Author
Akmal B. Chaudhri

Akmal B. Chaudhri
Technical Evangelist, GridGain Systems
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…
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This report is for week 7 of the Big Data meetup tour. The two cities covered this week were Brussels and Luxembourg. These were also the last two cities in the Big Data meetup tour. Day 15: Brussels I flew out from London to Brussels on Wednesday 17 October. It was the earliest flight on this Big Data meetup tour. My hotel was in downtown Brussels, near Brussels Midi Station. Since I planned…
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This report is for week 6 of the Big Data meetup tour. The two cities covered this week were Singapore and Stockholm, half a world apart. Continuing on from the previous meetup report, I took the KLIA Ekspres from KL Sentral to Kuala Lumpur International Airport (KLIA) on Sunday 7 October. Anyone that has attended one of my meetup or conference presentations will know that I occasionally make…
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This report is for week 5 of the Big Data meetup tour. Similar to week 4, only one city this week and that was Helsinki. However, from Helsinki I travelled on to Kuala Lumpur (KL) to run an Apache® Ignite™ workshop. Day 12: Helsinki I have had the good fortune to visit many cities around the world through various roles that I have held with other employers, so I have actually visited Helsinki…
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This is week 4 of the Big Data meetup tour. Only one city this week and that was Oslo. So, this meetup report will be quite short. Day 11: Oslo Quite a long time ago, I visited Oslo when working for a previous employer. That last trip was in winter time and there was lots of snow. This time, no snow and very mild weather. Having stayed in some airport hotels over the past few weeks where it…
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Previously, I briefly wrote about the first week and the second week of the Big Data meetup tour. This blog post is about the third week of the Big Data meetup tour. Three more meetups this week. This time it was Paris, Amsterdam and London. Great to finish-up in London, which is my home town. This first meetup tour also concludes in London. However, there are other meetups that GridGain is…
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In the previous blog post, I provided a quick summary of the first week of the Big Data Tour, where I am presenting on Apache® Ignite™. After returning home from the first week and able to rest over the weekend, I started the second week of the tour on Monday 10 September. In this blog post, I will quickly summarise the second week of the tour. The second week required more travel as there were…
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In my job at GridGain, I am privileged to work with some of the smartest and most talented people that I have ever met in my IT career. I am also fortunate in the variety of work that I get involved with. This includes presenting at meetups and conferences, building demos, writing blog posts, presenting webinars, occasionally writing and editing product documentation, and even contributing to the…
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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…
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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…
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In this two-part series, we will look at how Apache® Ignite™ and Apache® Spark™ can be used together. 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 typically ingests data from HDFS or other storage.…
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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…
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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…
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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…
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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…
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