Data Versioning and Lineage

One of the challenge is tracking changes in data schemas and datasets over time; and managing data dependencies for reproducibility in ML models. This results in inconsistent or outdated data can break pipelines or lead to inaccurate results.

For Tech leaders, ensuring data lineage visibility—tracking how data evolves across its lifecycle—can be a significant challenge. Without it, debugging issues, maintaining compliance, and ensuring data reliability can become time-consuming.

The Data Engineering platform assists implement tools like Delta Lake or Data Version Control (DVC) to version datasets and manage lineage. It makes data lineage effortless by:

• Visualizing Lineage: Automatically track and document data transformations.

• Ensuring Compliance: Maintain audit-ready records for regulatory requirements.

• Boosting Confidence: Enable teams to trust their data and decisions.

It helps solving the data lineage challenge through no-code engineering.

Managing the changes in data schema through data engineering 

One of the challenge faced by data custodians and managers is managing changes in data schema—a common pain point as data sources evolve and grow in complexity. Without a robust approach, schema drift can lead to pipeline failures, delayed deployments, and disrupted downstream processes.

The Data Engineering platform assists overcome these hurdles by:

• Implementing schema evolution handling: Automatically detect and adapt to schema changes.

• Ensuring backward compatibility: Minimize disruptions to dependent systems.

• Enhancing Data Validation: Proactively catch and address schema mismatches.

It can help ensure seamless schema management and boost pipeline reliability.

Solving Data dependency challenges for reproducibility in ML models

One of the challenge faced by data custodians and managers is ensuring data dependency management for ML models. Poorly managed dependencies can hinder reproducibility, introduce inconsistencies, and delay model deployments.

The Data Engineering platform assists solve these challenges, by:

• Automated Dependency Tracking: Capture data lineage to ensure all transformations are accounted for.

• Version Control for Data: Enable reproducibility by maintaining historical datasets tied to specific model versions.

• Real-Time Monitoring: Detect and resolve data drift that could impact model accuracy.

It can support the organization’s ML initiatives and empower the data science teams.