This page provides you with instructions on how to extract data from Sage Intacct and load it into Snowflake. (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 Sage Intacct?
Sage Intacct provides accounting and financial management software with automation and controls around billing, accounting, and reporting. Components include accounts payable, accounts receivable, cash management, general ledger, order management, and purchasing.
What is Snowflake?
Snowflake is a cloud-based data warehouse implemented as a managed service. It runs on the Amazon Web Services architecture using EC2 and S3 instances. Snowflake is designed to be fast, flexible, and easy to work with. For instance, for query processing, Snowflake creates virtual warehouses that run on separate compute clusters, so querying one virtual warehouse doesn't slow down the others.
Getting data out of Sage Intacct
Sage Intacct provides an API that lets developers retrieve data stored in the platform. Intacct also has a Data Delivery Service (DDS) that enables companies to extract data from the platform and send it to a cloud storage location.
Preparing data for Snowflake
Depending on the structure of your data, you may need to prepare it for loading. Look at the supported data types for Snowflake and make sure that the data you've got will map neatly to them.
Note that you don't need to define a schema in advance when loading JSON data into Snowflake.
Loading data into Snowflake
The Snowflake documentation's Data Loading Overview section can help you with the task of loading your data. If you're not loading a lot of data, you might be able to use the data loading wizard in the Snowflake web UI, but chances are the limitations on that tool will make it a non-starter as a reliable ETL solution. Alternatively, there are two main steps for getting data into Snowflake:
- Use the PUT command to stage files.
- Use the COPY INTO table command to load prepared data into an awaiting table.
You’ll have the option of copying from your local drive or from Amazon S3. One of Snowflake's slick features lets you make a virtual warehouse that can power the insertion process.
Keeping Sage Intacct data up to date
You can code 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. Once you've taken new data into account, you can set your script up as a cron job or continuous loop to keep pulling down new data as it appears.
Other data warehouse options
Snowflake 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 Microsoft Azure SQL Data Warehouse, 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 Panoply, To Azure SQL Data Warehouse, 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 Sage Intacct to Snowflake automatically. With just a few clicks, Stitch starts extracting your Sage Intacct data via the API, structuring it in a way that's optimized for analysis, and inserting that data into your Snowflake data warehouse.