Presenting Apache Ignite SQL Grid at Big Data Bootcamp
Apache Ignite community welcomes you to attend Big Data Bootcamp on March 27th, 28th and 29th 2017 in Santa Clara, USA.
The conference gathers experts and vendors from Big Data realm in sunny California who will be covering a variety of Big Data products and technologies, including, but not limited to, Hadoop, Spark, NoSQL, Data Science, Machine Learning, Artificial Intelligence & Deep Learning.
Apache Ignite will be introduced at the conference by its PMC chair and committer - Denis Magda.
As all we know, in-memory data grids bring exceptional performance and scalability gains to applications built on top of them. The applications truly achieve 10x more performance improvement and become easily scalable and fault-tolerant thanks to the unique data grids architecture. However, because of this particular architecture, a majority of data grids have to sacrifice traditional SQL support requiring application developers to completely rewrite their SQL-based code to support data grid specific APIs. This, however, is not true for Apache Ignite.
In this presentation, Denis will introduce Apache Ignite SQL Grid component that combines the best of two worlds - performance and scalability of data grids and traditional ANSI-99 SQL support of relational databases. Moreover, Denis will take an existing application that works with a relational database and will show how to run it on top of Apache Ignite with minimum efforts.
The talk is called "Apache Ignite SQL Grid: Hot Blend of Traditional SQL and Swift Data Grid" and takes place at 1:00 PM - 1:40PM on March 28. Refer to Big Data Bootcamp's agenda for more details.
Finally, use promotional code SPEAKER to receive $200 discount on or before March 15th by registering at the conference site.
See you at the conference!
Apache Ignite 1.9 Released
Apache Ignite community is pleased to announce Apache Ignite 1.9 - the next minor release of a well-known in-memory data fabric. The release, as usual, encompasses many bug fixes, performance improvements and fresh features. Below you can see a description of the most significant updates.
Apache Ignite was integrated with Kubernetes which is a modern open source container cluster manager. The integration helps to simplify a deployment of an Apache Ignite cluster in environments managed by Kubernetes and let the latter care of resources management, cluster's scalability and lifecycle.
For instance, you're no longer need to monitor a cluster state constantly to be sure that the number of cluster nodes doesn't go, let's say, below 4. If Kubernetes sees that one cluster node is disconnected and only 3 are left then it will start one more automatically to meet the deployment requirements.
Refer to Kubernetes Deployment Getting Started if this is the feature of interest for you.
Performance Optimizations and Benchmarks Automation
Apache Ignite 1.9 can boast of much better performance for core cache operations and SQL queries in compare to the previous Apache Ignite 1.8 release. In general, we observe up to 40% performance increase for particular operations.
It's no longer a challenge to reproduce the performance numbers. Starting with Apache Ignite 1.9 release all the benchmarks are delivered in every Apache Ignite distribution and can be easily executed in your own environment.
Data Modification Language and Queries Parallelism
The community keeps spending significant time improving SQL Grid component that empowers Apache Ignite users with in-memory database capabilities.
In this release, DML (Data Modification Language) support was expanded to the level of Ignite.NET and Ignite.C++ APIs. Plus, a streaming mode was introduced for DML allowing to execute DML operations even faster for specific scenarios like initial data preloading.
One more SQL Grid related optimization makes it possible to parallelize a query execution on every Ignite node where the query has been mapped. By default, a query is executed in a single thread on every participating node. However, for a variety of OLAP use cases it might be a bottleneck and this is where the query parallelism can help out.
Apache Ignite implemented .NET TransactionScope API allowing to work with distributed Apache Ignite transactions fully relaying on standard interfaces available in .NET Framework. Refer to this documentation page for more information.
Ignite.C++ introduced support for well-known continuous queries API. Now, you can listen to data modifications happened on Apache Ignite's distributed caches side from your C++ applications.
Ignite’s spark integration was upgraded to the latest Spark version. Presently, you can leverage from Ignite Shared RDDs in applications using latest Spark version.
Give a Try
Go and grab the latest 1.9 release from our main site. Looking forward to your feedback!