AWS Data Analytics and Data Processing Training Course

Course Introduction

AWS Data Analytics and Data Processing Training Course provides comprehensive practical knowledge for professionals who need to collect, prepare, process, query, analyze, and visualize data using Amazon Web Services. The course explores modern cloud analytics architectures and demonstrates how AWS services can be combined to create scalable, secure, automated, and cost-conscious data processing solutions for organizations of different sizes and industries.

Participants develop a strong understanding of the AWS analytics ecosystem, including Amazon S3, Amazon Athena, AWS Glue, Amazon EMR, Amazon Redshift, Amazon Kinesis, Amazon Data Firehose, and Amazon Quick. These services address different requirements ranging from data lake storage and ad-hoc SQL analysis to distributed processing, streaming ingestion, enterprise data warehousing, and business intelligence.

The training emphasizes practical data processing workflows, including extracting data from diverse sources, cataloging datasets, transforming information, managing schemas, partitioning analytical data, and preparing datasets for downstream analysis. AWS Glue provides capabilities for discovering and integrating data from multiple sources, maintaining centralized metadata, and creating ETL pipelines, while its Data Catalog can be used by services such as Athena and EMR.

Learners also explore Amazon Athena for serverless SQL analysis of structured, semi-structured, and unstructured data stored in Amazon S3. The course explains how Athena works with the AWS Glue Data Catalog, different data formats, partitioning strategies, federated data sources, and analytical querying techniques. This enables participants to perform exploratory and ad-hoc analytics without having to provision traditional query infrastructure.

The course further introduces distributed data processing and real-time analytics using Amazon EMR, Amazon Kinesis Data Streams, Amazon Data Firehose, and related AWS technologies. Participants learn how batch and streaming workloads differ, how processing frameworks such as Apache Spark fit into AWS architectures, and how real-time data can be delivered to analytical destinations. AWS identifies EMR for large-scale distributed processing and Kinesis services for streaming data workloads.

By the end of the course, participants will be able to design and implement end-to-end AWS data analytics workflows that connect storage, ingestion, processing, cataloging, querying, warehousing, visualization, governance, monitoring, and optimization capabilities. The course is designed to build practical cloud data skills while helping learners understand how to select AWS services according to workload characteristics, scalability, latency, integration, automation, and operational requirements.

Duration

5 days

Who Should Attend

• Data analysts responsible for querying, preparing, and interpreting business datasets.

• Data engineers developing cloud-based ingestion, transformation, and processing pipelines.

• Business intelligence professionals working with AWS analytics and visualization services.

• Cloud engineers supporting data platforms and analytical workloads on AWS.

• Database professionals transitioning from traditional platforms to cloud data analytics.

• Data warehouse developers working with Amazon Redshift and related AWS services.

• Analytics engineers designing reusable data transformation and analytical workflows.

• Big data professionals working with Apache Spark, Hadoop, and distributed processing.

• Cloud architects designing modern AWS data lake and analytics architectures.

• DevOps and platform professionals supporting automated data processing environments.

• Technical consultants implementing AWS-based analytics and data modernization projects.

• IT professionals seeking practical knowledge of AWS data analytics technologies.

Course Objectives

• Understand the architecture, capabilities, and use cases of major AWS services used for modern data analytics and large-scale data processing.

• Design practical data workflows that collect, store, transform, process, query, and analyze information across AWS cloud environments.

• Use Amazon S3 as a scalable foundation for analytical data lakes and understand effective organization of datasets for downstream processing.

• Develop SQL-based analytical queries with Amazon Athena and understand how serverless querying can support exploratory and ad-hoc data analysis.

• Use AWS Glue to discover data sources, maintain metadata, create ETL workflows, and prepare datasets for analytics and business intelligence.

• Apply Amazon EMR and Apache Spark concepts to distributed processing workloads involving large datasets and complex analytical transformations.

• Design streaming data workflows using Amazon Kinesis and Amazon Data Firehose for continuous ingestion and delivery of analytical information.

• Understand Amazon Redshift and its role in enterprise data warehousing, complex analytical queries, historical reporting, and structured business analytics.

• Apply security, governance, monitoring, performance, partitioning, and cost optimization practices to improve AWS analytics environments.

• Build and evaluate end-to-end AWS analytics architectures that align data processing technologies with business requirements, scalability, latency, and operational needs.

Comprehensive Course Outline

Module 1: AWS Data Analytics Fundamentals

• Overview of modern cloud analytics architectures and the evolution from traditional data platforms to AWS-based analytical environments.

• Understanding the AWS analytics ecosystem and the roles of storage, processing, streaming, querying, warehousing, and visualization services.

• Exploring common AWS analytics use cases including business intelligence, data lakes, operational analytics, big data processing, and real-time analysis.

• Selecting AWS services according to workload characteristics such as data volume, processing requirements, latency, scalability, integration, and operational complexity.

Module 2: Amazon S3 and Data Lake Foundations

• Designing Amazon S3 storage structures for analytical datasets, raw data, processed information, and curated business-ready data.

• Understanding objects, buckets, prefixes, storage classes, lifecycle policies, permissions, and organization strategies for analytics environments.

• Working with common analytical formats including CSV, JSON, Parquet, and other structured or semi-structured data representations.

• Applying data lake organization, partitioning, naming, security, and lifecycle practices to support efficient downstream analytics.

