Learn AWS Analytics with hands-on demos of Athena, Glue, EMR, Redshift, OpenSearch, and QuickSight.
β±οΈ Length: 3.2 total hours
β 4.88/5 rating
π₯ 3,822 students
π October 2025 update
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- Course Overview
- Dive into the expansive world of cloud-native data analytics with ‘AWS Analytics Services – Hands-On Lab Demos’, a concise yet impactful course designed for data professionals eager to master AWS’s robust suite of analytical tools. This course stands out by adopting a highly practical, demo-driven approach, ensuring that learners don’t just understand concepts but actively apply them through direct engagement with the AWS console. Spanning 3.2 total hours, this program is meticulously structured to provide a comprehensive, end-to-end view of data analytics pipelines on AWS, from raw data ingestion and transformation to sophisticated querying and dynamic visualization.
- With an impressive 4.88/5 rating from over 3,822 students, this course’s popularity and effectiveness speak volumes about its quality and relevance. It strategically covers foundational to advanced AWS analytics services, including Athena for serverless querying, Glue for powerful ETL operations, EMR for big data processing, Redshift for petabyte-scale data warehousing, OpenSearch for search and log analytics, and QuickSight for intuitive business intelligence dashboards. The content is rigorously updated, with the latest refresh in October 2025, guaranteeing you learn with the most current features and best practices. This course empowers data engineers, analysts, architects, and developers to confidently build, manage, and optimize scalable analytics solutions on the AWS cloud.
- Requirements / Prerequisites
- Foundational AWS Knowledge: A basic understanding of core AWS services such as IAM (Identity and Access Management), S3 (Simple Storage Service), and EC2 (Elastic Compute Cloud) is highly recommended to effectively navigate the environment and concepts presented.
- Basic Data Concepts: Familiarity with general data principles, including database concepts, data warehousing, and the Extract, Transform, Load (ETL) process, will be beneficial.
- SQL Proficiency: A working knowledge of SQL (Structured Query Language) is crucial, as it is extensively used for querying data with services like Athena, Redshift, and even in some aspects of OpenSearch.
- Active AWS Account: An AWS account (either free tier or with appropriate billing setup) is essential to follow along with the hands-on lab demos and gain practical experience.
- No Prior AWS Analytics Experience Required: While general AWS knowledge helps, specific prior experience with Athena, Glue, EMR, Redshift, OpenSearch, or QuickSight is not a prerequisite, as the course will introduce and guide you through each service.
- Skills Covered / Tools Used
- AWS Athena Mastery: Gain proficiency in executing serverless SQL queries directly on data stored in Amazon S3, leveraging Athena for interactive analysis of data lakes and understanding cost-effective querying strategies.
- AWS Glue for ETL: Develop skills in designing and implementing robust ETL (Extract, Transform, Load) pipelines, configuring Glue Data Catalogs for metadata management, utilizing Glue Crawlers for automated schema discovery, and transforming data using PySpark scripts.
- AWS EMR for Big Data: Learn to provision, configure, and manage scalable Amazon EMR clusters, enabling you to process vast datasets using big data frameworks like Apache Spark and Apache Hive for complex transformations and analytics.
- AWS Redshift Data Warehousing: Acquire expertise in setting up and managing Amazon Redshift, a petabyte-scale cloud data warehouse, optimizing query performance through columnar storage, distribution keys, and efficient SQL practices for analytical workloads.
- AWS OpenSearch Service Integration: Understand how to deploy and manage OpenSearch clusters for real-time search, log analytics, and operational monitoring, including data ingestion, indexing, and visualization with OpenSearch Dashboards.
- AWS QuickSight Business Intelligence: Master the creation of dynamic and interactive business intelligence dashboards and reports using Amazon QuickSight, connecting to diverse data sources (e.g., S3, Redshift, Athena), and crafting insightful visualizations.
- Data Lake Architecture: Learn to conceptualize and implement components of a modern data lake architecture on AWS, integrating various services for a cohesive analytics ecosystem.
- Cloud Analytics Pipeline Development: Develop the ability to connect various AWS analytics services to build complete, end-to-end data processing and analysis workflows.
- Performance Optimization: Gain insights into optimizing the performance and cost-efficiency of AWS analytics services through practical configuration adjustments.
- Benefits / Outcomes
- Hands-On Expertise: You will gain practical, directly applicable experience with the core AWS analytics services, enabling you to confidently implement them in real-world scenarios.
- Architectural Acumen: Develop a solid understanding of how to design and integrate various AWS analytics tools to build comprehensive, scalable, and cost-effective data solutions.
- Enhanced Career Prospects: Acquire highly sought-after skills in cloud analytics and data engineering, boosting your professional value and opening doors to new career opportunities.
- Data-Driven Decision Making: Empower yourself to transform raw data into actionable insights, supporting informed business decisions through robust analysis and visualization.
- Problem-Solving Capability: Be able to identify appropriate AWS analytics services for different data challenges, from big data processing to real-time insights and business intelligence.
- AWS Certification Readiness: Build a strong foundational knowledge that will be highly beneficial for preparing for AWS Data Analytics Specialty and other relevant AWS certification exams.
- PROS
- Highly Practical Approach: The course is built around hands-on lab demos, ensuring immediate application and deeper understanding of each service.
- Comprehensive Service Coverage: Explores a wide and essential array of AWS analytics services, providing a holistic view of the ecosystem.
- Up-to-Date Content: Recently updated in October 2025, guaranteeing relevance with the latest AWS features and best practices.
- High Student Satisfaction: Boasts an excellent 4.88/5 rating from a large base of 3,822 students, indicating proven effectiveness and quality.
- Efficient Learning Curve: Delivers significant knowledge and practical skills within a manageable 3.2-hour timeframe, ideal for busy professionals.
- CONS
- Depth of Coverage: While comprehensive in breadth, the concise nature of the course means certain advanced configurations or niche use cases for individual services might not be explored in extensive detail.
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