Analytics & BI Services
1. Analytics & BI Services
Let’s continue our data journey with AWS analytics and BI services.2. Introduction to data analytics
Data analytics involves collecting, processing, and transforming data to gain insights. In today’s dynamic environment, it’s an iterative process that supports continuous improvement.3. Data analytics in AWS
In this video, we’ll explore six key AWS services for data analytics: Athena, QuickSight, Kinesis, Redshift, Macie, and Glue. Let’s dive into each service and its use cases.4. Amazon Athena
Amazon Athena is a serverless analytics service that enables petabyte-scale data analysis directly from the storage location. It offers data integration across AWS, other clouds, and on-premises sources, with a simple pay-per-query model. Athena also supports SQL-powered machine learning and integrates with QuickSight for data visualization.5. Amazon QuickSight
Amazon QuickSight is a managed BI service that builds interactive visualizations from various data sources.6. Amazon QuickSight
It’s serverless, automatically scales,7. Amazon QuickSight
and includes generative BI, allowing users to input natural language questions to generate visual answers.8. Amazon QuickSight
It also supports paginated reports for offline sharing.9. Amazon Kinesis
Kinesis is an AWS service for real-time data stream analysis. It processes data as it flows in, making it ideal for live leaderboards,10. Amazon Kinesis
IoT sensor data,11. Amazon Kinesis
and speeding up traditional batch processing. Kinesis is serverless, ensuring efficient scaling with low latencies.12. Amazon Redshift
Amazon Redshift is an AI-powered data warehousing service with massively parallel processing. It delivers high performance at lower costs, supports zero-ETL for unified data integration, and enables secure, parallel project collaboration. Redshift excels in handling large datasets and advanced analytics.13. Securing data with Amazon Macie
Amazon Macie is an ML-driven data security service that automatically discovers and protects sensitive data in S3, making it ideal for storing PII.14. Securing data with Amazon Macie
It ensures compliance and security,15. Securing data with Amazon Macie
and can assess data migration risks before moving data.16. AWS Glue
Lastly, AWS Glue is a data integration service that unifies data across AWS, supporting both batch and streaming data.17. AWS Glue
It's ideal for preparing data for advanced analytics tasks like machine learning.18. AWS Glue
It’s serverless, scales to petabyte levels,19. AWS Glue
and simplifies ETL pipeline development by automatically provisioning and integrating data.20. Creating an end-to-end data workflow
Now, let’s apply these services in a sample data analytics project. Imagine analyzing usage data from a mobile app:21. Creating an end-to-end data workflow
We would use Amazon Kinesis to ingest real-time data coming in from the mobile app and store it in S3.22. Creating an end-to-end data workflow
A function in AWS Glue can then be used to transform the data and move it to Amazon Redshift and Amazon Athena.23. Creating an end-to-end data workflow
Amazon Macie will be set up across S3, Redshift, and Athena to monitor for any sensitive data being flown into the systems.24. Creating an end-to-end data workflow
The data in Redshift can be used by a downstream machine learning process for advanced analytics.25. Creating an end-to-end data workflow
And QuickSight can read data from Athena and build interactive reports to present the analysis to others.26. Let's practice!
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