A Data Engineering designs, builds, and maintains a company’s data infrastructure, ensuring data is accessible and timely for analysis and decision-making. They focus on creating pipelines that transform raw data into usable formats for data scientists, analysts, and business users. This involves collecting data from various sources, storing it efficiently, and making it readily available for use
Key Features of Course Divine:
Career Opportunities after Data Engineering:
Essential Skills you will Develop
Data Engineering:
Tools Covered:
Syllabus:
Module 1: Introduction to Data Engineering Role of a Data Engineer vs. Data Scientist Overview of Data Ecosystem (OLTP, OLAP) Data Lifecycle & Pipelines
Structured vs Semi-structured vs Unstructured data Batch vs Streaming Data
Module 2: Programming for Data Engineering (Python & SQL) Python basics: data types, control structures, functions Working with files (CSV, JSON, XML) Error handling, logging SQL: DDL, DML, Joins, Aggregations Advanced SQL: Window Functions, CTEs, Indexing
Module 3: Databases and Data Warehousing RDBMS Concepts MySQL/PostgreSQL NoSQL Databases MongoDB, Cassandra Data Modeling Star & Snowflake Schema Data Warehousing (Snowflake, Amazon Redshift, Google BigQuery) Data Partitioning, Indexing, and Optimization.
Module 4: Data Ingestion Tools ETL vs ELT concepts Apache Sqoop, Apache Flume
Batch ingestion using Airflow Real-time ingestion using Apache Kafka APIs & Web Scraping.
Module 5: Data Processing Frameworks Apache Spark: RDDs, Data Frames, Spark for ETL Jobs Spark Streaming vs Structured Streaming Hadoop Ecosystem Overview HDFS, MapReduce, Hive.
Module 6: Workflow Orchestration & Automation Apache Airflow: DAGs, Tasks, Operators Scheduling and Monitoring ETL Pipelines Retry Policies and Alerting
Airflow with Kubernetes & Docker.
Module 7: Cloud Platforms for Data Engineering AWS: S3, EC2, Glue, Lambda, Redshift GCP: BigQuery, Dataflow, Cloud Storage Azure: Data Lake, Data Factory, Synapse Cloud Data Pipeline Use Cases Cost Optimization.
Module 8: Data Lakes & Lakehouse Architecture What is a Data Lake?
Lakehouse Concept (Delta Lake, Apache Hudi, Apache Iceberg) Parquet vs ORC vs Avro Data Governance & Lineage Apache Atlas, AWS Glue Catalog.
Module 9: DevOps, CI/CD & Data Security Version Control with Git & GitHub
Docker for Containerization CI/CD with Jenkins or GitHub Actions Data Encryption, Masking, GDPR Role-Based Access Control (RBAC), IAM Policies.
Module 10: Capstone Project & Interview Preparation Real-world Capstone Project:
Build an end-to-end data pipeline using Spark, Kafka, and Airflow Store data in Data Lake & Warehouse Create BI dashboards (optional: Power BI, Tableau) Resume & Portfolio Building Mock Interviews & Common Questions.
Industry Projects:
Who is this program for?
How To Apply:
Mobile: 9100348679
Email: coursedivine@gmail.com
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