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How to Fast Load Large Datasets into Apache Ignite by Using a Key-Value API

Now, in-memory cache technology is becoming popular, motivating companies to experiment with distributed systems. The technology is advertised to be fast, and data-load speed is often critical for building a successful solution prototype. This blog post provides a technical tutorial on how to populate a distributed Apache Ignite cluster with values that originate from large relational tables. All the code in the tutorial is available from the FastDataLoad repository at the GitHub.
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Previous Entries

Note: This is post 2 in the blog series: Continuous Machine Learning at Scale with Apache Ignite. For post 1 click here and for post 3 click here. In my first post, I introduced the topic “continuous machine learning at scale with Apache Ignite,” which is how we members of the Apache® Ignite™ community describe machine learning (ML) architectures that offer the following advantages: Support…
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Glenn Wiebe, Solutions Architect at GridGain, has created a helpful video series that introduces developers to Apache Ignite as an in-memory database (IMDB) and features a demo that will set up a working IMDB in ten minutes. The demo walks through the process of configuration creation, data loading and cluster querying via SQL tools. 1. Introduction Learn the difference between Apache Ignite as…
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Note: This is post 1 in the blog series: Continuous Machine Learning at Scale with Apache Ignite. For post 2 click here and for post 3 click here. This is my first blog post in a series that discusses continuous machine learning at scale with the Apache® Ignite™ machine learning (ML) library. In this article, I’ll introduce the notion of continuous machine learning at scale. Then, I’ll discuss…
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Kafka with Debezium and GridGain connectors allows synchronizing data between third party Databases and a GridGain cluster. This change data capture based synchronization can be done without any coding; all it requires is to prepare configuration files for each of the points. Developers and architects who can’t yet fully move from a legacy system can deploy this solution to give a performance…
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In-memory computing can provide tremendous benefits for the 5G ecosystem. We’ve seen the marketing for the new fifth-generation mobile networks. The benefits of 5G for end-users are easy to understand. Speeds faster than your home broadband and latencies only a little slower promise to be game-changers for consumers, enhancing existing applications and opening open entirely new categories that we…
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Memory access is so much faster than disk I/O that many of us expect to gain striking performance advantages by merely deploying a distributed in-memory cluster and start reading data from it. However, sometimes we overlook the fact that a network interconnects cluster nodes with our applications, and it can quickly diminish the positive effects of having an in-memory cluster if a lot of data…
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Apache Ignite AWS basics

Introduction Cloud computing is on the rise for a couple of reasons: it is flexible, relatively cheap compared to supporting in-house infrastructure, and it allows excellent automation of resource allocation, thus cutting costs even more. Cloud computing also allows horizontal scalability, which is crucial for many businesses in today’s digital age. When the amount of data to be processed grows…
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In keeping with our commitment to more regular and frequent releases, GridGain Web Console 2019.11.00 is now available for download from GridGain Downloads and DockerHub. This release includes improvements for deploying Web Console on RedHat OpenShift, updates to the hosted Web Console, and bug fixes.   RedHat OpenShift Support GridGain and Apache Ignite have supported container-based…
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My acquaintanceship with PostgreSQL started back in 2009 - the time when many companies were trying to board the social networking train by following Facebook's footsteps. An employer I used to work for was not an exception. Our team was building a social networking platform for a specific audience and faced various architectural challenges. For instance, soon after launching the product and…
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Introduction The Spark SQL engine provides structured streaming data processing. The benefit here is that users can implement scalable and fault-tolerant data stream processing between the initial data source and final data sync. You can read more about it here: https://spark.apache.org/docs/latest/structured-streaming-programming-guide.html Apache Ignite provides the…
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