Serverless Analytics on Postgres.
Deploy the server
Create a table
Insert sample data
Run example queries
Select the Scale plan for a 14-day free trial with a $300 usage credit. A valid credit card is required to verify the account.
To deploy a cloud database, navigate to https://platform.hydra.so/projects.
For a detailed walkthrough, check out the Deploying Hydra Cloud guide.
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.
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.
Q0: SELECT COUNT(*)
SELECT COUNT(*) … counts the number of rows in the analytics.rides table.
Q1: SELECT * FROM
SELECT * FROM … retrieves up to 10 rows of data from the analytics.rides table.
Q2: SELECT vendor_id
SELECT vendor_id … is list of the top 10 vendors, displaying the number of rides and total revenue for each, ordered by the greatest number of rides first.
When using cloud hosted Hydra run each statement by clicking the green triangle next to each query in the SQL Editor.
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.
We recommend
Serverless Analytics on Postgres.
Deploy the server
Create a table
Insert sample data
Run example queries
Select the Scale plan for a 14-day free trial with a $300 usage credit. A valid credit card is required to verify the account.
To deploy a cloud database, navigate to https://platform.hydra.so/projects.
For a detailed walkthrough, check out the Deploying Hydra Cloud guide.
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.
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.
Q0: SELECT COUNT(*)
SELECT COUNT(*) … counts the number of rows in the analytics.rides table.
Q1: SELECT * FROM
SELECT * FROM … retrieves up to 10 rows of data from the analytics.rides table.
Q2: SELECT vendor_id
SELECT vendor_id … is list of the top 10 vendors, displaying the number of rides and total revenue for each, ordered by the greatest number of rides first.
When using cloud hosted Hydra run each statement by clicking the green triangle next to each query in the SQL Editor.
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.
We recommend