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High Demand
Master Data Engineering & Pipelines
Build robust data infrastructures, scalable pipelines, and master the modern data stack in 56 intensive sessions.
56 Sessions
Certification
Exemplary Feedbacks
Avg. Salary
$135k+
Pipelines Built
12+
Prerequisites
Python programming proficiency
SQL fundamentals
Basic understanding of databases
What You'll Learn
Design and build scalable ETL/ELT data pipelines
Process big data with Apache Spark & PySpark
Build real-time streaming systems with Kafka
Orchestrate workflows with Apache Airflow
Design data warehouses & data lakes
Deploy pipelines on AWS, GCP & Azure
Learning Roadmap
Python & SQL for Engineers
Session no. 1-8
Data Modeling & Warehousing
Session no. 9-16
ETL & Data Pipelines
Session no. 17-26
Big Data with Spark
Session no. 27-36
Streaming & Real-Time Data
Session no. 37-44
Cloud Data Platforms
Session no. 45-50
Capstone Project
Session no. 51-56
Detailed Syllabus
Module 1: Python & SQL for Engineers
8 sessionsAdvanced Python patternsSQL optimization & indexingDatabase design principlesWorking with ORMs
Module 2: Data Modeling & Warehousing
8 sessionsStar & snowflake schemasDimensional modelingData warehouse designSlowly changing dimensions
Module 3: ETL & Data Pipelines
10 sessionsETL vs ELT patternsApache Airflow orchestrationDAG design & schedulingError handling & monitoring
Module 4: Big Data with Spark
10 sessionsSpark architecture & RDDsPySpark DataFramesSpark SQL & optimizationDelta Lake & data lakehouse
Module 5: Streaming & Real-Time Data
8 sessionsApache Kafka fundamentalsKafka Streams & ConnectSpark Structured StreamingReal-time analytics pipelines
Module 6: Cloud Data Platforms
6 sessionsAWS (S3, Redshift, Glue)GCP (BigQuery, Dataflow)Snowflake & dbtInfrastructure as Code (Terraform)
Module 7: Capstone Project
6 sessionsEnd-to-end data platform designMulti-source ingestion pipelineReal-time + batch processingMonitoring & documentation
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