Apache Spark
by Apache Software Foundation
Spark often handles the heaviest processing in a data platform, with results stored in tables that other tools struggle to reach. Through Spark Connect or the Thrift JDBC server, a Python connector runs SQL and brings the result sets into the Lake. Model outputs, aggregates and feature tables then sit where BI and AI Minds can use them next to everything else.
- Category
- Cloud & Data Platforms
- Type
- Query engine
- Interface
- gRPC API
- Built with
- Connect Anything
- Credentials
- Access token, Vendor credentials
02Data
What you can sync.
- Sql query results
- Tables
- Dataframes
Put Apache Spark sql query results next to revenue and usage, so every cost has an owner and a reason.
Need more? Name any other Apache Spark data the API returns, and the AI adds it to the connector.
03Credentials
Credentials you will need.
- An access token
- Other credentials
Apache Spark uses an access token or other credentials. You add the credentials once, when the AI asks for them, and it tells you where to create them in Apache Software Foundation if you need help.
04Questions
Questions you can answer.
Once Apache Spark data is in your Lake, ask an AI Mind in plain words or build the answer in DataPrism BI.
- How do the churn scores produced in Spark compare with actual cancellations last quarter?
- Which feature tables computed in Spark changed row counts unexpectedly between runs?
- How do Spark-computed daily aggregates reconcile with the raw source totals?
- Which customer segments from the Spark model generate the most revenue?
05How it works
How it works in DataMind.
-
Pick Apache Spark.
Open Lake, pick Apache Spark in the source picker and name the connection. Connect Anything opens with a ready prompt that points the AI at the Apache Spark API docs.
-
Answer a few questions.
The AI reads the Apache Spark documentation, then asks which credentials you will use, which data to sync (for example sql query results and tables), whether you want a one-time load or a recurring sync, and which accounts or filters apply.
-
Your data lands in Lake.
The connector agent writes a Python connector for Apache Spark, tests it against the live API and shows you sample rows. Then the data loads into your Lake on your schedule, ready for the Warehouse, DataPrism BI and AI Minds.
06Pairs
Works well with Apache Spark.
Sources teams join with Apache Spark in the Warehouse, each one connected the same way.
