The data storage method is another important difference between a data lake and a data warehouse. A data lake stores raw information to make it easier to search through or analyze.

Data lakes, along with data warehouses and databases, are central to modern computing. They form building blocks for data transformation.

At the 2nd Annual Semantic Layer Summit, which took place April 26, AtScale founder and CTO Dave Mariani sat down with Bill Inmon, recognized by many as the father of the data warehouse, to discuss ...

There are a few key differences between data warehouses and data lakes. Here’s what K–12 IT leaders should consider when looking at how they might address their schools’ data storage needs. What Are ...

Data lakes are cool, but you don’t have to jump in head-first. It’s easy to start by dipping a toe: Integrating a legacy data warehouse into a data lake leverages the structured systems that ...

In the ongoing debate about where companies ought to store data they want to analyze – in a data warehouses or in data lake — Databricks today unveiled a third way. With SQL Analytics, Databricks is ...

The data lakehouse – it’s not a summer retreat for over-worked database administrators (DBAs) or data scientists, it’s a concept that tries to bridge the gap between the data warehouse and ...

Ignite 2019: Microsoft has revved its Azure SQL Data Warehouse, re-branding it Synapse Analytics, and integrating Apache Spark, Azure Data Lake Storage and Azure Data Factory, with a unified Web ...

Grab's one central data platform brings together the flexibility and reliability of a data lake and the BI capabilities of a data warehouse.

Part 4 of CRN’s Big Data 100 takes a look at the vendors solution providers should know in the data warehouse and data lake systems space.