“They’ve got their data warehouse and they’ve got their data lake, and they’re moving data between them every day, at quite high frequency. And the feedback we’re getting on this is, it’s very ...

Unlike the old data warehouse, the data lake holds “raw” data in its native format, including structured, semistructured, and unstructured data. The data structure and requirements are not ...

They eliminate data silos. At a young company, quickly sharing data and performing a variety of cross-sectional analyses can supply insights and new, unexpected paths forward.

This makes data lakes fit for more exotic and “bulk” data types that we generally do not find in data warehouses, such as social media feeds, clickstreams, server logs, and sensor data. A data lake ...

According to a report released today by Research and Markets, the data lake market is projected to drive nearly $9 billion in revenue by 2021, up significantly from the $2.5 billion in spending ...

The challenges Amazon has faced with big data are similar to the challenges many other companies face: data silos, difficulty analyzing diverse datasets, data controllership, data security and ...

It is combining the concept of the data lake with edge computing into what it calls “interconnected micro data lakes,” or data pools. “Data pools” are micro-data lakes that function like a ...

This data, it’s worth noting, could sit in BigQuery or live on AWS S3 and Azure Data Lake Storage Gen2, too.Through BigLake, developers will get access to one uniform storage engine and the ...

What spurred Google to go for a data lake solution when its Big Query data warehouse has had a successful run for 11 years? The need to break data silos and put in place seamless data management ...