Lack of consistency: Companies may often find it difficult to keep their data lake and data warehouse architecture consistent. It is not just a costly affair, but teams also need to employ ...

Data governance: While the data in the data lake tend to be mostly in different file-based formats, a data warehouse is mostly in database format, and it adds to the complexity in terms of data ...

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 ...

For years, TDWI research has tracked the evolution of data warehouse architectures as well as the emergence of the data lake. The two have recently converged to form a new and richer data architecture ...

We have established that data lakehouse is a product of data warehouse and data lake capabilities. It enables efficient and highly flexible data ingestion. Let’s take a deeper look at how they ...

That’s what we call the next generation data lake architecture ... the need to move it to a data warehouse is diminished. “We didn’t have the flexibility to scale compute and storage ...

“Lakehouses redeem the failures of some data lakes. That’s how we got here. People couldn’t get value from the lake,” says Adam Ronthal, vice president and analyst at Gartner.

Excitement over DuckLake, but momentum is with Iceberg as players at AWS, Snowflake weigh in It's been a year since Databricks bought Tabular for $1 billion, livening up the sleepy world of table ...

A data warehouse is designed to answer specific business questions, whereas a data lake is designed to be a storage repository for all of an organization’s data with no particular purpose. In a data ...

A Deep Dive Into Data. Data warehouse systems have been at the center of many big data initiatives going as far back as the 1980s. Today companies from leading cloud hyperscalers such as Amazon ...