Solving different data ingestion use cases with AWS (ANT330)

Data Ingestion Strategies on AWS

Outline

  1. Data Ingestion Patterns
  2. Ingesting Data into Data Warehouses
    • Using AWS Glue and AWS Redshift for Data Warehouse Ingestion
  3. 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
  4. Ingesting Data into Lakehouse
    • Amazon Sagemaker Lakehouse and Zero-ETL Integrations
  5. Ingesting Data into Log and Analytics Services
    • Amazon OpenSearch Service Integrations
  6. 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:

  1. Inside-Out: Data ingested from a centralized data lake to purpose-built data stores like data warehouses or ML applications.
  2. Outside-In: Data coming from business partners or specialized systems shared with the centralized data hub.
  3. 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.

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