still required extensive time in either direction. Replacing ETL with other data wrangling tools doesn’t actually solve the problem.

Data lakes with an underlying search function could offer more efficient insights thanks to a DataOps layer. Instead of relying on ETLs or extensive data wrangling, operationalizing the data pipeline will provide data scientists, companies, and their teams will the opportunity to analyze data more efficiently.


Flexibility is key to upgrading data processes

Data lakes are now more ubiquitous than ever, and companies are looking for the next wave of evolution. Instead of complicated, rigid architectures with time-consuming upfront planning, modular approaches can reinvigorate the use of data lakes. With the right data operating system, organizations can build something with discipline and governance but offering real flexibility.

What does flexibility give companies? It allows them to behave more like agile organizations and opens new avenues of data processing. In order to do this effectively, they’ll need a new type of data operating system to manage and connect each component.


Explore Data Lakes with our latest papers

The Modern Data Company knows that data lakes offer potential; we don’t have to throw everything out for a shiny new object. That’s the purpose of our two new papers—to address the situation many companies face as they look for the next part of their data lake story

  • In Data Lakes 101: Core Value Propositions and Usage, Modern looks at the history of data lakes and the premises that drove their creation. Readers explore the value propositions and challenges of data lakes and the four main types of usage.
  • In our second paper,Data Lakes 101: Making the Most of Data Lakes Through Agile Methods, we explore how to make the most of data lakes through agile methods. Readers can look at how users can experiment with different data setups to provide the greatest ROI for the organization.

These papers offer a special look at why data lakes are still in play and how users can build data lakes that deliver what was promised. By using a data operating system like DataOS from The Modern Data Company, companies can build the next evolution of data pipelines that leverage data lakes the way they were always meant to be.

DataOS works with whatever system you already have in place and with your current data storage. There’s no need to move data, no need to offload legacy systems, and users can bring their own components or use ones recommended by DataOS. Most important of all, you’ll turn your data swamp into a true data lake with real potential.

Contact us to see exactly how DataOS brings all legacy systems, including data lakes, into a single source of truth. 

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