Big Data Analytics with SQL & Cloud Technologies

22,999.00

Course Overview

This course is designed to provide you with the essential skills required to analyze and process large datasets using SQL and cloud technologies. You’ll learn how to harness the power of cloud platforms like AWS, Google Cloud, and Azure to handle big data workloads efficiently. By the end of the course, you will be proficient in querying and analyzing big data, setting up cloud environments, and optimizing data pipelines for scalable solutions.


Course Content

Module 1: Introduction to Big Data

  • What is big data and its impact on business?
  • Big Data architecture and technologies overview
  • Key differences between traditional databases and big data

Module 2: Big Data Technologies Overview

  • Hadoop ecosystem: HDFS, MapReduce, and YARN
  • Introduction to Spark for large-scale data processing
  • NoSQL vs. SQL databases for big data

Module 3: SQL for Big Data Analytics

  • Advanced SQL queries for big data
  • Aggregation, joins, and subqueries in large datasets
  • Data partitioning, indexing, and optimization techniques

Module 4: Data Processing with Apache Spark

  • Introduction to Apache Spark and its components
  • Setting up Spark clusters
  • Performing data analysis and transformations with PySpark

Module 5: Cloud Technologies for Big Data

  • Introduction to cloud platforms (AWS, Google Cloud, Azure)
  • Storing and querying big data in cloud storage (S3, BigQuery, Data Lake)
  • Scaling big data solutions using cloud services

Module 6: Big Data Analytics Using AWS

  • AWS services for big data analytics (Redshift, EMR, Athena)
  • Running big data jobs with AWS Lambda and AWS Glue
  • Monitoring and optimizing big data processes in AWS

Module 7: Google Cloud Big Data Solutions

  • Using Google BigQuery for big data analytics
  • Managing cloud resources with Google Cloud Platform
  • Optimizing big data queries and storage in GCP

Module 8: Big Data Security & Privacy

  • Ensuring data security in cloud environments
  • Data encryption, authentication, and compliance best practices
  • Best practices for securing big data pipelines

Module 9: Capstone Project & Certification

  • Real-world big data analytics project using SQL & cloud technologies
  • Complete data pipeline deployment in the cloud
  • Project review and certification

Who Should Enroll?

  • Data engineers & data scientists working with large datasets
  • Cloud engineers & professionals looking to implement big data solutions
  • Analysts and business intelligence professionals who want to scale their analytics capabilities

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