Learn AWS Analytics with hands-on demos of Athena, Glue, EMR, Redshift, OpenSearch, and QuickSight.
β±οΈ Length: 3.2 total hours
π₯ 74 students
π October 2025 update
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- Course Overview:
- This intensive, hands-on course provides a rapid immersion into the diverse landscape of AWS analytics services, guiding participants from raw data to actionable insights through practical, instructor-led demonstrations.
- Explore the core data lifecycle stages, including ingestion, processing, storage, visualization, and real-time search capabilities within AWS.
- The curriculum extends beyond foundational data querying to encompass robust big data processing with Amazon EMR and real-time operational analytics with Amazon OpenSearch Service, alongside traditional BI tools.
- Gain a holistic understanding of how these powerful, interconnected services form scalable, cost-effective, and performant analytics solutions for diverse business needs.
- Designed to bridge the gap between theoretical knowledge and practical application, ensuring confidence in designing and managing AWS analytics architectures.
- Requirements / Prerequisites:
- A fundamental understanding of cloud computing concepts and basic navigation within the AWS Management Console is recommended.
- Familiarity with SQL basics will be beneficial for querying services like Amazon Athena and Amazon Redshift.
- An active AWS account is advisable for independent practice, understanding potential usage-based charges, often within the AWS Free Tier for initial exploration.
- Reliable internet access and a modern web browser are necessary for participation.
- No prior expert knowledge in analytics or big data technologies is expected; a keen interest in data and a willingness to learn are key.
- Skills Covered / Tools Used:
- Data Lake Foundation: Establishing and leveraging Amazon S3 as a scalable data lake for various analytics workloads.
- Advanced Glue Transformations: Mastering AWS Glue for serverless ETL, including schema inference, data partitioning, and custom PySpark scripting for complex data preparation.
- Interactive Data Querying: Proficiently using Amazon Athena for ad-hoc, serverless SQL queries directly on S3 data, enabling rapid exploratory analysis.
- High-Performance Data Warehousing: Implementing and optimizing Amazon Redshift clusters, focusing on columnar storage, distribution keys, and performance tuning for analytical queries.
- Big Data Processing with EMR: Introductory hands-on experience launching and managing Amazon EMR clusters for large-scale data processing with frameworks like Spark and Hadoop.
- Real-time Search & Analytics with OpenSearch: Deploying and interacting with Amazon OpenSearch Service for indexing, searching, and analyzing semi-structured data for operational dashboards and log analysis.
- Business Intelligence Development: Creating dynamic, interactive dashboards and visual reports using Amazon QuickSight, incorporating advanced calculations and filtering.
- Centralized Metadata Management: Utilizing the AWS Glue Data Catalog for unified data discovery and consistent schema definitions across the analytics ecosystem.
- Performance Optimization: Learning practical techniques to improve query speed and reduce processing costs across different AWS analytics services.
- End-to-End Analytics Workflow: Understanding how to integrate these services into cohesive, scalable, and cost-efficient data analytics pipelines.
- Benefits / Outcomes:
- Architectural Proficiency: Confidently design and implement robust, scalable, and cost-effective end-to-end data analytics solutions on AWS.
- Informed Decision Making: Acquire practical skills to extract meaningful business insights from diverse datasets, driving data-driven strategic decisions.
- Career Enhancement: Strengthen your profile for roles in Data Engineering, Cloud Architecture, Data Analysis, and BI Development with demonstrable AWS analytics expertise.
- Problem-Solving Acumen: Develop the ability to identify common data analytics challenges and effectively apply appropriate AWS services and best practices.
- Cost Efficiency: Gain awareness of cost implications across AWS analytics services and strategies for optimizing expenditure.
- Certification Readiness: Build a strong practical foundation highly relevant for advanced AWS certifications, particularly the AWS Certified Data Analytics – Specialty exam.
- Demonstrable Expertise: Obtain tangible, hands-on experience through lab demos, enhancing marketability for real-world cloud data projects.
- Integrated Data Management: Understand the critical role of a centralized Data Catalog for effective data governance and discovery.
- PROS:
- Comprehensive Service Exposure: Provides extensive hands-on introduction to Athena, Glue, EMR, Redshift, OpenSearch, and QuickSight, offering broad ecosystem understanding.
- Practical, Demo-Centric Learning: Strong emphasis on “Hands-On Lab Demos” ensures practical skill acquisition through real-world implementations.
- Up-to-Date Content: “October 2025 update” guarantees fresh material reflecting the latest AWS analytics features and best practices.
- Efficient Knowledge Transfer: Delivers substantial information and practical insights within a compact 3.2-hour duration, ideal for accelerated learning.
- Versatile Skill Set: Covers diverse analytics capabilities from BI to big data processing and real-time search, catering to various data-centric roles.
- Foundation for Specialization: Excellent starting point for those aspiring to specialize in AWS data analytics, providing a solid broad overview.
- Serverless Advantage Highlighted: Focuses on the benefits and implementation of key serverless analytics options for cost-efficient cloud architectures.
- Instructor-Led Guidance: Benefit from expert guidance through complex setups, enhancing the overall learning experience.
- CONS:
- Introductory Depth: Due to the broad coverage of six major services within a highly condensed 3.2-hour format, the course inherently provides an introductory overview for each, primarily focusing on core functionalities and lab demos, which may not suffice for those requiring in-depth architectural design, advanced optimization, or troubleshooting expertise for individual services.
Learning Tracks: English,IT & Software,Other IT & Software
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