Module 3: AWS Glue and Data Integration

• Understanding AWS Glue as a serverless data integration service for discovering, preparing, moving, and integrating information from multiple sources.

• Creating and managing AWS Glue Data Catalog databases, tables, schemas, partitions, crawlers, and metadata definitions for analytical datasets.

• Designing ETL workflows that extract data, apply transformations, validate information, and load prepared datasets into analytical destinations.

• Monitoring Glue jobs and workflows while applying reusable data integration patterns for scalable cloud analytics environments.

Module 4: Amazon Athena and SQL Data Analytics

• Using Amazon Athena to perform serverless SQL analysis against structured, semi-structured, and unstructured data stored in Amazon S3.

• Creating databases, tables, schemas, views, and analytical queries using Athena and the AWS Glue Data Catalog.

• Applying filtering, aggregation, joins, window functions, subqueries, and other SQL techniques to investigate large analytical datasets.

• Improving Athena query efficiency through appropriate data formats, partitioning, metadata management, and query design practices.

Module 5: Amazon EMR and Distributed Data Processing

• Understanding Amazon EMR architecture and its role in processing large datasets with distributed frameworks such as Apache Spark and Hadoop.

• Creating processing workflows that use Spark for data transformation, aggregation, exploration, and large-scale analytical workloads.

• Understanding clusters, compute resources, storage integration, processing frameworks, and configuration considerations for EMR environments.

• Integrating EMR workloads with Amazon S3, AWS Glue Data Catalog, Athena, and other AWS services to create connected analytics workflows.

Module 6: Streaming Data and Real-Time Processing

• Understanding real-time analytics requirements and the differences between batch processing, micro-batch processing, and continuous streaming workloads.

• Exploring Amazon Kinesis Data Streams for collecting and processing continuous streams of application, operational, and business data.

• Using Amazon Data Firehose concepts to deliver streaming information to destinations such as Amazon S3, Amazon Redshift, and other supported services.

• Designing real-time processing architectures for use cases such as application monitoring, clickstream analytics, IoT data, and operational intelligence.

Module 7: Amazon Redshift and Data Warehousing

• Understanding Amazon Redshift architecture and its role in enterprise data warehousing and large-scale structured analytical workloads.

• Designing analytical tables, schemas, relationships, loading strategies, and SQL queries for business reporting and historical analysis.

• Comparing data lake querying with Athena against data warehouse workloads supported by Amazon Redshift.

• Applying data warehouse performance concepts including query optimization, distribution considerations, workload management, and analytical modeling.

Module 8: Data Modeling, Cataloging, and Governance

• Developing effective analytical data models that support reliable querying, reporting, transformation, and business intelligence requirements.

• Using AWS Glue Data Catalog to maintain consistent metadata, schemas, data types, locations, and dataset definitions across analytical services.

• Understanding data discovery, cataloging, lineage, access management, and governance requirements for enterprise analytics environments.

• Exploring integration between cataloged datasets, Athena, EMR, Redshift, Lake Formation, and other AWS analytics services.

Module 9: Analytics Visualization, Security, and Optimization

• Connecting analytical datasets with Amazon Quick and other business intelligence tools to develop dashboards, reports, and decision-support outputs.

• Applying AWS identity, access control, encryption, network, and data protection principles to analytical workloads and data processing pipelines.

• Monitoring processing jobs, query workloads, streaming pipelines, storage usage, and service performance to identify operational issues.

• Applying cost and performance optimization strategies based on workload characteristics, processing frequency, data formats, resource utilization, and service selection.

Module 10: End-to-End AWS Data Analytics Solution

• Designing an integrated analytics architecture connecting Amazon S3, AWS Glue, Athena, EMR, Kinesis, Redshift, and visualization capabilities.

• Building an end-to-end workflow that ingests source data, catalogs information, transforms datasets, processes workloads, and produces analytical results.

• Evaluating batch and streaming architecture options according to business requirements, data volumes, latency expectations, scalability, and operational considerations.

• Applying course concepts to realistic enterprise scenarios and developing practical recommendations for implementing or modernizing AWS analytics platforms.

Training Approach

This course will be delivered by our skilled trainers who have vast knowledge and experience as expert professionals in the fields. The course is taught in English and through a mix of theory, practical activities, group discussion and case studies. Course manuals and additional training materials will be provided to the participants upon completion of the training.

Tailor-Made Course

This course can also be tailor-made to meet organization requirement. For further inquiries, please contact us on: Email: training@upskilldevelopment.com Tel: +254 721 331 808

Training Venue 

The training will be held at our Upskill Training Centre. We also offer training for a group (at a discount of 10% to 50%) at requested location all over the world. The Onsite course fee covers the course tuition, training materials, two break refreshments, buffet lunch, airport transfers, Upskill gift package, and guided tour.

Visa application, travel expenses, dinners, accommodation, insurance, and other personal expenses are catered by the participant

Certification

Participants will be issued with Upskill certificate upon completion of this course.

Airport Pickup and Accommodation

Airport pickup and accommodation is arranged upon request. For booking contact our Training Coordinator through Email: training@upskilldevelopment.com, +254 721 331 808

Terms of Payment:

Unless otherwise agreed between the two parties’ payment of the course fee should be done 3 working days before commencement of the training so as to enable us to prepare better.