Big Data Analytics in the Cloud (Covers techniques and tools for processing and analyzing large-scale data in the cloud)

None

a) Introduce participants to the concepts and capabilities of big data analytics in the cloud.
b) Provide a comprehensive understanding of cloud-based big data services offered by AWS or Google Cloud.
c) Explore real-world examples and use cases of big data analytics in the cloud using AWS or Google Cloud.
d) Equip participants with the knowledge to leverage cloud-based big data analytics for actionable insights.
e) Address participant questions and concerns related to big data analytics in the cloud using AWS or Google Cloud.

Module 1: Introduction to Big Data Analytics in the Cloud

• Overview of big data analytics and its significance in the era of data-driven decision-making
• Understanding the benefits and challenges of conducting big data analytics in the cloud
• Exploring the role of cloud computing platforms like AWS or Google Cloud in enabling scalable big data analytics

Module 2: Cloud-Based Big Data Services Overview (AWS or Google Cloud)

• Introduction to AWS or Google Cloud big data services and offerings
• Understanding key components such as data storage, processing, and analytics tools
• Exploring cloud-based technologies like Amazon S3, Redshift, Athena, or Google BigQuery

Module 3: Real-World Case Study: Netflix's Data Analytics on AWS

• Analyzing how Netflix leveraged AWS for big data analytics and personalization
• Understanding their data ingestion, processing, and insights generation pipeline
• Lessons learned from Netflix's data-driven approach to content recommendations and customer experience

Module 4: Real-World Case Study: Google Analytics 360 for E-commerce

• Investigating how e-commerce businesses utilize Google Analytics 360 for big data analytics
• Understanding the integration of Google Analytics 360 with other Google Cloud services
• Extracting insights from real-time e-commerce data and enhancing marketing strategies

Module 5: Cloud-Based Data Storage and Management

• Exploring cloud-based data storage options like Amazon S3 or Google Cloud Storage
• Understanding data lake and data warehouse concepts for big data analytics
• Addressing data governance, security, and compliance considerations

Module 6: Cloud-Based Data Processing and Analytics

• Leveraging cloud-based data processing tools like Amazon EMR, AWS Glue, or Google Cloud Dataflow
• Introduction to big data analytics frameworks like Apache Spark or Google Cloud Dataproc
• Real examples of data processing and analytics using AWS or Google Cloud services

Module 7: Advanced Analytics and Machine Learning in the Cloud

• Overview of cloud-based machine learning services like Amazon SageMaker or Google Cloud AI Platform
• Understanding the integration of machine learning models with big data analytics pipelines
• Exploring real-world examples of advanced analytics and predictive modeling in the cloud

Module 8: Data Visualization and Reporting

• Utilizing cloud-based visualization tools like Amazon QuickSight or Google Data Studio
• Creating meaningful dashboards and reports for data-driven decision-making
• Addressing best practices for effective data visualization and storytelling

a) Introduction to big data analytics in the cloud and its significance.
b) Overview of AWS or Google Cloud big data services and offerings.
c) Real-world case studies of big data analytics using AWS or Google Cloud.
d) Cloud-based data storage, processing, analytics, and machine learning capabilities.
e) Data visualization and reporting for effective data-driven decision-making.

Methodology based on Active Learning: 50% minimum practice. Each theoretical point is systematically followed by examples and exercises.

Continuous evaluation and monitoring

Certificate of participation