The Evolution Of Polybase

Asad Khan gets into improvements in SQL Server 2019:

  • Break down data silos and deliver one view across all of your data using data virtualization. Starting in SQL Server 2016, PolyBase has enabled you to run a T-SQL query inside SQL Server to pull data from your data lake and return it in a structured format—all without moving or copying the data. Now in SQL Server 2019, we’re expanding that concept of data virtualization to additional data sources, including Oracle, Teradata, MongoDB, PostgreSQL, and others. Using the new PolyBase, you can break down data silos and easily combine data from many sources using virtualization to avoid the time, effort, security risks and duplicate data created by data movement and replication. New elastically scalable “data pools” and “compute pools” make querying virtualized data lighting fast by caching data and distributing query execution across many instances of SQL Server.

Just in time for me to scramble to update Polybase slides for Conference Season…

Related Posts

Connecting PolyBase to Spark

I have a blog post connecting PolyBase to a Spark cluster: If you do define your Spark DataFrames well, you get a much happier result. Here’s me creating a better-looking DataFrame in Spark: import org.apache.spark.sql.functions._ spark.sql(""" SELECT INT(SUMLEV) AS SummaryLevel, INT(COUNTY) AS CountyID, INT(PLACE) AS PlaceID, BOOLEAN(PRIMGEO_FLAG) AS IsPrimaryGeography, NAME AS Name, POPTYPE AS PopulationType, […]

Read More

PolyBase on Linux

I have a post showing how to set up PolyBase on Linux: Now that we have SQL Server on Linux installed, we can begin to install PolyBase. There are some instructions here but because we started with the Docker image, we’ll need to do a little bit of prep work. Let’s get our shell on. First, run docker […]

Read More

Categories

September 2018
MTWTFSS
« Aug Oct »
 12
3456789
10111213141516
17181920212223
24252627282930