Analytics SDKs for event data transformation

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The Snowplow Analytics SDKs are designed for data engineers and data scientists working with Snowplow in a number of languages.

Some good use cases for the SDKs include:

1. Transforming the Enriched TSV to Enriched JSON for further processing
2. Developing AI/ML models on your event data
3. Performing analytics-on-write in AWS Lambda as part of our Kinesis real-time pipeline
4. Within Snowplow pipeline components to process event data

The Analytics SDKs parse one enriched event at a time. If you read from an enriched stream with compression enabled, each stream record contains many events. Decompress the record and split it into events before passing each event to the SDK.

Snowplow Analytics SDKs

• Scala Analytics SDK - lets you work with Snowplow enriched events in your Scala event processing, data modeling and machine-learning jobs. You can use this SDK with Apache Spark, AWS Lambda, GCP Cloud Functions, Apache Flink and other Scala-compatible data processing frameworks.
• JavaScript and TypeScript Analytics SDK - lets you work with Snowplow enriched events in your Node.js or other JavaScript environments. This SDK can be used with AWS Lambda and Google Cloud Functions.
• Go Analytics SDK - lets you work with Snowplow enriched events in your Go environments. This SDK can be used with AWS Lambda and Google Cloud Functions.
• Python Analytics SDK - lets you work with Snowplow enriched events in your Python event processing, data modeling and machine-learning jobs. You can use this SDK with Apache Spark, AWS Lambda, GCP Cloud Functions and other Python-compatible data processing frameworks.
• .NET Analytics SDK - lets you work with Snowplow enriched events in your .NET event processing, data modeling and machine-learning jobs. You can use this SDK with Azure Data Lake Analytics, Azure Function, AWS Lambda, GCP Cloud Functions other .NET-compatible data processing frameworks.