Part I: Overview
Overview
Delta is a data synchronization and enrichment platform developed by Netflix to address the challenges of keeping multiple datastores in sync while allowing for data enrichment. It utilizes an event-driven architecture and Change Data Capture (CDC) to streamline data processing across various microservices.
What You'll Learn
1
How to implement Change Data Capture (CDC) using Delta-Connector
2
Why an event-driven architecture improves data synchronization
3
How to enrich data on-the-fly in a microservices architecture
Prerequisites & Requirements
- Understanding of microservices architecture
- Familiarity with Kafka and Flink(optional)
Key Questions Answered
What are the limitations of dual writes in data synchronization?
Dual writes can lead to inconsistencies between datastores if one write fails after the other succeeds. A repair routine is often needed, but it can add complexity and stress to the primary data source. This approach is not scalable when dealing with multiple datastores.
How does Delta improve data synchronization and enrichment?
Delta simplifies the process of keeping multiple datastores in sync by using an event-driven architecture that captures changes in real-time. It allows for data enrichment by integrating with external services, thus abstracting complexities from application developers.
What features does Delta-Connector provide for CDC?
Delta-Connector captures real-time changes from datastores and publishes them as Delta events. It supports writing to custom outputs, manual dumps, and high availability without locking tables, ensuring minimal impact on database performance.
What architectural patterns does Delta utilize?
Delta employs an event-driven architecture with Change Data Capture (CDC) to facilitate real-time data synchronization and enrichment across microservices, making it easier to manage complex data workflows.
Technologies & Tools
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Messaging
Kafka
Used for transporting CDC events in real-time.
Stream Processing
Flink
Used in the Delta Stream Processing Framework for processing and enriching data.
Database
Mysql
Supported by Delta-Connector for capturing changes.
Database
Postgresql
Supported by Delta-Connector for capturing changes.
Database
Cassandra
Supported by Delta-Connector for capturing changes.
Key Actionable Insights
1Implementing Delta can streamline your data synchronization processes significantly.By adopting Delta, teams can reduce the complexity of managing multiple datastores and focus on building business features rather than maintaining synchronization logic.
2Utilizing CDC with Delta-Connector allows for real-time data updates.This ensures that your applications always work with the most current data, which is crucial for user-facing features that rely on up-to-date information.
3Leveraging an event-driven architecture can enhance system resilience.By decoupling services and using events to communicate changes, systems can better handle failures and scale more effectively.
Common Pitfalls
1
Relying solely on dual writes can lead to data inconsistencies.
This happens because if one write fails after the other succeeds, the datastores can become out of sync, necessitating complex repair routines that may not be reusable.
2
Implementing a Change Log Table requires careful consideration of schema changes.
Since some databases do not support transactional schema changes, capturing schema changes accurately can be challenging and may lead to inconsistencies if not handled properly.
Related Concepts
Change Data Capture (cdc)
Event-driven Architecture
Microservices Data Synchronization
Data Enrichment Techniques