From 𝗥𝗲𝗮𝗰𝘁𝗶𝘃𝗲 to 𝗣𝗿𝗼𝗮𝗰𝘁𝗶𝘃𝗲 data engineering : Data Contracts serve the purpose. As data engineers, we must emphasize on how data contracts can make our lives easy. If you want to Prevent pipeline breakdowns by enforcing schema consistency and validation rules or Automate data quality checks through predefined SLAs. The fundamentals of these data contracts helps to Clarify ownership by assigning accountability to producers and building Future-proof architectures with versioned schemas and lineage tracking. If you're curious on how to turn these into real implementation, here's an example: 1. Schema changes → Git approval → Schema Registry 2. Raw data → Kafka → Flink validation 3. Invalid data → Dead Letter Queue (fix later) 4. Valid data → Validated Topic → Object Storage 5. Scheduled validation → Data Warehouse 6. SLA breaches → Automated alerts How can data engineers benefit from this? → Having well-defined Data Contracts cultivate trust between teams avoiding data discrepancies. → With Proactive validation and SLA monitoring you can prevent costly pipeline failures and data quality issues. → Embedding Data Contracts enhances scalability by standardizing data expectations across evolving sources and consumers. If you're looking to master Data Contracts, begin with clear agreements, automate validation, and always evolve your contracts with your data landscape. Share your perspective on Data Contracts. What's your biggest data quality pain point? Thanks a lot Aurimas Griciūnas(SwirlAI) for this amazing holistic picture on Data Contracts. Stay tuned for more on Data Engineering 👉 Pooja Jain #data #engineering #dataquality #bigdata | 37 comments on LinkedIn