Zero-ETL patterns to meet AI data freshness needs
Three patterns for converging transactional and analytical stores
Need for Zero-ETL in the AI-era
Let’s consider an example: You’ve published a blog on Medium and want to track its lifetime earnings in real-time. However, the current system updates these earnings only once every 24 hours.
Why is this delay happening? The data from transactional systems, which track activity related to the blog (such as views and clicks), must first be copied to a Data Lake for aggregation and analysis. This is typically handled as a batch process, where data is periodically transferred to the Data Lake at scheduled intervals.
While this is a relatively straightforward example, the challenge becomes more significant when applied to the growing number of customer-facing AI applications that rely on real-time data from the Data Lake. These applications require reducing the lag between data being generated in transactional systems and being available for consumption in the Data Lake.
Modern applications will increasingly provide analytical insights directly to end-users, require data that is as fresh and accurate as possible. Whether it’s tracking blog earnings, delivering real-time recommendations, or powering AI-driven dashboards, minimizing the delay in data movement is critical to delivering meaningful and timely insights.
The concept of Zero-ETL (Extract, Transform, Load) is changing the way organizations handle data integration. Traditional ETL pipelines, while powerful, often introduce latency, require complex setups, and demand ongoing maintenance. Zero-ETL, on the other hand, aims to eliminate these challenges by enabling seamless, real-time data movement and transformation directly within integrated systems.
This blog explores three key patterns of Zero-ETL implementation — Direct Data Synchronization, Event-Driven Architectures, and Embedded Data Processing — highlighting how each works and examples of tools that bring them to life.
1. Direct Data Synchronization
Definition:
Direct data synchronization refers to the real-time or near-real-time sharing of data between systems without staging it in intermediate storage layers. This is typically achieved using native integrations, APIs, and Change Data Capture (CDC) mechanisms that continuously track and replicate changes from source to destination systems.
How It Works:
- Native Integrations: Cloud ecosystems like AWS, Google Cloud, and Azure provide built-in connectivity between their services. For instance, a database can stream updates directly to a data warehouse.
- CDC Mechanisms: These track changes in databases (e.g., inserts, updates, deletes) and propagate them to other systems in real time.
- APIs: Application programming interfaces enable direct data sharing between tools, often bypassing the need for intermediate transformation steps.
Examples of Tools:
- Fivetran: Uses CDC to replicate changes from operational databases to analytical platforms like Snowflake or BigQuery in real-time.
- Debezium: An open-source CDC tool built on Kafka, enabling real-time synchronization of database changes.
- AWS Aurora to Redshift Integration: Allows seamless real-time synchronization between Aurora databases and Redshift without requiring ETL.
- Google Cloud BigQuery Data Transfer Service: Provides native integrations for syncing data directly from Google services (e.g., Google Ads, Analytics).
Benefits:
- Minimal latency ensures up-to-date data is available for analysis or operational use.
- Eliminates the need for intermediary storage, reducing complexity and cost.
- Ensures data consistency across systems in real-time.
2. Event-Driven Architectures
Definition:
Event-driven architectures enable systems to communicate asynchronously by publishing and subscribing to events on a central bus or streaming platform. Instead of moving data in batches, Zero-ETL systems react to individual data changes in real-time.
How It Works:
- Publish-Subscribe Model: Source systems publish events (e.g., database updates, application events) to a central streaming platform.
- Subscribers: Other systems consume these events and act on them, such as updating analytics dashboards or triggering downstream processes.
- Central Bus: Platforms like Apache Kafka or AWS EventBridge serve as the central bus for managing and distributing events.
Examples of Tools:
- Apache Kafka: Widely used for real-time streaming, Kafka acts as a central event hub, supporting Zero-ETL pipelines for real-time analytics and operational dashboards.
- AWS EventBridge: Provides event buses to connect applications and services, enabling real-time event-driven workflows.
- Google Cloud Pub/Sub: A messaging service for ingesting and processing events in real-time across cloud systems.
Use Cases:
- IoT Data Processing: Collecting sensor data and triggering actions, such as alerts or automated responses.
- Operational Automation: Publishing business events (e.g., order placement) to trigger downstream actions like inventory updates or shipment tracking.
Benefits:
- Real-time responsiveness enables faster decision-making.
- Decoupled architecture ensures systems operate independently, improving scalability and resilience.
- Flexibility to connect diverse systems, supporting hybrid and multi-cloud environments.
3. Embedded Data Processing
Definition:
Embedded data processing involves transforming data on-the-fly, often during ingestion or directly within the source or destination system. This pattern eliminates the need for separate ETL pipelines, minimizing latency and simplifying workflows.
How It Works:
- In-System Processing: Transformations are performed within the data source.
- On-the-Fly Processing: Data is transformed in transit, during ingestion into analytical or operational systems.
- Query-Based Transformations: Systems use SQL-like interfaces to define transformations applied dynamically at query time.
Examples of Tools:
- Snowflake with Snowpipe: Enables real-time data ingestion and transformation directly in the warehouse.
- Databricks Auto Loader: Performs transformations during ingestion for both batch and streaming data pipelines.
- AWS Glue DataBrew: Allows users to clean and transform data visually without the need for separate ETL tools.
- Google BigQuery: Supports query-time transformations using SQL, allowing dynamic processing without pre-staging transformed data.
Use Cases:
- Streaming ETL: Transforming log data into structured formats during ingestion for real-time analytics.
- Data Enrichment: Augmenting ingested data with external context, such as adding geolocation details to transaction records.
- Self-Service Analytics: Empowering business users to query transformed data dynamically without requiring engineering support.
Benefits:
- Reduces latency by eliminating batch transformation steps.
- Simplifies pipeline maintenance by consolidating processing within fewer systems.
- Supports real-time use cases, such as anomaly detection or operational insights.
Summary
Zero-ETL represents a significant shift in how data integration is approached, prioritizing speed, simplicity, and flexibility. It enables applications build with faster insights, lower costs, and streamlined workflows. While Zero-ETL won’t replace traditional ETL in all scenarios, particularly for complex data transformations, its real-time capabilities make it indispensable for today’s fast-paced data needs of AI applications. Zero-ETL patterns will become increasingly important in modern data platforms.
