Data Lakes

Data fuels the modern enterprise — today more than ever, businesses compete on their ability to turn big data into essential business insights. Increasingly, enterprises leverage data lakes as the platform used to store data for analytical purposes, combined with various compute engines for processing that data.

Data Lakes vs. Data Warehouses

Traditionally, enterprises have relied on data warehouses to manage data for operational reporting, business intelligence and a variety of essential decision support applications. However, data lakes implemented in a loosely coupled cloud architecture have numerous benefits over tightly coupled data warehouses.

Learn about the key differences between data lakes and data warehouses and discover how new technological approaches can help you modernize your data architecture and realize better performance and flexibility at a fraction of the cost.

Learn More

Cloud Data Lakes

While traditional legacy data lakes have been built on on-premises Hadoop clusters, many enterprises are modernizing their data lakes by migrating them to the cloud as an infrastructure-as-a-service.

A cloud data lake is a cloud-hosted centralized repository that allows you to store all your structured and unstructured data at any scale, typically using an object store. The two most common cloud data lake object stores are:

• Amazon Simple Storage Service (Amazon S3) – an object storage service that offers industry-leading scalability, data availability, security and performance
• Azure Data Lake Storage (ADLS) – massively scalable and secure data lake for high-performance analytics workloads

Learn More

Discover the Data Lake Engine