Data pipelines are the circulatory system of your analytics platform. We engineer pipelines with software engineering discipline — reliable, observable, and maintainable at any scale.
dbt implementation for SQL-based transformation — modular models, testing, documentation, and lineage making your transformation layer trustworthy.
Airflow DAG development for complex pipeline orchestration — dependency management, retry logic, SLA alerting, and dynamic task generation.
PySpark and Spark SQL pipelines for large-scale batch processing — optimized for partition pruning, broadcast joins, and adaptive query execution.
Kafka Streams, Flink, and Spark Structured Streaming for real-time processing — exactly-once semantics, stateful operations, and watermark management.
Automated testing — unit tests for transformations, integration tests for end-to-end flows, and data contract testing for schema compatibility.
Data lineage, SLA monitoring, freshness alerts, and anomaly detection dashboards for full visibility into your pipeline estate.
Our specialists will design a tailored solution for your organization.