- Rapidly Build and Run Apache Spark Applications in the Cloud with StreamAnalytix on AWS Marketplace - Mar 1, 2019.
StreamAnalytix is an Apache Spark based big data analytics and machine learning platform. It offers an intuitive visual development environment to rapidly build and operationalize batch + streaming applications, across industries, data formats, and use cases.
- Top Considerations for Selecting a Real-time Streaming Analytics Platform - Aug 28, 2018.
Information on how to download this whitepaper, which provides a view into how streaming data analytics is different from traditional analytics and thus have unique data processing needs that translate into absolute must-haves for the streaming analytics platform.
- Key Takeaways from the Strata San Jose 2018 - Jul 16, 2018.
By dropping 'Hadoop' from its name, the @strataconf 2018 in San Jose signaled the emphasis on machine learning, cloud, streaming and real-time applications.
- Introducing WSO2 Stream Processor - Jun 25, 2018.
WSO2 Stream Processor is an open source, lightweight, Streaming SQL based platform that enables you to do running aggregations, to detect patterns, and to generate alerts on data streams in real-time.
- 5 Key Takeaways from Strata London 2018 - Jun 19, 2018.
5 highlights and thoughts from my attendance to Strata London 2018.
- Event Processing: Three Important Open Problems - May 28, 2018.
This article summarizes the three most important problems to be solved in event processing. The facts in this article are supported by a recent survey and an analysis conducted on the industry trends.
- Build, Test and Run Spark Applications at No Cost with StreamAnalytix Visual Spark Studio - Oct 25, 2017.
Experience the Ease and Speed of Building Spark Application on Your Desktop. Free to download and use!
- Apache Flink: The Next Distributed Data Processing Revolution? - Jul 5, 2017.
Will Apache Flink displace Apache Spark as the new champion of Big Data Processing? We compare Spark and Apache Flink performance for batch processing and stream processing.
- Streaming vs Batch Analytics, Model Creation and Deployment – April 19 Webinar - Apr 13, 2017.
Catch this live webinar from Open Data, which will explain both streaming and batch analytic types, typical use cases for each, as well as the best way to deploy these analytics in production. It happens April 19th, 2017 at 10am PST (1pm EST).
- Spark Streaming Innovation Contest - Feb 15, 2017.
Build a Spark application on StreamAnalytix, a real-time streaming analytics platform and win $10K. Register by March 31, 2017.
- Beginner’s Guide to Apache Flink – 12 Key Terms, Explained - Oct 4, 2016.
We review 12 core Apache Flink concepts, to better understand what it does and how it works, including streaming engine terminology.
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- Analyzing Log Data with Spark and Looker - May 31, 2016.
Learn how to set up a modern pipeline that collects, processes, and analyzes high-volume, machine-generated data. This on-demand webinar discusses popular collection mechanisms, does a hands-on log-parsing example in Spark, and shows how to use Looker to get insights from event data.
- Analyzing Log Data with Spark & Looker, Webinar May 18 - Apr 22, 2016.
Learn how to set up a modern pipeline that collects, processes, and analyzes high-volume, machine-generated data. We’ll talk about popular collection mechanisms, do a hands-on log-parsing example in Spark, and discuss how to use Looker to get insights from event data.
- 7 Essential Elements in a Real-time Streaming Analytics Platform - Jan 19, 2016.
Download the white paper to learn about what to look for in a Big Data real-time streaming analytics (RTSA) platform.
- FICO Chief Analytics Officer 2016 Predictions - Jan 5, 2016.
NASA Juno mission will arrive at Jupiter. The Summer Olympics will take place in Rio de Janeiro. The US will have a presidential election. And prescriptive analytics will take center stage as the ultimate destination on the analytics journey.
- A Future-proof Architecture for Streaming Data Analytics - Dec 2, 2015.
Get this white paper to explore future- proof strategies to leverage the steady flow of new, advanced real-time streaming analytics (RTSA) application development technologies.
- Getting started with Python and Apache Flink - Nov 13, 2015.
Apache Flink built on top of the distributed streaming dataflow architecture, which helps to crunch massive velocity and volume data sets. With version 1.0 it provided python API, learn how to write a simple Flink application in python.
- Fast Big Data: Apache Flink vs Apache Spark for Streaming Data - Nov 10, 2015.
Real-time stream processing has been gaining momentum in recent past, and major tools which are enabling it are Apache Spark and Apache Flink. Learn with the help of a case study about Data processing, Data Flow, Data management using these tools.
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- Spark SQL for Real Time Analytics – Part Two - Sep 22, 2015.
Apache Spark is the hottest topic in Big Data. Part 2 of this covers basic concepts of Stream Processing for Real Time Analytics and for the next frontier – Internet of Things (IoT).
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- Build Real-time Streaming Apps in Minutes with Free Versions of StreamAnalytix - Sep 8, 2015.
Check free and trial Versions of StreamAnalytix, enterprise-class streaming analytics platform, which enable acceleration of real-time analytics.
- Apache Flink and the case for stream processing - Aug 7, 2015.
Realtime analytics have been proven challenging in the past, but with new tools it will be possible to setup your pipelines in relative short time. Apache Flink is one of such framework, find out how you can exploit it for your demands.
- Patterns for Streaming Realtime Analytics - Aug 5, 2015.
Design patterns are well-known for solving the recurrent problems in software engineering, on similar lines we can have Streaming Realtime Analytics patterns and avoid reinventing the wheel. Here, you can see the major patterns we found out for it.
- Getting Business Value from Real-time Streaming Analytics - Jun 29, 2015.
Get the white paper to learn how real-time streaming analytics can add value to business, including what is real-time streaming analytics, its characteristics, best ways to use it and implement it.
- KDnuggets™ News 15:n09, Mar 25: Deep Learning from Scratch; 10 steps to Kaggle Success; US CDS DJ Patil Cartoon - Mar 25, 2015.
Deep Learning for Text Understanding from Scratch; New Poll: Computing platform; 10 Steps to Success in Kaggle Data; Cartoon: US Chief Data Scientist Most Difficult Challenge; SQL-like Query Language for Real-time Streaming Analytics.
- SQL-like Query Language for Real-time Streaming Analytics - Mar 12, 2015.
We need SQL like query language for Realtime Streaming Analytics to be expressive, short, fast, define core operations that cover 90% of problems, and to be easy to follow and learn.