25 Mar What is a data lake and why it is important
First off, let’s focus on a definition: a data lake may be a storage repository that holds a huge amount of data in its native format, including structured, semi-structured, and unstructured data.
Why this is important: there is significant value in data, whether is structured or unstructured and if the right steps are taken beforehand and the data lake is effectively designed for storage, security and governance, your organization can definitely have a shot at extracting the most value from its big data assets.
Through its ability to store huge amounts all sorts of information; data lakes are often used for flexible analytics that support smart move making. Data from the varied sources are often used for several different applications and analyses, including real-time analytics and machine learning. The aim is to be as agile as possible in achieving optimal results and responding to new business opportunities.
Data lakes may have a slim margin for error, but that only reflects their relevance. In today’s world, a knowledge lake is that the foundation of data management — and, when built successfully, it can empower all end-users, even nontechnical ones, to use data and unlock its value.
Data lakes are no longer used as cold data stores, but rather sources for ad-hoc analytics of near real-time data combined with hot data in data warehouses and the truly are business drivers for the most advanced initiatives. Data lakes have evolved considerably to enable enterprises to gain real-time insights using business intelligence dashboards or build artificial intelligence capabilities.
But what really are data lakes? ‘Data Lake’ is usually synonym with large volume of data organized in non-hierarchical, usually stored on Hadoop HDFS or Amazon S3, Google GCP products (e.g. Google Big Query) or Microsoft Azure. Data sets could be discoverable by tags, instead of file systems hierarchy.
Given that this is not clearly a trivial undertaking… why build a data lake then?
Traditional data warehousing is slow and doesn’t necessarily respond to today-s challenges. They are not optimized for the variety and sheer volume of big data. Data lakes, on the other hand, can store data from diverse sources in their native format. They are scalable, allow for data streaming and fit perfectly todays unpredictable volume demands due to incremental sources or new streams of data.
What is really crucial in this case is creating a data catalog combined with governance is crucial in understanding the data in your data lake and ensuring its trustworthiness. This is designed to provide a single source of truth about the contents of the data lake and helps you to understand the sources as well as the transformations of the data.This will also help in directing any privacy related activity (e.g. GDPR) or any additional data transformation activity required by more strict data governance requirements.
There are plenty of benefit with leveraging a data lake but that doesn’t necessarily have to fit your specific need. Having data in one location, during a native format, and available for Hadoop family of tools could be useful… sometimes. Sometimes not. Ask yourself why you need the data lake.