Data Engineering Training
& Data Platform Programs

Pipeline Architecture Complexity

Designing reliable data pipelines that handle ingestion, transformation, quality validation, and delivery at enterprise scale requires deep expertise in distributed systems and data architecture patterns.

Real-Time Streaming Challenges

Building low-latency streaming pipelines with Kafka, Flink, and Spark Streaming for real-time analytics, event processing, and AI feature engineering requires specialized streaming expertise.

Data Quality & Governance

Ensuring data quality, lineage tracking, schema evolution, and governance compliance across complex multi-source data pipelines requires systematic data engineering practices and tooling.

Multi-Cloud Data Integration

Integrating data across AWS, Azure, GCP, and OCI with consistent governance, security, and performance requires multi-cloud data engineering expertise beyond single-platform skills.

Data Lake & Warehouse Design

Designing scalable data lakes and warehouses with proper partitioning, indexing, format selection (Parquet, Delta, Iceberg), and query optimization requires advanced data architecture knowledge.

AI/ML Data Infrastructure

Building data infrastructure for AI and ML workloads — feature stores, training data pipelines, model serving data, and MLOps data flows — requires specialized AI data engineering skills.

ETL & ELT Pipelines

Master ETL and ELT pipeline design using Apache Spark, dbt, Airflow, and cloud-native services for batch data processing, transformation, and delivery at enterprise scale.

Streaming Data Engineering

Build real-time streaming pipelines with Apache Kafka, Apache Flink, Spark Streaming, and cloud streaming services for event-driven architectures and real-time analytics.

Data Lakes & Lakehouses

Design and implement data lakes and lakehouses using Delta Lake, Apache Iceberg, and Apache Hudi on AWS S3, Azure ADLS, GCP GCS, and OCI Object Storage with ACID transactions.

Data Warehousing

Implement cloud data warehouses with Snowflake, BigQuery, Redshift, and Synapse Analytics covering dimensional modeling, query optimization, and cost management.

Data Quality & Governance

Implement data quality frameworks with Great Expectations, data lineage with OpenLineage, data cataloging with Apache Atlas, and governance with Unity Catalog.

AI/ML Data Pipelines

Build data infrastructure for AI and ML including feature stores (Feast, Tecton), training data pipelines, vector embedding pipelines, and MLOps data flows.

Data Orchestration

Orchestrate complex data workflows with Apache Airflow, Prefect, and Dagster covering DAG design, scheduling, dependency management, and monitoring for production data pipelines.

Cloud-Native Data Engineering

Implement cloud-native data engineering on AWS (Glue, EMR, Kinesis), Azure (Data Factory, Synapse), GCP (Dataflow, Dataproc), and OCI (Data Integration, Data Flow).

Design and implement production-grade ETL/ELT pipelines with Apache Spark, dbt, and Airflow for batch data processing, transformation, and delivery at enterprise scale.

Implement Kafka-based streaming architectures, build Flink and Spark Streaming jobs, and design event-driven data pipelines for real-time analytics and AI feature engineering.

Design and build data lakehouses with Delta Lake or Apache Iceberg on cloud object storage, implementing ACID transactions, time travel, and schema evolution for reliable analytics.

Implement and optimize Snowflake, BigQuery, or Redshift data warehouses with dimensional modeling, query optimization, clustering, and cost governance for enterprise analytics.

Build data quality frameworks with automated testing, implement data lineage tracking, configure data catalogs, and establish governance policies for enterprise data compliance.

Design feature stores, build training data pipelines, implement vector embedding pipelines, and create MLOps data flows for enterprise AI and machine learning workloads.

Data Engineers

Data engineers building pipelines, data lakes, and warehouses who need structured training in modern data engineering tools, patterns, and cloud-native data infrastructure.

Software Engineers

Software engineers transitioning to data engineering who need expertise in distributed data processing, streaming architectures, and cloud data platform engineering.

Data Architects

Data architects designing enterprise data platforms who need hands-on expertise in lakehouse architecture, data mesh patterns, and multi-cloud data integration.

Analytics Engineers

Analytics engineers using dbt and SQL transformations who need broader data engineering skills in pipeline orchestration, streaming, and data infrastructure.

ML Engineers

ML engineers building AI data infrastructure who need data engineering expertise for feature stores, training data pipelines, and MLOps data flows.

Data Platform Teams

Platform teams building enterprise data infrastructure who need comprehensive data engineering training covering orchestration, quality, governance, and cloud-native tooling.

• Data Pipeline Patterns
• ETL vs ELT
• Batch vs Streaming
• Data Modeling
• Storage Formats

• Spark Architecture
• DataFrames & Datasets
• Spark SQL
• Performance Tuning
• Spark on Kubernetes

• Kafka Architecture
• Kafka Streams
• Apache Flink
• Spark Streaming
• Event-Driven Patterns

• Delta Lake
• Apache Iceberg
• Apache Hudi
• ACID Transactions
• Time Travel

• Snowflake
• BigQuery
• Redshift
• Dimensional Modeling
• Query Optimization

• DAG Design
• Airflow Operators
• Scheduling
• Dependencies
• Monitoring

• Great Expectations
• Data Lineage
• Data Catalog
• Unity Catalog
• Compliance

• Feature Stores
• Training Pipelines
• Vector Pipelines
• MLOps Data Flows
• dbt for ML

Data Engineering Fundamentals

3-day foundational training covering ETL/ELT, Spark, Kafka, data lakes, and cloud data warehousing with hands-on pipeline labs.

Streaming Data Engineering

Focused 2-day streaming workshop covering Kafka architecture, Flink, Spark Streaming, and real-time pipeline design with hands-on labs.

Enterprise Data Platform

Comprehensive 5-day program covering the full data engineering stack from pipelines through lakehouse, warehousing, governance, and AI data infrastructure.

Corporate Data Engineering

Customized data engineering training aligned to your cloud platform, data stack, and organizational data maturity level.

Build a production Spark ETL pipeline reading from multiple sources, applying complex transformations, implementing data quality checks, and writing to a Delta Lake table.

Implement a real-time streaming pipeline with Kafka producers, Kafka Streams processing, and a consumer writing enriched events to a data lake for real-time analytics.

Build a Delta Lake lakehouse with bronze/silver/gold medallion architecture, implementing ACID transactions, schema evolution, time travel, and Z-order optimization.

Implement a dbt project with staging, intermediate, and mart models, add data tests, configure documentation, and deploy to Snowflake or BigQuery with CI/CD integration.

Design and deploy an Airflow DAG orchestrating a multi-step data pipeline with sensors, operators, XComs, error handling, and Slack alerting for production monitoring.

Implement Great Expectations data quality checks, configure OpenLineage for lineage tracking, and set up a data catalog with automated metadata discovery.

Databricks Certified Data Engineer Associate

Databricks certification covering Delta Lake, Spark, and lakehouse data engineering.

AWS Certified Data Analytics Specialty

AWS data analytics certification covering Kinesis, Glue, EMR, Redshift, and Athena.

Google Professional Data Engineer

Google Cloud data engineering certification covering Dataflow, BigQuery, and Pub/Sub.

Snowflake SnowPro Core

Snowflake certification covering data warehousing, data sharing, and Snowflake architecture.