𝗧𝘂𝘁𝗼𝗿𝗶𝗮𝗹 𝗳𝗼𝗿 𝗗𝗲𝗹𝘁𝗮 𝗟𝗮𝗸𝗲 𝗘𝗧𝗟 𝘄𝗶𝘁𝗵 𝗣𝗮𝘁𝗵𝘄𝗮𝘆 𝗳𝗼𝗿 𝗦𝗽𝗮𝗿𝗸 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀
In the era of big data, efficient data preparation and analytics is essential for deriving actionable insights. This app template by Sergey Kulik
demonstrates using Pathway for the ETL process, Delta Lake for efficient data storage, and Apache Spark for data analytics.
The comprehensive guide with code is available here: https://lnkd.in/gKFu7e5x
Using 𝗣𝗮𝘁𝗵𝘄𝗮𝘆 for Delta ETL simplifies these tasks significantly:
- Extract: You can use Airbyte to gather data from sources like GitHub, configuring it to specify exactly what data you need, such as commit history from a repository.
- Transform: Pathway helps remove sensitive information and prepare data for analysis. Additionally, you can add useful information, such as the username of the person who made the changes and the time of the changes.
- Load: The cleaned data is then saved into Delta Lake, which can be stored on your local system or in the cloud (e.g., S3) for efficient storage and analysis with Spark.
Why This Approach Works:
- Versatile Data Integration: Pathway’s Airbyte connector allows you to ingest data from any data system, be it GitHub or Salesforce, and store it in Delta Lake.
- Seamless Pipeline Integration: Expand your data pipeline effortlessly by adding new data sources without significantly changing them. Just place data into your Spark ecosystem without any heavy lifting or rewriting.
- Optimized Data Storage: Querying over data organized in Delta Lake is faster, enabling efficient data processing with Spark. Delta Lake’s scalable metadata handling and time travel support make it easy to access and query previous versions of data.