GridGain and Apache Ignite In-Memory Computing Videos

Learn about GridGain® and Apache® Ignite by watching this library of in-memory computing videos captured during speaking engagements at conferences, Meetups, and webinars.


In this presentation, attendees will learn about the challenges and pitfalls they may face when architecting and developing a distributed system. They will also see how to take advantage of the affinity collocation concept that is one of the most powerful and usually undervalued techniques provided by distributed systems.
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In this presentation Dmitriy will present how to achieve the best performance and scale with the new memory-centric approach to distributed architectures. Dmitriy will go over traditional in-memory and disk-based systems, compare their strengths and weaknesses, cover such features as ACID compliance, SQL compatibility, persistence, replication, security, fault…
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Machine learning (ML) and deep learning (DL) support has been added to Ignite recently. It not only eliminates any delays caused by transferring data to a different database or store. It delivers near real-time performance by running a variety of ML and DL algorithms in place, in memory and on disk, that are optimized for collocated processing.
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In his keynote at the IMCS 2018, Abe Kleinfeld discusses how in-memory computing is leading the way for enterprises to digitally transform, enabling the acceleration of sales and growth of business from end-to-end.
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Machine learning is a method of data analysis that automates the building of analytical models.  These insights bring tremendous benefits into many different domains. For business users, in particular, these insights help organizations improve customer experience, become more competitive, and respond much faster to opportunities or threats. 
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Val explains how Apache Ignite simplifies development and improves performance for Apache Spark. He demonstrates how Apache Spark and Ignite are integrated, and how they are used to together for analytics, stream processing and machine learning.
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The discussion moves from larger trends in Cloud Computing. Introductory remarks by Stephen Turner start with a focus on the big picture, and larger geopolitical dynamics and how they may impact Cloud Computing. He will then moderate a discussion drilling down to more technical trends seen around the Valley and the World: in-memory computing, AI, web services MSA +…
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While the cost of memory is still slightly higher than disk-based storage, an in-memory computing solution offers a tremendous increase in performance and much greater flexibility to incorporate new capabilities in the future. The benefit? A far superior return on investment (ROI), especially when competitive advantage and customer experience is taken into account.
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Apache Ignite has built-in machine learning (ML) and deep learning (DL). It eliminates any delays caused by transferring data to a different database or store. It also delivers near real-time performance by running a variety of ML and DL algorithms in place, in memory, that are optimized for collocated processing.
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Talk 1: Apache Phoenix combines standard SQL and JDBC APIs with the scalability of HBase’s NoSQL data store to provide the best of both worlds. Thinking along the same vein, We looked to combine Apache Phoenix with Apache Ignite as a way to offer in-memory performance with large volumes of data including support for 3D-XPoint memory.
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Talk 1: Best practices for Hadoop acceleration. Talk 2: Learn about In-Memory data processing for IoT, streaming analytics and OLTP with SQL ACID transactions and high availability. Talk 3: In-Memory Key Value Store (KVS) in FPGA for Ultra Low Latency and High 
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Attendees will be introduced to the fundamental capabilities of in-memory computing platforms that boost highly-loaded applications, research projects, machine learning, risk analysis and fraud-detection tasks. How? By storing and processing massive amounts of data in memory and on disk across a cluster of machines.
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Talk 1: In this talk, learn how to use an Application Tier Database Cache with SQL. Writing a naive SQL read or write cache is easy. Writing a SQL read/write cache with cache coherency, concurrency, ACID transactions and high availability is hard. Having your SQL read/write cache enable a latency of less than 1 ms at the 99th percentile without any user code is very…
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