Akmal B. Chaudhri

Introduction In the first article in this series, we installed Node.js, the Node.js Thin Client package for Ignite and tested an example application. In this article, let's look at a particular scenario where Ignite can really help when working with existing data from another source, such as a Relational Database System. Often in industry, there are systems that have great business value. These…

Introduction Apache® Ignite™ provides support for a number of major programming languages. Recently, support for additional programming languages has also been added using what is termed as a Thin Client. New Thin Clients include Python, PHP and Node.js. The characteristics of a Thin Client are as follows: It is a lightweight Ignite client that connects to a cluster using a standard socket…

Introduction In the previous article, we discussed the steps required to sign-up for a GridGain® Cloud account, created our first cluster, described the two built-in SQL demos and briefly reviewed the monitoring capabilities. In this article, let's look at examples of how to connect to a GridGain Cloud Cluster using two different programming languages. Recall that in the previous article, we…

Introduction In 2018, GridGain® previewed GridGain Cloud. GridGain Cloud enables a GridGain cluster to be run as a service. It supports both distributed in-memory computing and persistence. A number of APIs are supported: JDBC ODBC Binary Protocol and Thin Clients REST We will look at examples of some of these APIs in this article series. GridGain Cloud provides compatibility with…

Previously, we looked at how to use GridGain® and Kafka® using a local installation. Let’s now look at an example where we deploy in the Cloud. We will use the GridGain Cloud and the Confluent Cloud environments. If you'd like to follow along with this example, ensure that you meet the required prerequisites first: Create an account on the GridGain Cloud Create an account on the Confluent…

One of the features of Apache® Ignite™ is its ability to integrate with streaming technologies, such as Spark Streaming, Flink, Kafka, and so on. These streaming capabilities can be used to ingest finite quantities of data or continuous streams of data, with the added bonus of fault tolerance and scale that Ignite provides. Data can be streamed into Ignite at very high rates that may reach many…

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 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…

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…

In the previous article in this Machine Learning series, we looked at the Apache® Ignite™ Machine Learning Grid. Now let’s take the opportunity to drill-down further into some of the Machine Learning algorithms that are supported in Apache Ignite and try out some examples using popular datasets. If we search for suitable datasets to use, we can find many that are available. However, one dataset…

In a previous article, we discussed the Apache® Ignite™ Machine Learning Grid. At that time, a beta release was available. Subsequently, in version 2.4, Machine Learning became Generally Available. Since the 2.4 release, more improvements and developments have been added, including support for Partitioned-Based Datasets and Genetic Algorithms. Many of the Machine Learning examples that are…

These are very exciting times for Apache Ignite. During this past year that I have been with GridGain, I have seen some significant technology additions to the Open Source project, such as support for SQL-99, Native Persistence, and Machine Learning to name but three. Earlier this year, new Genetic Algorithm (GA) code was donated to the Apache Software Foundation. Since I am not very familiar…

This is the second in a two-part series on the use of Fast Data in Healthcare and how in-memory technologies, such as Apache® Ignite™, can meet the requirements and challenges of the Healthcare industry. Part 1 focused on identifying some of the key challenges in Healthcare. In Part 2, we will discuss a Healthcare case study and learn how Apache Ignite and GridGain solved a customer’s…

This is a two-part series on the use of Fast Data in Healthcare and how in-memory technologies, such as Apache Ignite, can meet the requirements and challenges of the Healthcare industry. In this first part, we will focus on identifying some of the key challenges in Healthcare and in the next part, we will discuss a Healthcare case study and learn how Apache Ignite and GridGain solved a customer’…