Since our Real time for the bottom line webinar series was such a success, we decided to bring it back in smaller, recurring installments. Starting Thursday, 1/26, we’ll alternate streaming ingestion, streaming analytics, and more technical tutorials, so that you can pick the timing that works best for you.
Streaming ingestion and continuous ETL
January 26th | February 16th | March 9th
Let’s say you DO produce a lot of streaming data, so you know intelligence is to be had. But if you can’t capture it all, and direct it to where it’s needed in real time, how can the picture be 100% complete?
We maintain that Big Data processing and traditional ETL solutions are too slow, too complicated, and too conditional to support real-time results. And simply put, without streaming ingestion, there’s no streaming analytics, no action, and no results.
No streaming analytics? Sorry, no action for you
February 2nd | February 23rd | March 16th
Let’s say you CAN capture all the streaming data you produce, and you CAN integrate it with your stored data in one architecture. But if you can’t analyze it continuously and in real time, how can the results be 100% reliable?
The batch-oriented, collect-store-contemplate model employed by Big Data Analytics technologies is incomplete because it does not make use of live data in real time. At the same time, most Fast Data technologies don’t integrate with stored data- so they’re missing the historical context to their insight.
A SQL architecture for streaming
February 9th | March 2nd | March 23rd
We’re often asked why we chose to build a standard-compliant platform (over 2M lines of code, and growing). Here’s our answer:
1. SQL performs beautifully for both scale-up and scale-out implementations.
2. SQL is the only language that can seamlessly integrate streaming and stored data for streaming analytics.
3. Streaming technologies, DBMSs, and Hadoop are friends, not foes.
In this webinar, we’ll demo a wide range of operations ran on our 100% SQL-compliant streaming analytics architecture.