This page provides you with instructions on how to extract data from PostgreSQL and load it into Delta Lake on Databricks. (If this manual process sounds onerous, check out Stitch, which can do all the heavy lifting for you in just a few clicks.)
What is PostgreSQL?
PostgreSQL, also called Postgres, is an open source object-relational database management system that runs on all major operating systems. It's known for its stability and its ability to handle high volumes of transactions.
What is Delta Lake?
Delta Lake is an open source storage layer that sits on top of existing data lake file storage, such AWS S3, Azure Data Lake Storage, or HDFS. It uses versioned Apache Parquet files to store data, and a transaction log to keep track of commits, to provide capabilities like ACID transactions, data versioning, and audit history.
Getting data out of PostgreSQL
Most people retrieve data from relational databases by writing SQL queries. If you're just looking to export data in bulk, however, you can use the command-line tool
pg_dump to export data from a PostgreSQL database as a CSV file or a script that you can run to restore the database on any PostgreSQL server.
Loading data into Delta Lake on Databricks
To create a Delta table, you can use existing Apache Spark SQL code and change the format from
delta. Once you have a Delta table, you can write data into it using Apache Spark's Structured Streaming API. The Delta Lake transaction log guarantees exactly-once processing, even when there are other streams or batch queries running concurrently against the table. By default, streams run in append mode, which adds new records to the table. Databricks provides quickstart documentation that explains the whole process.
Keeping PostgreSQL data up to date
The script you have now should satisfy all your data needs for PostgreSQL – right? Not yet. How do you load new or updated data? It's not a good idea to replicate all of your data each time you have updated records. That process would be painfully slow; if latency is important to you, it's not a viable option.
Instead, you can identify some key fields that your script can use to bookmark its progression through the data, and pick up where it left off as it looks for updated data. Auto-incrementing fields such as updated_at or created_at work best for this. When you've built in this functionality, you can set up your script as a cron job or continuous loop to get new data as it appears in PostgreSQL.
Other data warehouse options
Delta Lake on Databricks is great, but sometimes you need to optimize for different things when you're choosing a data warehouse. Some folks choose to go with Amazon Redshift, Google BigQuery, PostgreSQL, or Snowflake, which are RDBMSes that use similar SQL syntax, or Panoply, which works with Redshift instances. Others choose a data lake, like Amazon S3. If you're interested in seeing the relevant steps for loading data into one of these platforms, check out To Redshift, To BigQuery, To Postgres, To Snowflake, To Panoply, and To S3.
Easier and faster alternatives
If all this sounds a bit overwhelming, don’t be alarmed. If you have all the skills necessary to go through this process, chances are building and maintaining a script like this isn’t a very high-leverage use of your time.
Thankfully, products like Stitch were built to move data from PostgreSQL to Delta Lake on Databricks automatically. With just a few clicks, Stitch starts extracting your PostgreSQL data, structuring it in a way that's optimized for analysis, and inserting that data into your Delta Lake on Databricks data warehouse.