This page provides you with instructions on how to extract data from Microsoft SQL Server and load it into PostgreSQL. (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 Microsoft SQL Server?
Microsoft SQL Server is a relational database management system that supports applications on a single machine, on a local area network, or across the web. SQL Server supports Microsoft's .NET framework out of the box, and integrates nicely into the Microsoft ecosystem.
What is PostgreSQL?
PostgreSQL, also known as Postgres, calls itself "the world's most advanced open source database." The popular object-relational database management system (ORDBMS) offers enterprise-grade features with a strong emphasis on extensibility and standards compliance.
PostgreSQL runs on all major operating systems, including Linux, Unix, and Windows. It's open source, fully ACID-compliant, and has full support for foreign keys, joins, views, triggers, and stored procedures in multiple languages. PostgreSQL is often the best back-end database for web systems and software tools. It's available in cloud-based deployments by most major cloud vendors. And since its syntax forms the basis for querying Amazon Redshift, which makes migration between the two systems relatively painless, Postgres a good stepping-stone for developers who may later use Redshift's data warehouse platform.
Getting data out of SQL Server
The most common way most folks who work with databases get their data is by using queries for extraction. With SELECT statements you can filter, sort, and limit the data you want to retrieve. If you need to export data in bulk, you can use Microsoft SQL Server Management Studio, which enables you to export entire tables and databases in formats like text, CSV, or SQL queries that can restore the database if run.
Loading data into Postgres
Once you have identified all of the columns you will want to insert, you can use the
CREATE TABLE statement in Postgres to create a table that can receive all of this data. Then, Postgres offers a number of methods for loading in data, and the best method varies depending on the quantity of data you have and the regularity with which you plan to load it.
For simple, day-to-day data insertion, running
INSERT queries against the database directly are the standard SQL method for getting data added. Documentation on INSERT queries and their bretheren can be found in the Postgres documentation here.
For bulk insertions of data, which you will likely want to conduct if you have a high volume of data to load, other tools exist as well. This is where the
COPY command becomes quite useful, as it allows you to load large sets of data into Postgres without needing to run a series of INSERT statements. Documentation can be found here.
The Postgres documentation also provides a helpful overall guide for conducting fast data inserts, populating your database, and avoiding common pitfalls in the process. You can find it here.
Keeping SQL Server data up to date
All set! You've written a script to move data from SQL Server into your data warehouse. But data freshness is one of the most important aspects of any analysis – what happens when you have new data that you need to add?
You could load the entire SQL Server database again. Doing this is almost guaranteed to be slow and painful, and cause all kinds of latency.
A better approach is to build your script to recognize new and updated records in the source database. Using an auto-incrementing field as a key is a great way to accomplish this. The key functions something like a bookmark, so your script can resume where it left off. 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 SQL Server.
Other data warehouse options
PostgreSQL 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, Snowflake, or Microsoft Azure Synapse Analytics, which are RDBMSes that use similar SQL syntax, or Panoply, which works with Redshift instances. Others choose a data lake, like Amazon S3 or Delta Lake on Databricks. If you're interested in seeing the relevant steps for loading data into one of these platforms, check out To Redshift, To BigQuery, To Snowflake, To Panoply, To Azure Synapse Analytics, To S3, and To Delta Lake.
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 Microsoft SQL Server to PostgreSQL automatically. With just a few clicks, Stitch starts extracting your Microsoft SQL Server data, structuring it in a way that's optimized for analysis, and inserting that data into your PostgreSQL data warehouse.