Month 14: Cloud Data Analytics and Big Data

Week 1: Introduction to Big Data and Analytics Concepts

Day 1: Understanding Big Data: characteristics, sources, and challenges

Day 2: Introduction to data analytics and its importance

Day 3: Types of analytics: descriptive, diagnostic, predictive, and prescriptive

Day 4: Data warehousing and data lakes

Day 5: Hands-on activity: Exploring popular Big Data and analytics tools and libraries

Week 2: Data Processing Frameworks – Apache Hadoop and Spark

Day 1: Introduction to Apache Hadoop and its ecosystem

Day 2: Hands-on activity: Setting up and working with a Hadoop cluster

Day 3: Introduction to Apache Spark and its components

Day 4: Hands-on activity: Running Spark jobs for data processing

Day 5: Hadoop vs. Spark: similarities, differences, and use cases

Week 3: AWS Big Data and Analytics Services

Day 1: Introduction to AWS EMR (Elastic MapReduce)

Day 2: Hands-on activity: Running Hadoop and Spark jobs with EMR

Day 3: Introduction to AWS Kinesis

Day 4: Hands-on activity: Building real-time data processing with Kinesis

Day 5: Introduction to AWS Glue

Day 6: Hands-on activity: Creating ETL jobs with Glue

Week 4: Azure Big Data and Analytics Services

Day 1: Introduction to Azure HDInsight

Day 2: Hands-on activity: Running Hadoop and Spark jobs with HDInsight

Day 3: Introduction to Azure Stream Analytics

Day 4: Hands-on activity: Building real-time data processing with Stream Analytics

Day 5: Introduction to Azure Data Factory

Day 6: Hands-on activity: Creating ETL jobs with Data Factory

Week 5: GCP Big Data and Analytics Services

Day 1: Introduction to GCP DataProc

Day 2: Hands-on activity: Running Hadoop and Spark jobs with DataProc

Day 3: Introduction to GCP DataFlow

Day 4: Hands-on activity: Building real-time and batch data processing with DataFlow

Day 5: Introduction to GCP DataFusion

Day 6: Hands-on activity: Creating ETL jobs with DataFusion