4 minute read · June 30, 2021
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.
What Is a Data Lake?
A data lake consists of a cost-effective and scalable storage system along with one or more compute engines. It supports a broad range of essential functions from traditional decision support to business analytics to data science.Learn more about data lake solutions and how they can help your business be more productive and efficient.Learn About Data Lakes
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.
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
Discover the Data Lake Engine
A data lake engine is an application or service which queries and/or processes the vast sets of data stored in a data lake. Rather than relying on expensive, complex and proprietary data warehouse environments, a data lake engine supports both data science and business intelligence queries directly on the data lake.
For any business using data warehouses and data lakes, or for businesses simply wanting to get more capability from their data lake, the data lake engine is a game changer.
Additional Resources
BLOG
Ingesting Data Into Apache Iceberg Tables with Dremio: A Unified Path to Iceberg
By unifying data from diverse sources, simplifying data operations, and providing powerful tools for data management, Dremio stands out as a comprehensive solution for modern data needs. Whether you are a data engineer, business analyst, or data scientist, harnessing the combined power of Dremio and Apache Iceberg will undoubtedly be a valuable asset in your data management toolkit.
Dremio Blog: Product Insights,
Learn More ->
BLOG
The Why and How of Using Apache Iceberg on Databricks
Dremio Blog: Open Data Insights,
Learn More ->
BLOG
Table-Driven Access Policies Using Subqueries
This blog helps you learn about table-driven access policies in Dremio Cloud and Dremio Software v24.1+.
Dremio Blog: Product Insights,
Learn More ->