What You’ll Learn
- Data Architecture: Data modeling, schema design, data lakes, and data warehouses
- ETL & ELT Pipelines: Building scalable extraction, transformation, and loading processes
- Big Data Frameworks: Apache Spark, Hadoop, Kafka for real-time and batch processing
- Cloud Platforms: AWS (Glue, Redshift), Azure (Data Factory, Synapse), GCP (BigQuery, Dataflow)
- Data Integration: APIs, streaming connectors, and cross-platform data flows
- Data Quality & Governance: Master data management, data cataloging, compliance
- Performance Optimization: Partitioning, indexing, caching, and query tuning
Tools & Technologies Covered:
- Programming & Scripting: Python, SQL, Scala
- Big Data Tools: Apache Spark, Hadoop, Kafka, Flink
- Data Orchestration: Airflow, dbt, Luigi
- Databases: PostgreSQL, MySQL, MongoDB, Cassandra
- Cloud Data Services: AWS Redshift, Azure Synapse, GCP BigQuery
- Data Storage: Amazon S3, Azure Data Lake, Google Cloud Storage
- BI & Visualization: Power BI, Tableau, Looker
Who Should Enroll
- Data Analysts & BI Professionals — Seeking to transition into advanced data engineering roles
- Software Developers — Interested in building large-scale data platforms
- Database Administrators — Wanting to expand into big data & cloud-native solutions
- AI/ML Engineers — Needing strong, reliable data pipelines for model training
- IT Professionals — Looking to upgrade to modern cloud-based data architectures
- Fresh Graduates — Aspiring to launch a career in big data and analytics engineering
Why Choose Our Data Engineering Training
- Hands-on projects with real-world datasets and architectures
- Led by industry experts in big data & cloud engineering
- Practical exercises: ETL pipelines, real-time streaming, data lakehouse builds
- Curriculum aligned with the latest tools & industry practices
- Career guidance with resume and portfolio building
- Placement assistance with top data-driven companies