Big Data Integration and ETL
( 25 Modules )

Module #1
Introduction to Big Data
Overview of big data, its characteristics, and importance

Module #2
Big Data Integration Challenges
Understanding the challenges of integrating big data from various sources

Module #3
ETL Fundamentals
Introduction to Extract, Transform, and Load (ETL) and its importance in big data integration

Module #4
ETL Tools Overview
Overview of popular ETL tools such as Informatica, Talend, and Microsoft SSIS

Module #5
Data Ingestion Techniques
Methods for ingesting data from various sources, including batch and real-time ingestion

Module #6
Data Quality and Profiling
Importance of data quality and profiling in big data integration

Module #7
Data Transformation Techniques
Methods for transforming data, including data aggregation, filtering, and sorting

Module #8
Data Mapping and Schema Design
Designing schemas and mapping data from source to target systems

Module #9
Handling Large Datasets
Techniques for handling large datasets, including data partitioning and parallel processing

Module #10
Big Data Storage Options
Overview of big data storage options, including HDFS, NoSQL, and cloud storage

Module #11
Hadoop and HDFS
In-depth look at Hadoop and HDFS, including data storage and processing

Module #12
NoSQL Databases
Overview of NoSQL databases, including MongoDB, Cassandra, and Couchbase

Module #13
Cloud Storage Options
Overview of cloud storage options, including Amazon S3, Azure Blob Storage, and Google Cloud Storage

Module #14
Data Warehousing and MPP
Introduction to data warehousing and massively parallel processing (MPP)

Module #15
ETL for Big Data
Using ETL tools for big data integration, including Informatica, Talend, and Microsoft SSIS

Module #16
Real-time Data Integration
Real-time data integration using streaming technologies like Apache Kafka and Apache NiFi

Module #17
Big Data Integration Security
Securing big data integration pipelines, including data encryption and access control

Module #18
Big Data Governance
Importance of big data governance, including data quality, security, and compliance

Module #19
Data Lineage and Provenance
Tracking data lineage and provenance in big data integration

Module #20
ETL Testing and Quality Assurance
Testing and quality assurance of ETL pipelines

Module #21
Big Data Integration Best Practices
Best practices for big data integration, including design patterns and architecture

Module #22
Case Studies in Big Data Integration
Real-world case studies in big data integration

Module #23
Future of Big Data Integration
Emerging trends and technologies in big data integration

Module #24
Hands-on Lab:ETL with Informatica
Hands-on lab experience with ETL using Informatica

Module #25
Course Wrap-Up & Conclusion
Planning next steps in Big Data Integration and ETL career

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Big Data Integration and ETL ( 25 Modules )

Big Data Integration and ETL
( 25 Modules )