![]() The Wikimedia Foundation offers a data stream of recent changes to its wikis, including Wikipedia, through its API. The data: recent changes feed from Wikipedia Relevant product documentation is also linked throughout. You might find it helpful to read this blog post for a quick primer. ![]() Note: As you read this article, you’ll encounter a couple of concepts unique to Estuary Flow. At the end of this article, you’ll find steps to materialize them to your own database so you can test them however you’d like.Ĭlick here to skip straight to the DIY section. The raw and transformed datasets we use here are both publicly available from Estuary. We’ll compare the dramatic difference in performance between queries on the raw data and the materialized view, and note the impact on our database’s CPU usage. In this article, we’ll discuss an example workflow that uses Estuary Flow to create a real-time materialized view in Postgres. On its own, Postgres doesn’t support materialized views that update continuously.īut that doesn’t mean it’s impossible to create one. They’re powerful optimization tools.īut what about materialized views of a real-time data feed? When you run a query at a set interval, you lose the real-time nature of your data. Traditional materialized views are database objects that contain the results of a query - usually, a focused subset of a large dataset. PostgreSQL is a powerful open-source database that supports materialized views. What will you do with real-time materialized views?.Step 2: Create the real-time materialized view.Step 1: Add a derivation to transform the data.Setup: Capture the data into Flow (if necessary).First attempt: Materialize the raw data feed.The data: recent changes feed from Wikipedia.
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