This page provides you with instructions on how to extract data from Amazon RDS 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 Amazon RDS?
Amazon RDS (relational database service) lets users spin up cloud-based database instances without worrying about infrastructure provisioning or software maintenance or many of the administrative tasks involved in running a database on premises.
Cloud platforms can scale up or down quickly to meet changing demands. RDS takes advantage of that capability to let users add database instances to as needed. It offers automatic backup and recovery for database instances, and can replicate data across multiple zones for high availability.
RDS supports six different database engines: Amazon Aurora, PostgreSQL, MySQL, MariaDB, Oracle Database, and Microsoft SQL Server.
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 Amazon RDS
The most common way to get data out of any database is to write SQL SELECT queries. As part of any query you can join tables, specify filters, and sort and limit results.
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 Amazon RDS data up to date
At this point you've coded up a script or written a program to get the data you want and successfully moved it into your data warehouse. But how will 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 and resource-intensive.
The key is to build your script in such a way that it can identify incremental updates to your data. You can identify 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 your database.
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 Amazon RDS to Delta Lake on Databricks automatically. With just a few clicks, Stitch starts extracting your Amazon RDS data, structuring it in a way that's optimized for analysis, and inserting that data into your Delta Lake on Databricks data warehouse.