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.
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.
Best Practices for Hadoop Acceleration - In-Memory for the Enterprise - In-Memory Key Value Store (KVS) in FPGA for Ultra Low Latency and High
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
Akmal B. Chaudhri
Introducing Big-SQL: Apache Ignite + Apache Phoenix on Spring Boot - Best Practices for Stream Ingestion, Processing and Analytics Using In-Memory Computing.
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.
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…
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.
Akmal B. Chaudhri
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.
Abe Kleinfeld discuss the growing role that an In-Memory Computing Platform serves as companies progress toward a digital world. All companies, regardless of the industry they serve, are rapidly becoming software companies in their own right, creating a real-time digital twin that virtually models their physical world.
Terry Erisman, VP of Marketing at GridGain, moderates a panel of industry experts (including end users and vendors) from ING, FSB Technology, CG Consultancy, GridGain Systems and Scaleout Software will discuss the impact of in-memory computing on the digital enterprise. The panel discussion was recorded June 26 in London at the In-Memory Computing Summit Europe 2018.
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.
Denis Magda, Rob Meyer
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.
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 +…
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.
Akmal B. Chaudhri
The In-Memory Computing Platform that is Durable, Strongly Consistent and Highly Available with Powerful SQL, Key-Value and Processing APIs
GridGain technology evangelist Akmal Chaudhri explains what GridGain is all about