AWS Data Analytics Specialist Professional Certificaion Course
Course Name - AWS Data Analytics Specialist Professional Certification Course
The only professional certification course you need to learn AWS Data Analytics Specialist.
Course Overview
This course provides a detailed introduction to AWS Data Analytics services, focusing on building, managing, and securing data pipelines. Through hands-on labs and real-world projects, learners gain the skills to design and implement data lakes, process real-time data, optimize data warehousing, and create interactive visualizations. The program prepares participants for the AWS Certified Data Analytics – Specialty certification while equipping them with practical expertise to handle end-to-end data workflows on AWS.
Course Type
Intermediate-level course designed for professionals transitioning into data analytics or enhancing their AWS expertise.
Course Objectives
– Understand the key AWS services for data analytics and their roles in building scalable solutions.
– Build data lakes and optimize them for efficient querying.
– Process real-time data streams and implement data warehouses for analytics.
– Create interactive dashboards to visualize insights and communicate results.
– Ensure data security and governance while managing AWS analytics services.
– Prepare for the AWS Certified Data Analytics – Specialty certification.
What You'll Learn?
– The fundamentals of AWS analytics services, including S3, Glue, Athena, Redshift, Kinesis, and QuickSight.
– How to build, query, and optimize data lakes and warehouses for big data applications.
– Techniques for processing real-time data streams and implementing robust analytics pipelines.
– Best practices for securing and governing data analytics workflows on AWS.
– Hands-on experience through labs and a capstone project to build an end-to-end data analytics solution.
– Certification preparation with mock exams and practice questions.
Duration:
40 Hours
Requirements:
– A computer with a reliable internet connection.
– Access to an AWS account (Free Tier recommended for hands-on labs).
– Familiarity with data concepts such as ETL, data lakes, and dashboards.
Pre-requisite:
– Basic knowledge of AWS services and cloud computing.
– Some experience with data analytics tools or SQL is beneficial but not mandatory.
Target Audience
– Data engineers, data analysts, and IT professionals looking to build expertise in AWS data analytics.
– Cloud architects and developers interested in designing data pipelines and analytics workflows.
– Professionals preparing for the AWS Certified Data Analytics – Specialty certification.
– Organizations and individuals aiming to leverage AWS for big data processing and visualization.
Batch Details
Batch Start Date
Batch Timing
Batch End Date
Batch Days
Curriculum
– Overview of AWS Analytics services
– Understanding data analytics workflows
– Setting up AWS accounts and analytics services
– Introduction to S3 as a data lake foundation
Lab:
– Create an S3 bucket and load sample datasets
– What is a data lake?
– Using AWS Glue for data cataloging and ETL (Extract, Transform, Load)
– Querying data lakes with Amazon Athena
– Partitioning and optimizing data in data lakes
Lab:
– Build a data lake with AWS Glue and Athena
– Introduction to Amazon Redshift
– Setting up a Redshift cluster
– Data ingestion methods (S3 to Redshift)
– Redshift Spectrum for querying data lakes
– Optimizing Redshift performance
Lab:
– Load data into Redshift and perform queries
– Introduction to real-time analytics
– Using Amazon Kinesis Streams and Firehose
– Data transformation with Kinesis Data Analytics
– Real-time use cases (log analysis, clickstream analytics)
Lab:
– Build a real-time streaming pipeline using Kinesis
– Introduction to QuickSight
– Connecting QuickSight to data sources (S3, Redshift, Athena)
– Building dashboards and visualizations
– Sharing insights and embedding dashboards
Lab:
– Create interactive dashboards using QuickSight
– AWS Identity and Access Management (IAM) for analytics services
– Encrypting data at rest and in transit (KMS, S3 encryption)
– Monitoring and auditing with AWS CloudTrail and CloudWatch
– Implementing data governance policies
Lab:
– Secure a data pipeline with IAM and encryption
– Design and implement a complete analytics pipeline:
– Build a data lake in S3
– Process real-time data with Kinesis
– Store processed data in Redshift
– Visualize insights with QuickSight
Deliverables:
– Submit the project for evaluation with a presentation
– Recap of core concepts
– Practice exam questions for AWS Data Analytics Specialty Certification
– Certification exam logistics and tips
Lab:
– Mock exam with hands-on scenarios
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