BigQuery Versus Redshift

Kiyoto Tamura compares Google’s BigQuery versus Amazon’s Redshift for cloud-based warehousing:

Neither service is truly “set and forget” and requires a dedicated engineer to learn the service and maintain it. You can use various tools to automate many aspects of the operation, but someone will have to maintain automation scripts and workflows.

That said, here are things that I’ve heard first-hand from talking to users

The bottom line there is that Redshift is a bit more mature than BigQuery today, but keep an eye on both of them.

Related Posts

Virtualize Data Or Move It?

James Serra contrasts data virtualization with traditional ETL moving data to a warehouse: Data virtualization integrates data from disparate sources, locations and formats, without replicating or moving the data, to create a single “virtual” data layer that delivers unified data services to support multiple applications and users. Data movement is the process of extracting data from source […]

Read More

Avoid Scalar Functions In Computed Columns

Daniel Hutmacher shows why you should not include scalar functions inside computed column definitions: Scalar functions can be a real headache when you’re performance tuning. For one, they don’t parallelize. In fact, if you use a scalar function in a computed column, it will prevent any query that uses that table from going parallel – even if you […]

Read More

Categories

June 2016
MTWTFSS
« May Jul »
 12345
6789101112
13141516171819
20212223242526
27282930