Title

AWS re:Invent 2022 - Build interactive analytics applications (ANT209)

Summary

• Raj Devnath, a product manager for Amazon Athena, and Ravi, an engineer from FINRA, discuss building interactive analytics applications using Amazon Athena and the newly announced support for Apache Spark.
• Athena's integration with Apache Spark allows for powerful interactive analytics, maintaining serverless and fully managed experience.
• The motivation behind this feature is to handle modern applications that generate large amounts of data, which require interactive queries and complex analytics beyond the capabilities of SQL.
• Python is identified as a complementary tool to SQL for complex analytics due to its expressivity and extensive library ecosystem.
• Athena for Apache Spark offers instant start applications, auto-scaling, optimized runtime, and a simplified notebook experience on the console.
• Users can focus on business applications without worrying about hardware and software infrastructure, and they only pay for the compute they use.
• Athena for Spark supports open data formats and works seamlessly with AWS Glue, allowing users to work with data where it lives.
• Example use cases include interactive data exploration and integration with front-end business applications.
• Ravi from FINRA shares their use case, highlighting the benefits of Athena for Apache Spark in providing faster analysis, better visualization, and empowering users to perform analytics independently.
• FINRA's journey and future plans with Athena for Apache Spark are discussed, emphasizing the importance of security, data access, and CI/CD pipeline enrichment.

Insights

• The integration of Apache Spark with Amazon Athena represents a significant advancement in AWS's analytics offerings, providing users with a more powerful and flexible toolset for interactive analytics.
• Athena's serverless approach to Apache Spark workloads can lead to cost savings and operational efficiencies, as it eliminates the need for provisioning and managing clusters.
• The instant start and auto-scaling features of Athena for Apache Spark can significantly reduce the time to insight for users, which is critical in fast-paced environments like financial market surveillance.
• Athena for Apache Spark's support for Python and its libraries opens up possibilities for more complex data analysis and machine learning applications, which can be seamlessly integrated into the analytics workflow.
• The pay-as-you-go pricing model for Athena for Apache Spark aligns with AWS's overall pricing philosophy and can be more cost-effective for users with variable analytics workloads.
• FINRA's use case demonstrates the practical application of Athena for Apache Spark in a regulatory environment, showcasing the potential for improved data analysis, fraud detection, and market integrity.
• The session highlights the ongoing need for organizations to provide easy-to-use, scalable, and secure analytics tools to their users, which Athena for Apache Spark aims to address.