Role of Data Contracts in Data Pipeline

What are Data Contracts?

A data contract is an agreement or set of rules defining how data should be structured and processed within a system. It serves as a crucial communication tool between different parts of an organization or between various software components. It refers to the management and intended data usage between different organizations or sometimes within a single company.

The primary purpose of a data contract is to ensure that data remains consistent and compatible across different versions or components of a system. A data contract includes the following – 

• Terms of Services: Description of how the data can be used, whether for development, testing, or deployment.
• Service Level Agreements (SLA): SLAs describe the quality of data delivery and might include uptime, error rates, availability, etc. 

Similar to how business contracts outline responsibilities between suppliers and consumers of a product, data contracts establish and ensure the quality, usability, and dependability of data products.

What Metadata should be included in a Data Contract?

• Schema: Schema provides useful information on data processing and analysis. It is a set of rules and constraints placed on the columns of a dataset. Data Sources evolve, and producers must ensure detecting and reacting to schema changes is possible. Consumers should be able to process data with the old Schema.
• Semantics:  Semantics capture the rules of each business domain. They include aspects like how businesses transition to and from different stages within their lifecycle, how they relate to one another, etc. Just like Schema, Semantics can also evolve over a period of time.
• Service Level Agreements (SLAs): SLAs specify the availability and freshness of data in a data product. They help data practitioners design data consumption pipelines effectively. SLAs include commitments like maximum expected delay, when is the new data expected in the data product and metrics like mean time between failures and mean time to recovery.

What is the significance of Data Contracts?

The primary benefit of a data contract is its role in ensuring compatibility and consistency between various versions of data schemas. Specifically, data contracts offer several advantages:

1. Compatibility Assurance: When a data contract is established to define data structure and rules, it guarantees that data produced and consumed by different components or system versions remain compatible. This proactive approach minimizes data processing complications during schema evolution.
2. Consistency Enforcement: Data contracts act as enforcers of consistency in data representation. They compel all producers and consumers to adhere to the same Schema, promoting data correctness and enhancing system reliability.
3. Version Control: Data contracts can undergo versioning and tracking over time. This capability enables structured management of changes to data schemas, which is invaluable for navigating schema evolution seamlessly.
4. Effective Communication: Data contracts are an effective communication tool among diverse organizational teams or components. They establish a shared understanding of data structures and formats, fostering collaboration.
5. Error Prevention: A well-defined data contract prevents error, particularly in schema mismatches or unexpected alterations. It facilitates early detection of schema-related issues.

Practical Ways to Enforce Data Contracts

In this data processing pipeline, schema changes are managed within a Git repository and applied to data-producing applications, ensuring consistent data structures. The applications send their data to Kafka Topics, separating raw data from Change Data Capture (CDC) streams. A Flink App validates the data against Schema Registry schemas from the raw data streams. Any inaccurate data is directed to the Dead Letter Topic, while valid data is sent to the validated Data Topic. Real-time applications can directly access data from these validated topics.

Furthermore, data from the validated Data Topic is stored for additional checks, including validation against specific Service Level Agreements (SLAs). Subsequently, this data is sent to the Data Warehouse for in-depth analysis. Should any SLAs be breached, consumers and producers receive alerts. Lastly, invalidated Flink Apps review real-time data for potential fixes with a recovery Flink App. This comprehensive pipeline ensures data consistency, validation, and reliability throughout the process, facilitating efficient data analysis and monitoring.

References

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