Build reliable, observable, and automated data pipelines — master DataOps practices with expert-led hands-on training.
Our DataOps training courses teach data engineers, analysts, and platform teams how to apply DevOps principles to data workflows. You will learn to build reliable, testable, and observable data pipelines using Apache Airflow, dbt, Kafka, and Spark — embedding data quality, versioning, and monitoring from day one.
All courses are delivered by certified data engineers with real-world experience in financial services, e-commerce, and cloud-native data platforms. Choose from cohort-based live training or dedicated 1-to-1 mentoring.
Apache Airflow DAG design, scheduling, error handling, and production-grade pipeline management.
Apache Kafka, Confluent Platform, and Kinesis for event-driven data ingestion and processing.
SQL-based transformations, dbt testing, documentation, and integration with cloud data warehouses.
Great Expectations, dbt tests, and data contracts for ensuring pipeline correctness and reliability.
DVC, Delta Lake, Apache Iceberg, and lineage tracking with OpenLineage and Marquez.
Monitoring pipeline health, data freshness, schema drift, and volume anomalies with Monte Carlo.
Build and debug real data pipelines in live Airflow, Kafka, and Spark environments.
Expert-led sessions with real-time Q&A from certified data engineers.
Data engineering experts available around the clock to answer questions.
Courses aligned to Databricks, dbt Analytics Engineering, and cloud data certification objectives.
Three ways to learn — from free self-service to dedicated 1-to-1 instruction.
Self-service practice tests to assess your knowledge and prepare for certification. No sign-up required.
Instructor-led live sessions with cohort peers, hands-on labs, real-time Q&A, and exam preparation.
Fully personalised training delivered by a senior engineer, exclusively for you at your pace and schedule.
Join 1800+ data engineers who have mastered DataOps practices with our expert-led programs.