Neo Technology

Maximising Analytics & Machine Learning with Data Lakes

Maximising Analytics & Machine Learning with Data Lakes

Is your organisation struggling to keep up with massive data volumes? Perhaps it's time to embrace the Data Lake!

In today’s digital-first landscape, organisations are facing an ever-growing volume of data. This data can come from a variety of sources, including internal systems, external data sets, and user activity and trends. To capitalise on the valuable insights and competitive advantages that such data can provide, organisations need to have a robust and flexible data infrastructure in place.

One potential solution is the Data Lake. A Data Lake is fundamentally a large storage repository that can accommodate vast volumes of disparate data types. Its key advantage lies in its flexibility – unlike traditional storage solutions, which often require rigid schema specifications for each stored dataset, a Data Lake allows for very granular control over the exact type and format of the stored data. This makes it possible to store different datasets in their raw form, without having to them pre-processed or structured in any special way.

Another key benefit of using a Data Lake is that it allows for sophisticated analysis techniques like machine learning and analytics to be used effectively on large volumes of continuously growing data. By making it easier to retain all available relevant information in one place, organisations can reap the benefits of powerful analytical tools without being constrained by restrictive storage limitations or intensive computational demands. Ultimately, by choosing to

As of last year, global demand for Data Lakes is predicted to grow by 27.4%.

The Origins of Data Lakes

The term ‘Data Lake’ was first coined by Pentaho CTO James Dixon in October 2010. They were originally built using on-site file systems, but these proved difficult to deploy since the only way to increase capacity was adding physical servers. This made it difficult for organisations to upgrade their systems and increase capacity.

However, since the early 2010s, the rise of Cloud-based services has enabled companies to build and manage Data Lakes without having to build costly on-premises infrastructures.

Data Lakes are now a trusted and established form of architecture in the world of data science, advanced analytics, and digital-first business. Many organisations are rapidly re-platforming their Data Lakes, abandoned legacy platforms and remodelling data.

“If you think of a Data Mart as a store of bottled water, cleansed and packaged and structured for easy consumption, the Data Lake is a large body of water in a more natural state. The contents of the Data Lake stream in from a source to fill the lake, and various users of the lake can come to examine, dive in, or take samples.” – James Dixon, CTO of Pentaho

Why do Businesses need Data Lakes?