Apache Storm Made Easy
2015 is witnessing many enterprises wanting to speed up initiatives for turning Big Data into Business Insights, and early starters are going beyond Hadoop with real-time streaming, in-memory computing, advance analytics and machine learning in their architecture. Companies will want to design, iterate and rapidly deploy new and emerging use cases with Fast data – like Internet of Things and Real-time Customer Engagement Analytics.
Having to learn and code native Apache Storm or Spark-streaming would make things slow. There is a need for platforms that abstract these powerful streaming engines and provide an easy and powerful visual IDE-like tool for real-time streaming analytics application development.
In this webinar, we will discuss a Modern Enterprise Data Architecture to serve customers in real-time or near-real-time in a context-sensitive manner enabled by a powerful blend of both offline and real-time Analytics.
During this webinar you will get-
- What distinguishes a Modern Agile Enterprise
- Lambda Architecture overview
- An overview of a powerful real-time streaming analytics platform for rapid app development on Apache Storm
- A demo of a practical implementation of Lambda architecture in Social Media Analytics involving batch and real-time analysis
Anand is a techno-business leader at Impetus Technologies providing product strategy, product marketing and sales leadership for the StreamAnalytix business at Impetus. He is focused on evangelizing and delivering real business value from big data and fast data analytics to Fortune 1000 enterprises. Having spoken at numerous big data conferences on a range of topics including big data use cases, ROI, real-time streaming analytics, enterprise big data bus – Anand is a well-known thought leader in the big data ecosystem. He brings 22+ years of software technology, architecture and go-to-market experience in hi-tech, telecom, mobile, gaming and enterprise big data and analytics systems to enrich his speaking engagements.
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Mar 19, 2021 | 11:00 am PT / 2:00 pm ET