real-time analytics

On this episode, we dive into Apache Druid 28. This latest Druid release includes improved ANSI SQL and Apache Calcite support, the addition of window functions as an experimental feature, async queries and query from deep storage going GA, array enhancements, multi-topic Apache Kafka ingestion, and so much more! Will Xu, program manager at Imply returns to give us the full scoop.

React fast to changes in data with an automated system of detection and action using Data Activator. Monitor and track changes at a granular level as they happen, instead of at an aggregate level, where important insights may be left in the detail and have already become a problem.

As a domain expert, this provides a no code way to take data, whether real-time streaming from your IoT devices, or batch data collected from your business systems, and dynamically monitor patterns by establishing conditions. When these conditions are met, Data Activator automatically triggers specific actions, such as notifying dedicated teams or initiating system-level remediations. 

Join Will Thompson, Group Product Manager for Data Activator, as he shares how to monitor granular high volume of operational data and translate it into specific actions.

► QUICK LINKS:

00:00 - Monitor and track operational data in real-time

00:53 - Demo: Logistics company use case

02:49 - Add a condition

04:04 - Test actions

04:36 - Batch data

06:21 - Trigger an automated workflow

07:12 - How it works

08:12 - Wrap up

► Link References

Get started at https://aka.ms/dataActivatorPreview

Check out the Data Activator announcement blog at https://aka.ms/dataActivatorBlog

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We’re back again with another Druid release! Here we are at Apache Druid 27.0, thanks to the dedication of the Druid Community. This release was made possible by over 350 commits & 46 contributors. Will Xu, Product Manager at Imply joins the show to discuss new features like Smart Segment Loading, a new mechanism for managing data files as the database scales, improvements to schema auto-discovery, and the long-awaited feature – querying from deep storage!

If you’re a fan of SQL, this episode is for you. The addition of the multi-stage query engine in Apache Druid has enabled SQL-based ingestion. While that’s not something new to the database space, it makes Druid easier to use since more developers know SQL. Sergio Ferragut, Senior Developer Advocate at Imply walks us through using SQL to define and run batch ingestions and how it’s simpler and faster than traditional Druid batch ingestion.

Apache Druid today isn’t the Druid that you’re used to. It’s so much more. The addition of the multi-stage query engine didn’t just change the way Druid handles queries but enabled data and transformation on ingestion and inside of Druid from one table to another using SQL. This has made Druid about 40% faster. But why stop there? Get the inside scoop of what’s coming to Druid this year, from cold tier storage to asynchronous queries and more.

How fast is fast enough? Every hour, every minute, every second? Data has to move fast. Especially with the technology today, you have to be up to date on everything. The market is changing rapidly, so you need to get your analytics in real-time. Once you get to sub-second demands for analytics, that's a different class of solution, especially if you have hundreds or even thousands of analysts querying that data!

Join Eric Kavanagh as he talks to the SVP of Sales at GigaSpaces Technologies, Kevin Mergruen, and VP of Strategy at CelerData, Li Kang. Learn how In-Memory Data Grids work and find out how you can have real-time analytics. Remember, speed, consistency, and availability are mandatory regardless of what business you're in.