Quick Start
Enable serverless, realtime analytics on Postgres in seconds.
1. Install locally or deploy the server
Create analytics schema and table
Insert sample data
Run example queries
2. Create analytics schema and table
We will connect to Hydra using psql, the terminal-based front-end to Postgres, but you can use any Postgres client you’d prefer and achieve the same result. We will start by creating our analytics schema with a table “analytics.rides” which we will populate with sample data from the New York Taxi data in step 3.
3. Insert sample data
Let’s insert the New York Taxi data into our rides table in the analytics schema. Below we’ve written example queries you can copy / paste.
4. Run example queries
When using cloud hosted Hydra run each statement by clicking the green triangle next to each query in the SQL Editor.
5. Insert existing data
Now let’s test Hydra with your own data.
It’s a good idea to drop the “analytics.rides” sample data that was inserted earlier before loading your own files from S3, PostgreSQL, Google Cloud Storage, or local files.
To delete the sample data:
Note: Switch the codebox tabs for steps on S3, Postgres, and/or local data.
What’s Next?
We recommend
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following the migration documentation from Object Storage (S3, GCS), Amazon RDS, Heroku, Render, Postgres.
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learning about Hydra, our team and partners behind the project
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scanning the Hydra architecture