Data comes in fast, and lots of it. An estimate from 2018 is that 2.5 quintillion bytes of data are created each day. And since that was over a year ago, that number is already outdated. Regardless, that’s a lot of data. Where does it all go? Much of it is stored in various, raw formats in large storage repositories, such as AWS S3, waiting to be analyzed. These repositories of data are known as data lakes, vast pools of semi-structured data, ripe for fishing through for insights.
MongoDB Atlas Data Lake
Enter the MongoDB Atlas Data Lake. This product, currently a Beta release, allows for the exploration of semi-structured data residing in an AWS S3 bucket to be queried with the MongoDB Query Language (MQL). This opens up the possibility to explore the complex data in your data lake in place without having to move it to a database. By being able to use MQL to work with your data lake, it prevents that data lake from turning into a stale and stagnant data swamp.
One of the easiest and fastest ways to start exploring and working with your data is with MongoDB Compass. With the 1.19.6 release of MongoDB Compass, there is support for Atlas Data Lake. By connecting MongoDB Compass to your Atlas Data Lake, you are able to quickly gain insights into your data like overall structure and values. By using MQL with MongoDB Compass you can run queries on your data and even build complex aggregations to unearth the hidden secrets of your data lake.
Check out this quick video to see how to connect MongoDB Compass with Atlas Data Lake.
Sign up for MongoDB Atlas. You can get started with the Atlas Data Lake today, and we have $200 in credits to help you on your way.