Data Warehouses

Explore various data warehouse options for storing obscene amounts of enterprise data. Data warehouse products include Amazon Redshift, Google BigQuery, etc.
Using Amazon Redshift as your Data Warehouse

Using Amazon Redshift as your Data Warehouse

Get the most out of Redshift by performance tuning your cluster and learning how to query your data optimally.

Redshift is quickly taking its place as the world's most popular solution for dumping obscene amounts of data into storage. It's nice to see good services flourish while clunky Hadoop-based stacks of yesterdecade suffer a long, painful death. Regardless of whether you're in data science, data engineering, or analysis, it's only a matter of time before all of us work with the world's most popular data warehouse.

While Redshift's rise to power has been deserved, the unanimous popularity of any service can cause problems... namely, the knowledge gaps that come with defaulting to any de facto industry solution. Most of

Google BigQuery's Python SDK: Creating Tables Programmatically

Google BigQuery's Python SDK: Creating Tables Programmatically

Explore the benefits of Google BigQuery and use the Python SDK to programmatically create tables.

GCP is on the rise, and it's getting harder and harder to have conversations around data warehousing without addressing the new 500-pound gorilla on the block: Google BigQuery. By this point, most enterprises have comfortably settled into their choice of "big data" storage, whether that be Amazon Redshift, Hadoop, or what-have-you. BigQuery is quickly disrupting the way we think about big data stacks by redefining how we use and ultimately pay for such services.

The benefits of BigQuery likely aren't enough to force enterprises to throw the baby out with the bathwater. That said, companies building their infrastructure from the