AWS Data Analytics- Workshop

skype: constantinestanley

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Introduction

This workshop will delve into the powerful combination of AWS Tools For data analytics. You'll learn how to leverage Pyspark's computing capabilities to process large datasets and integrate them seamlessly with different services.We have 4hr -16hr workshops.

Module 1: Introduction to AWS Data Analytics

What is Data Analytics?

Definition, importance, and real-world applications

AWS for Data Analytics

Overview of AWS services for data analytics

Core services: S3, EC2, EMR, Redshift, Glue, Athena, SageMaker

Module 2: Data Ingestion and Storage

AWS S3

Object storage service for storing and retrieving data

Creating buckets, uploading data, and accessing data

AWS Glue Data Catalog

Central repository for data assets

Creating data catalogs and defining schemas

Module 3: Data Processing and Transformation

AWS Glue ETL

Serverless ETL service for extracting, transforming, and loading data

Creating ETL jobs using the visual interface or Python scripts

AWS EMR

Managed Hadoop framework for big data processing

Launching clusters, running Spark and Hive jobs

AWS Athena

Serverless interactive query service for analyzing data in S3

Writing SQL queries to analyze data

Module 4: Data Warehousing and Business Intelligence

AWS Redshift

Fully managed data warehouse service

Creating clusters, loading data, and running SQL queries

Amazon QuickSight

Business intelligence service for creating visualizations and dashboards

Building interactive dashboards and sharing insights

Module 5: Machine Learning and Predictive Analytics

AWS SageMaker

Fully managed platform for machine learning

Building, training, and deploying machine learning models

Using pre-trained models and custom models

Amazon Forecast

Fully managed service for forecasting

Creating forecasting models and generating predictions

Module 6: Security and Best Practices

AWS Security Best Practices for Data Analytics

Data encryption, access control, and network security

Compliance and regulatory requirements

Cost Optimization

Analyzing cost reports and identifying optimization opportunities

Using cost-saving strategies

Hands-on Exercises:

Setting up an AWS account

Creating S3 buckets and uploading data

Using AWS Glue to create ETL jobs

Running Spark jobs on EMR

Analyzing data with Athena

Creating a data warehouse on Redshift

Building dashboards with QuickSight

Training and deploying machine learning models with SageMaker

Forecasting future trends with Amazon Forecast

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