Cheat Sheet: Batch Data Ingestion on AWS

Batch data ingestion is the process of collecting and moving data in large volumes (batches) from source systems to a target location, typically a data lake or data warehouse. This process runs at regular, scheduled intervals (e.g., hourly, daily) and is suitable for workloads that can tolerate some delay between when data is generated and when it becomes available for analysis.

Why Batch Ingestion?

• Efficiency: Processing data in large chunks is often more efficient for systems than handling a constant stream of individual records.

• Cost-Effectiveness: It allows you to use resources only when the batch job is running, which can be more economical than maintaining an always-on streaming infrastructure.

• Simplicity: For many traditional analytics use cases, like end-of-day reporting, the complexity of real-time streaming is unnecessary.

Common Architectural Pattern on AWS

A typical batch data ingestion pipeline on AWS involves several stages, with specific services optimized for each step.

1. Data Transfer/Ingestion

This is the process of moving data from its source into the AWS environment.

• Source: Data can come from on-premises databases, SaaS applications, third-party systems, or IoT devices.

• AWS Services for Transfer:

  • AWS Transfer Family (SFTP/FTP/FTPS): A fully managed service for transferring files directly into Amazon S3 using standard transfer protocols. This is ideal for receiving batch files from external partners.

  • AWS DataSync: An online data transfer service that simplifies, automates, and accelerates moving large amounts of data between on-premises storage and AWS storage services like S3.

  • AWS Snowball: A physical device used to migrate petabyte-scale data into AWS when network transfer is not feasible.

  • Database Migration Service (DMS): While often used for continuous replication, DMS can also be used for one-time or recurring batch data loads from relational databases into AWS.

2. Data Storage (Data Lake)

The raw data, once ingested, needs a central, scalable, and durable place to land.

• Primary Service: Amazon S3 (Simple Storage Service)

  • S3 is the foundation of a data lake on AWS. It provides virtually unlimited scalability, high durability, and cost-effective storage.

  • Raw data is typically landed in its original format in a "bronze" or "raw" zone S3 bucket.

3. Data Cataloging and Transformation (ETL)

The raw data needs to be discovered, cataloged, cleaned, transformed, and optimized for analysis.

• Primary Service: AWS Glue

  • Glue Crawlers: Scan the raw data in S3 to automatically infer schemas and create table definitions in the AWS Glue Data Catalog.

  • Glue ETL Jobs: Serverless Apache Spark jobs that take the raw data as input, perform transformations (e.g., cleaning, joining, aggregating, converting formats), and write the curated, analysis-ready data back to a "silver" or "processed" zone in S3.

4. Data Warehouse / Analytics Consumption

The processed data is made available to end-users and BI tools for querying and analysis.

• Primary Service: Amazon Redshift

  • A fully managed, petabyte-scale data warehouse. You can load the transformed data from S3 into Redshift for high-performance SQL analytics.

  • Redshift Spectrum: Allows you to run SQL queries directly against data in your S3 data lake without having to load it into Redshift tables, blending the data lake and data warehouse.

• Alternative Service: Amazon Athena

  • A serverless, interactive query service that makes it easy to analyze data directly in S3 using standard SQL. Athena is perfect for ad-hoc querying of the data lake without needing to manage a data warehouse.

5. Orchestration and Scheduling

The entire end-to-end pipeline needs to be automated and scheduled.

• AWS Services for Orchestration:

  • AWS Glue Workflows: The native way to chain together crawlers and ETL jobs into a dependency-based pipeline.

  • AWS Step Functions: A serverless function orchestrator that can manage more complex workflows involving Glue, Lambda, and other AWS services.

  • Amazon EventBridge: Used to trigger pipelines based on a schedule (e.g., cron(0 12 * * ? *) for noon every day) or an event (e.g., the arrival of a specific file in S3).

Best Practices for Batch Ingestion

• Optimize Storage Formats: For analytics, convert raw data (like CSV or JSON) into columnar formats like Apache Parquet or Apache ORC. This drastically improves query performance and reduces costs for services like Athena and Redshift Spectrum.

• Partition Your Data: Organize your data in S3 into partitions, typically by date (e.g., s3://bucket/data/year=2024/month=06/day=23/). This allows query engines to scan only the relevant data, saving time and money.

• Use Data Quality Checks: Integrate AWS Glue Data Quality into your ETL jobs to validate data and prevent bad data from corrupting your analytics.

• Secure Your Pipeline: Use AWS Lake Formation to provide centralized, fine-grained access control to your data lake. Use IAM roles and policies to enforce the principle of least privilege for all services and users.

• Monitor Everything: Use Amazon CloudWatch to monitor metrics, logs, and pipeline execution status. Set up alarms to be notified of failures or anomalies.