AWS Glue is a fully managed extract, transform, load (ETL) service that makes it easy to prepare and load data for analytics. Furthermore, AWS Glue handles data streams using various methods. Here’s a quick overview of working with the streaming data using AWS Glue.
1. Data Sources: kinesis data stream
- AWS Kinesis: You can use AWS Glue to read data from Amazon Kinesis Data Streams( which is a service for real-time data streaming).
- Amazon MSK (Managed Streaming for Kafka): AWS Glue can connect to Kafka clusters ( Amazon MSK manages for streaming data processing).
2. Jobs and Triggers
- You can create Glue jobs triggered based on events in your streaming sources. For example, you can set up a trigger that runs a Glue job every time new data is available in a Kinesis stream.
3. Transformations
- AWS Glue provides a variety of transformations that you can apply to your streaming data. You can use Glue’s built-in libraries for data cleansing, normalization, and enrichment.
“An investment in knowledge pays the best interest.”
Benjamin Franklin
4. Glue Data Catalog
- Glue maintains a central repository (Called the Data Catalog), which keeps metadata about your data. This is especially useful for tracking schemas and data types for streaming datasets.
5. Output
- After processing, you can save the transformed data in formats like Parquet and send it to places like Amazon S3, Redshift, or databases.
6. Integration with AWS Services
- AWS Glue works with other AWS services to create data pipelines. It can be used with Amazon S3, AWS Lambda, and Amazon Athena.
Use Case Example
Suppose you’re collecting logs from a web application via Amazon Kinesis. You can use AWS Glue to automatically catalog this streaming data, perform transformations (like filtering out unnecessary fields), and then store the processed data in Amazon S3 for further analysis using Amazon Athena.
References
Srinimf 
You must be logged in to post a comment.