The GridGain Systems In-Memory Computing Blog
Challenged with scaling stream processing for your organization? Then you'll want to register for our webinar, "Best Practices for Stream Processing with GridGain® and Apache® Ignite™ and Kafka." This free, live webinar is scheduled for Oct. 10 at 11 a.m. PDT (2 p.m. EDT). Register here.
Making stream processing scale requires making all the components -- messaging, processing, storage -- scale…
GridGain's London-based technology evangelist Akma Chaudhri will deliver a live one-hour webinar Sept. 25 that will be of great value to anyone working with Big Data in a bank or financial institution. It's titled "Apache® Ignite™ + GridGain: powering up banks and financial institutions with distributed systems" and will start at 10 a.m. BST (that's 2 a.m. PDT and 5 a.m. EDT).
Don't worry if…
With real-time streaming analytics there is no room (or time) for staging or disk.
Denis Magda, GridGain's director of product management and Apache Ignite PMC chair, delivered an excellent webinar Sept. 12 detailing the best practices used for real-time stream ingestion, processing and analytics using Apache® Ignite™, GridGain®, Apache Kafka™, Apache Spark™ and other technologies.
The webinar…
GridGain technology evangelist Akmal Chaudhri delivered the second webinar of his popular highly technical two-part series for software developers and architects on Aug. 28 titled, "In-Memory Computing Essentials for Architects and Developers: Part 2."
This free webinar was recorded and is available for playback (and the slides for download) here.
Akmal continued his introduction of more of the…
In this two-part series, we will look at how Apache® Ignite™ and Apache® Spark™ can be used together.Let's briefly recap what we covered in the first article.Ignite is a memory-centric distributed database, caching, and processing platform. It is designed for transactional, analytical, and streaming workloads, delivering in-memory performance at scale.Spark is a streaming and compute engine that…
Apache® Ignite™ is a very versatile product that supports a wide-range of integrated components. These components include a Machine Learning (ML) library that supports popular ML algorithms, such as Linear Regression, k-NN Classification, and K-Means Clustering.
The ML capabilities of Ignite provide a wide-range of benefits, as shown in Figure 1. For example, Ignite can work on the data in place…
In-memory computing is a journey that requires up-front planning and preparation to complete. Companies often start with caching to add speed and scale to existing applications. But before you know it, you're building new hybrid transactional/analytical processing (HTAP) applications, new streaming analytics applications, or even starting to implement machine and deep learning. If you don't…
In the previous article in this Machine Learning series, we looked at k-NN Classification with Apache® Ignite™. We’ll now look at another Machine Learning algorithm and conclude our series. In this article, we’ll look at K-Means Clustering using the Titanic dataset. Very conveniently, Kaggle provides the dataset in a CSV form. For our analysis, we are interested in two clusters: whether…
Learn how Kubernetes can orchestrate a distributed database or in-memory computing solutions using Apache® Ignite™ as an example.
Denis Madga, GridGain's director of product management, took one of his most popular meetup talks and turned it into a webinar on July. And he recorded it! His talk is available for playback (along with his slides) here.
In-memory computing technologies such…
In the previous article in this Machine Learning series, we looked at Linear Regression with Apache® Ignite™. Now let’s take the opportunity to try another Machine Learning algorithm. This time we’ll look at k-Nearest Neighbor (k-NN) Classification. This algorithm is useful for determining class membership, where we classify an object based upon the most common class amongst its k nearest…