Can DataOps help data consumers reveal and take action on powerful product insights hidden in operational data? For many companies, the answer is yes! The emerging practice of DataOps applies Agile development principles and DevOps best practices (e.g. collaboration, automation, monitoring and logging, observability) to data science and engineering, making it faster and easier for organizations to uncover valuable product insights that enable innovation.

Streaming analytics is an invaluable capability for organizations seeking to extract real-time insights from the log data they continuously generate through applications and cloud services. To help our community get started with streaming analytics on AWS, we published a piece last year called An Overview of Streaming Analytics in AWS for Logging Applications, where we covered all the basics.

In the past, querying a database required Structured Query Language (SQL) skills, or knowledge of other database query languages, such as Kibana Query Language (KQL). Today, with the emergence of generative AI (GenAI), teams can query their analytic database using natural language — and get plain English results in return. Or, if you prefer to still use SQL, many teams use GenAI for database query optimization, making queries faster and more efficient.