The data lake supports capturing and storing raw data at scale for a low cost with many different types of data. With a data lake, data becomes increasingly available, and early adopters discovered that they could extract insight through new applications built to serve the business. You load data into the lake, and old bottlenecks where data teams spend their time building and maintaining complex ETL pipelines are gone, and waiting weeks for data access requests are skipped. A wide range of technical and non-technical data consumers can access that data for analytic use cases and machine learning models, including business intelligence and reporting.Ī data lake also removes the need to go through a proprietary format that traditional BI tools had done to transform the data. Its the primary destination for growing volumes and varieties of exploratory and operational data next to data warehouse destinations. What’s a Data Lake and Why Do You Need One?Ī data lake is a storage system with an underlying Data Lake File Format and its different Data Lake Table Formats that store vast amounts of unstructured and semi-structured data, stored as-is, without a specific purpose. With Databricks open-sourced the full Delta Lake 2.0, including premium features, and Snowflake announcing an integrated Iceberg Table, the market is hot right now. We will go through when you'd not use a data lake and what alternatives are there. And discuss the analytical capabilities of data lakes and how to make them. This article explains the data lake power and which technologies can build one to avoid creating a Data Swamp with no structure and orphaned files. Didn't know the difference between a Data Lakehouse and a Data Warehouse? Or you just wanted to govern your hundreds to thousands of files and have more database-like features but don't know how? Ever wanted or been asked to build an open-source Data Lake offloading data for analytics? Asked yourself what components and features would that include.
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