TCP #41: Zero-ETL Patterns on AWS with Aurora, Redshift and OpenSearch
Part 1: How AWS Aurora Zero-ETL with Redshift and OpenSearch Can Remove Batch ETL Dependencies
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This is part 1 of the 3-part series on Zero-ETL Patterns on AWS series.
In today’s newsletter issue, I’ll explore how AWS Aurora Zero-ETL integrates with Redshift and OpenSearch to enable real-time analytics, search, and machine learning without batch ETL pipelines.
The Shift from Batch to Zero-ETL
Traditional Extract, Transform, Load (ETL) processes have long been the backbone of data integration.
However, they come with significant drawbacks:
High Latency – Batch ETL jobs introduce delays.
Operational Overhead – Maintaining ETL pipelines adds complexity.
Cost Inefficiencies – Compute resources are often underutilized.
AWS introduced Zero-ETL architectures to address these challenges, eliminating the need for batch ETL by enabling real-time data replication between databases and analytics engines.
What is Zero-ETL?
Zero-ETL architectures streamline data movement by removing the need for scheduled ETL jobs.
Instead, they leverage event-driven data replication to instantly sync transactional data into data lakes, warehouses, and search engines.
How AWS Aurora Zero-ETL Works?
Amazon Aurora now supports Zero-ETL integration with Amazon Redshift and OpenSearch, allowing you to replicate transactional data in real time.
Aurora Zero-ETL to Amazon Redshift
AWS Aurora (MySQL and PostgreSQL) can now automatically stream data to Amazon Redshift, which is available for real-time analytics.
Key Features:
No need for scheduled ETL jobs – Data moves continuously.
Minimal latency – Near real-time ingestion into Redshift.
Auto-schema propagation – No manual table mapping is required.
Aurora Zero-ETL to OpenSearch
Similarly, Aurora can directly stream changes to Amazon OpenSearch Service, enabling real-time search and observability.
Key Features:
Low-latency indexing – Data is searchable within seconds.
High-performance full-text search – Supports real-time log and transaction searches.
No ETL pipeline maintenance – Reduces operational burden.
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Zero-ETL Architecture: Aurora → Redshift & OpenSearch
How It Works
Transactional Data Inserted – New data is written to Aurora.
Change Data Capture (CDC) – Aurora captures and streams changes to Redshift and OpenSearch.
Real-Time Analytics & Search
Redshift enables BI dashboards & ML analytics.
OpenSearch provides fast query-based search & logs processing.
Benefits of Zero-ETL Over Traditional ETL
Traditional ETL
Latency - Hours to days (batch)
Complexity - Requires manual ETL Jobs
Scalability - Limited by batch job sizes
Cost - High (compute + storage)
Use Cases - BI, Batch Analytics
Zero-ETL
Latency - Near real-time (seconds)
Complexity - Fully managed & automated
Scalability - Scales elastically
Cost - Optimized pay-as-you-go pricing
Use Cases - Streaming, log processing, AI/ML
Use Cases for Zero-ETL in AWS
Real-Time Analytics Dashboards – No need for scheduled ETL pipelines; BI dashboards reflect data instantly.
Fraud Detection & Monitoring – Capture real-time anomalies instead of waiting for batch processing.
Log Search & Observability – Index logs instantly in OpenSearch for real-time log analytics.
E-Commerce Product Search – Ensure inventory updates are reflected immediately in search results.
Financial Transaction Processing – Monitor bank transactions for fraud detection as they happen.
Setting Up AWS Aurora Zero-ETL
Step 1: Enable Zero-ETL Integration
Enable Zero-ETL to Redshift:
aws rds modify-db-cluster \ --db-cluster-identifier aurora-cluster \ --enable-zero-etl-integration
Enable Zero-ETL to OpenSearch:
aws rds modify-db-cluster \ --db-cluster-identifier aurora-cluster \ --enable-zero-etl-to-opensearch
Step 2: Verify Data Flow
Run a transaction in Aurora:
INSERT INTO orders (id, customer_name, total_amount) VALUES (1001, 'Alice', 250.00);
Query Redshift:
SELECT * FROM orders WHERE id = 1001;
Search in OpenSearch:
GET _search { "query": { "match": { "customer_name": "Alice" } } }
Best Practices for Zero-ETL on AWS
Use Change Data Capture (CDC) wisely – Only replicate necessary data, reducing cost & latency.
Monitor replication lag – Use Amazon CloudWatch metrics to ensure low-latency streaming.
Partition Redshift data – Optimize query performance by partitioning time-sensitive data.
Tune OpenSearch indexing – Avoid excessive writes that may cause shard imbalance.
Secure data streams – Encrypt Zero-ETL pipelines using AWS KMS and IAM policies.
Final Thoughts
Zero-ETL is an innovative approach for real-time analytics and search.
By removing batch ETL dependencies, AWS Aurora seamlessly integrates with Redshift and OpenSearch, eliminating operational overhead and delivering real-time insights at scale.
Stay tuned for next week, where I discuss Zero ETL integration between DynamoDB and Redshift.
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Until next week — Amrut
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