Spark is a big deal in big data and so is Snowflake, but what if you want to run your Spark SQL queries with a prebuilt connection to a Snowflake environment?
Well, it turns out there's a hard way and an easy way.
Before we look at the easy way, let's see what it's compared to. We'll use Spark Python (PySpark) for this example.
Quick refresher: what is Spark and why do we care about it if we are using Snowflake?
Apache Spark is an open-source solution for running really big workloads on data, utilizing clusters of machines to ramp up the power and performance. Running Spark queries on data in Snowflake is interesting as companies are quickly finding that keeping their databases in Snowflake storage is cost effective and easily scaled.
Here are the steps to get your Spark cluster to work with Snowflake:
Sounds simple, right?
Get BlackDiamond Studio and here's all you'll need to know:
That's it. BlackDiamond Studio will do the rest, using a prebuilt template and an instant git repo we'll set-up for you. All the connection stuff is built right in, so you can spend your time getting work done instead of troubleshooting configurations.
Try BlackDiamond Studio today. The fastest, easiest way to use Snowflake.