Data Engineering in the Cloud Big Data Solutions #259019

Upon completion of this course, participants will be able to:

1. Design scalable data architectures for batch and real-time processing in the cloud.

2. Implement data ingestion pipelines using tools like AWS Glue, Azure Data Factory, or Google Cloud Dataflow.

3. Process and transform large datasets with distributed computing frameworks like Apache Spark.

4. Leverage cloud data warehouses (e.g., Snowflake, BigQuery, Redshift) and data lakes (e.g., S3, ADLS).

5. Ensure data quality, governance, and security in cloud environments.

6. Optimize data pipelines for performance, cost, and reliability.

This course is ideal for:

• Data Engineers and Data Architects

• Big Data Developers and ETL Specialists

• Data Scientists seeking to deepen their engineering skills

• Cloud Engineers working with data workloads

• IT Professionals transitioning to data roles

• Analytics and BI Professionals

• Pre-assessment
• Live group instruction
• Use of real-world examples, case studies and exercises
• Interactive participation and discussion
• Power point presentation, LCD and flip chart
• Group activities and tests
• Each participant receives a binder containing a copy of the presentation
• slides and handouts
• Post-assessment

Day 1: Foundations of Cloud Data Engineering

• Module 1: Introduction to Big Data and Cloud Data Engineering

• Module 2: Cloud Data Architecture: Data Lakes, Data Warehouses, and Lakehouses

• Module 3: Overview of Cloud Data Services (AWS, Azure, GCP)

• Hands-On Lab: Setting Up a Cloud Data Lake (e.g., S3, ADLS, GCS)

• Case Study: How Netflix Manages Petabyte-Scale Data

Day 2: Data Ingestion and Integration

• Module 4: Batch vs. Real-Time Data Ingestion Patterns

• Module 5: Data Integration Tools: AWS Glue, Azure Data Factory, Google Cloud Dataflow

• Module 6: Streaming Data with Kafka, Kinesis, or Pub/Sub

• Hands-On Lab: Building a Data Ingestion Pipeline for Batch and Streaming Data

• Workshop: Designing a Multi-Source Data Integration Strategy

Day 3: Data Processing and Transformation

• Module 7: Distributed Processing with Apache Spark on Cloud (e.g., AWS EMR, Databricks)

• Module 8: Data Transformation Techniques: cleansing, enrichment, and aggregation

• Module 9: Serverless Data Processing: AWS Lambda, Azure Functions

• Hands-On Lab: Processing Large Datasets with Spark

• Group Exercise: Optimizing a Data Transformation Workflow

Day 4: Data Storage and Analytics

• Module 10: Cloud Data Warehouses: Snowflake, BigQuery, Redshift

• Module 11: Data Governance and Cataloging (e.g., AWS Glue Data Catalog, Azure Purview)

• Module 12: Analytics and Visualization: Power BI, QuickSight, Looker

• Hands-On Lab: Querying and Analyzing Data in a Cloud Data Warehouse

• Case Study: How Airbnb Uses BigQuery for Analytics

Day 5: Advanced Topics and Capstone Project

• Module 13: Data Pipeline Orchestration: Airflow, Step Functions, Data Fusion

• Module 14: Cost Optimization and Performance Tuning

• Module 15: Data Security and Compliance: Encryption, IAM, GDPR/HIPAA

• Capstone Project: Building an End-to-End Data Pipeline for a Real-World Use Case

• Course Wrap-Up: Q&A, Certification, and Next Steps