Data Ingestion Strategies on AWS
Outline
- Data Ingestion Patterns
- Ingesting Data into Data Warehouses
- Using AWS Glue and AWS Redshift for Data Warehouse Ingestion
- Ingesting Data into Data Lakes
- AWS Glue for Batch and Streaming Ingestion
- Amazon Kinesis Data Streams and Amazon MSK for Real-time Ingestion
- Amazon S3 and Amazon Athena for File-based Ingestion
- Ingesting Data into Lakehouse
- Amazon Sagemaker Lakehouse and Zero-ETL Integrations
- Ingesting Data into Log and Analytics Services
- Amazon OpenSearch Service Integrations
- Ingestion Strategies and Best Practices
- Leveraging Zero-ETL Integrations
- Optimizing Performance and Cost for Ingestion
1. Data Ingestion Patterns
The presenters discussed three main data ingestion patterns:
- Inside-Out: Data ingested from a centralized data lake to purpose-built data stores like data warehouses or ML applications.
- Outside-In: Data coming from business partners or specialized systems shared with the centralized data hub.
- Around the Perimeter: Users sharing data with each other to meet common business goals.
2. Ingesting Data into Data Warehouses
- Presenters used Amazon Redshift as an example data warehouse.
- Highlighted the use of AWS Glue and AWS Redshift's Zero-ETL integration for efficient data ingestion.
- Zero-ETL allows configuring data movement without creating custom pipelines.
- Supports integration with various data sources like databases, SaaS applications, and files.
- Discussed strategies like auto-copy from S3, integrating streaming data from Kinesis/MSK, and using AWS DMS for on-premises database ingestion.
3. Ingesting Data into Data Lakes
- Presenters discussed using AWS Glue for both batch and streaming data ingestion into data lakes.
- Glue provides connectors for various data sources and supports custom connectors.
- Continuously running Glue jobs for real-time ingestion from streaming sources like Kinesis and MSK.
- Highlighted Amazon S3 and Amazon Athena for file-based ingestion and querying.
- Discussed the use of Amazon Kinesis Data Firehose for efficient, scalable, and cost-effective data ingestion into data lakes.
4. Ingesting Data into Lakehouse
- Presenters introduced the concept of Amazon Sagemaker Lakehouse, which bridges the gap between data warehouses and data lakes.
- Discussed using Zero-ETL Integrations to ingest data from various sources directly into the Lakehouse.
- Highlighted the support for open table formats like Apache Iceberg, Apache Hudi, and Delta Lake for Lakehouse ingestion.
5. Ingesting Data into Log and Analytics Services
- Presenters focused on ingesting data into Amazon OpenSearch Service for log and security analytics.
- Covered Zero-ETL Integrations with data sources like DynamoDB, DocumentDB, and S3.
- Discussed direct querying of data in S3 and CloudWatch Logs without the need for full ingestion.
6. Ingestion Strategies and Best Practices
- Leverage Zero-ETL Integrations to reduce operational overhead and improve data availability.
- Optimize performance and cost by:
- Choosing the right worker types and auto-scaling for AWS Glue jobs.
- Utilizing Kinesis Data Streams' enhanced fan-out and express Brokers for MSK.
- Implementing fault tolerance and parallelism strategies for Flink.
- Configuring dead-letter queues and selective field mapping for OpenSearch ingestion.
Overall, the presenters provided a comprehensive overview of various data ingestion patterns and strategies, highlighting the use of managed AWS services to build efficient, scalable, and cost-effective data ingestion architectures.