Panoply is a fully managed data warehouse service that will spin up a Redshift instance in just a few clicks. With Panoply, you can use your favorite analysis, SQL, and visualization tools just like you would if you were creating a Redshift data warehouse on your own.
If you’re looking for a cost-effective, no-effort way to test out Stitch or get started consolidating your data, Panoply is your best bet.
In this guide, we'll walk you through:
- Panoply's pricing model,
- Some high-level limitations (including any incompatible data sources),
- How to spin up a Panoply data warehouse of your own, and
- How Stitch loads and organizes data in Panoply.
In just a few minutes, you can create a Panoply account and spin up your own Redshift data warehouse.
Pricing varies from plan to plan, but every Panoply plan includes:
- Unlimited queries
- Unlimited user accounts
- Automatic maintenance, vacuuming, and backups
To learn more about each of Panoply’s plans, check out their pricing page.
Every database has its own supported limits and way of handling data, and Panoply is no different. The table below provides a very high-level look at what Panoply supports, including any possible incompatibilities with Stitch’s integration offerings.
|Incompatibile Sources||Possible incompatibilities. Learn more.|
Panoply’s full list; Stitch reserves
|Table Name Length||127 characters||Column Name Length||115 characters|
|Max # of Columns||1,600||VARCHAR Max Width||65K|
|Nested Structure Support||None; structures will be de-nested. Learn more.||Case||Case Insensitive|
Not sure if Panoply is the data warehouse for you? Check out the Choosing a Stitch Destination guide to compare each of Stitch’s destination offerings.
After you’ve successfully connected your Panoply data warehouse to Stitch, you can start adding integrations and replicating data.
For each integration that you add to Stitch, a schema specific to that integration will be created in your data warehouse. This is where all the tables for that inegration will be stored.
Stitch will encounter dozens of scenarios when replicating and loading your data. To learn more about how Panoply handles these scenarios, check out the Data Loading Guide for Panoply.
Rejected Records Log
Occasionally, Stitch will encounter data that it can’t load into the data warehouse. For example: a table contains more columns than Panoply’s allowed limit of 1,600 columns per table.
When this happens, the data will be “rejected” and logged in a table called
_sdc_rejected. Every integration schema created by Stitch will include this table as well as the other tables in the integration.
Stitch replicates data from your sources based on the integration’s Replication Frequency and the Replication Method used by the tables in the integration. In Stitch, you have the ability to control what and how often data is replicated for the majority of integrations.
The time from the sync start to data being loaded into your data warehouse can vary depending on a number of factors, especially for initial historical loads.
To learn more about Stitch’s replication process and how loading time can be affected, check out the Stitch Replication Process guide.