![]() ![]() With high adoption rates, a community began to form, and together with Steven Schuurman, Uri Boness, and Simon Willnauer, they founded a search company. The response was impressive, and users took to it naturally. As of this writing, Elasticsearch has produced six major releases in the following order: Shay Banon released the first version of Elasticsearch in February 2010. The second was Elasticsearch (with Apache Lucene under the hood). He built “a solution built from the group up to be distributed” and used a common interface, JSON over HTTP, suitable for programming languages other than Java. Brief History of ElasticsearchĮlasticsearch was created by Shay Banon, a software engineer who set out to build a scalable search solution for his wife’s growing list of recipes. ![]() In this post, we will cover an overview of the basics of Elasticsearch and when and why you should use it. In contrast, Elasticsearch provides true search engine functionality with the best performance for real-time and time-series data retrieval. Hadoop and Spark are perfect for large transactions, especially bulk inserts or pipelining. Indeed, there are applications you have already heard of for use in big data, such as Apache Hadoop and Apache Spark - and then there’s Elasticsearch. Handling big data that you intend to use for search or analytics for your machine learning, artificial intelligence, IoT, geospatial processing, telecommunications, military, and weapon systems, and health systems applications requires speed, real-time processing, scalability, and performance. The dilemma is that it takes a lot of research and development, financial cost, and time to accomplish and meet delivery time, speed, and flexibility demands. The problem is that it’s not a simple thing to do. Many organizations may attempt to develop something from scratch in combination with various existing technologies to provide storage of big data, analytics, and other related services to power up the application to their standards. ![]() Large organizations with voluminous data have experienced rough times and a taxing amount of jobs, especially for improvising analytics, likely demanding real-time results, especially during retrieval or searching data on a real-time basis. Existing databases may be able to provide this, but regardless of your best setup and configuration efforts, the speed is often poor or underperforming. Your traditional, orthodox databases cannot provide the types of blazing speeds required to provide your analytical reports, especially when running a large data aggregation. Managing big data can be very taxing and stressful, especially when speed, reliability, scalability, and high availability are requirements for your organization. ![]()
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