Big Data on AWS

1. Scalable Infrastructure: AWS offers scalable infrastructure and services to handle large data volumes.
2. Storage Options: Amazon S3, EBS, and Glacier provide diverse storage solutions for different data types.
3. Data Processing: AWS EMR facilitates processing of large datasets using Hadoop and Spark.
4. Analytics Tools: Amazon Athena, Kinesis, QuickSight, and Elasticsearch Service support real-time analytics and data visualization.

Before learning Big Data on AWS, it's beneficial to have the following skills:

1. Fundamental Cloud Knowledge: Understand cloud computing concepts, AWS services, and how they are used to build scalable solutions.

2. Data Management: Familiarity with data management concepts including storage, processing, and analysis.

3. Programming Skills: Proficiency in programming languages such as Python, Java, or Scala, used for data manipulation, scripting, and development.

4. Database Knowledge: Understanding of databases and data warehousing concepts, including relational databases like MySQL, PostgreSQL, or NoSQL databases like MongoDB.

By learning Big Data on AWS, you gain the following skills:

1. AWS Services Mastery: Proficiency in utilizing AWS services such as Amazon S3, Amazon EMR, Amazon Redshift, Amazon DynamoDB, Amazon Athena, and more for storing, processing, analyzing, and visualizing large datasets.

2. Big Data Analytics: Understanding of big data analytics concepts and techniques including data ingestion, transformation, processing, querying, and visualization.

3. Data Engineering: Ability to design and implement data pipelines for ingesting, processing, and transforming large datasets using AWS services and tools.

4. Data Warehousing: Knowledge of building and managing data warehouses on AWS, including schema design, data modeling, and optimization for analytical queries.