A data lake could be the biggest game changer since the emergence of the database forty years ago. Data lakes have the potential to revolutionize the way organizations manage their data. Rather than pitching one against the other, data lakes and warehouses work in tandem to provide a well rounded data strategy for organizations. In this blog post, we’ll discuss why a data lake is important for your business and compare data lake vs data warehouse.
What is a data lake?
A data lake is a centralized repository designed to store, process, and secure large amounts of structured, semistructured, and unstructured data. Data lakes are designed to hold several different kinds of data, so they’re not only used to process transactional data. They’re designed to hold data from all channels, including social media, web, IoT, and more.
How do data lakes help ?
With the ubiquity of data and the deluge of data that is constantly coming in, it is not surprising that businesses are adopting the data lake approach to manage data. Here’s how data lakes help:
- Simplify data management: Store any type of data from all sources with templates for better data discovery and security
- High processing capability: Handle the massive volumes of data and the diverse sources of data that are coming your way
- Reduce TCO: Go from managed services to serverless analytical platform, leveraging built-in connectors for the marketing platform
- Facilitate AI and ML functions: Moving away from model-centric AI-ML to data-centric AI-ML, leverage your continuous stream of data for better AI-ML functions by setting up ML Ops feedback loops to improve accuracy
- Accelerate analytics: Improve analytics and churn better reports faster with better quality control over the data at scale while protecting sensitive and confidential data
How are data lakes different from data warehouses
As your organization grows, the volume of data will increase exponentially. In the past, the solution was to create a data warehouse to store all of this data. Here’s a data lake vs data warehouse comparison
- The data warehouse was designed to provide a single view of all data. A data lake is designed to simply store all of your data. It is not designed to provide a single view of all the data in the lake.
- Creating a data warehouse can be a challenge, and more expensive to implement and maintain. A data lake is a more affordable solution that scales as your data grows. It is also easier to implement and maintain.
- The type of data permitted to a data warehouse also differs from a data lake. Data lakes permit all kinds of data, some of which is not supported by data warehouses.
- The kind of end user also differs between a data warehouse and a data lake. Business analysts can use data warehouses easily for pre-integrated reporting and business intelligence while data scientists, data engineers, or sophisticated business users are required to process, analyze and extract insights from massive volumes of data in the data lake.