SAN FRANCISCO, Feb. 27, 2018 (GLOBE NEWSWIRE) — (ElasticON 2018) – Elastic, the company behind Elasticsearch and the Elastic Stack, today announced that it has exceeded the 225 million product download milestone, up from 100 million one year ago. In addition, Elastic unveiled the opening of its X-Pack code as part of a strategy to make it easier for users to download, inspect, and collaborate with Elastic’s engineering team on X-Pack feature development, which today includes features, such as, security, alerting, monitoring, Graph, and machine learning.
“We are truly humbled by the adoption of our products by millions of developers and thousands of customers who rely on them to power mission-critical use cases,” said Shay Banon, Elastic Founder and CEO. “Opening up our X-Pack code will give users full transparency and the ability to collaborate with us, as they do with our open source products. This will help us create better products and features and enable us to build a sustainable business model that inspires innovation for every developer, every customer, and partner who uses our software.”
Having acquired three new companies in past 18 months and with a user community that has grown to more than 100,000 developers around the world, ElasticON 2018, is the single largest gathering of Elasticsearch users. Over three days, more than 2,500 attendees have come together to learn and share ideas, see the unveiling of a record number of new features, and get first-look previews of upcoming technology releases.
- Swiftype App Search: Built for developers to add more powerful search functionality to their applications, Swiftype App Search delivers a robust set of APIs and additional search-specific features such as result positioning, synonyms, and typo-tolerance. Swiftype App Search is a turnkey SaaS solution requiring no infrastructure, management and maintenance, and offers an easy getting-started experience. Swiftype App Search is now available as a public beta.
- Machine Learning Forecasting: The first major extension of Elastic’s machine learning capabilities extends functionality into the realm of predictive analytics. Users can model time series data and use sophisticated, ready-made, machine learning algorithms to forecast outcomes several time intervals into the future. With on-demand forecasting, users can take an existing machine learning job and, using the predictive model built into machine learning, gain accurate predictions on where that model is expected to grow over the forecast period. The forecast results are written to an Elasticsearch index allowing users to compare actual results to forecast models. Elastic’s machine learning forecasting capabilities are now available as part of the 6.2 release.
- GIS App: Elastic’s newest REsearch project, GIS (geographic information systems) is a system designed to capture, store, manipulate, analyze, manage, and present all types of geographical data. As part of Kibana, this app enables a new way to perform ad-hoc geospatial analysis; embed enhanced map visualizations into your dashboard; and includes key features, such as, support for multiple layers, mapping individual geo points and client side styling. The GIS app is currently in technology preview.
- SQL for Elasticsearch: This new feature opens up the power of the Elastic Stack to the world’s most established database community of SQL developers, allowing users to query Elasticsearch data in familiar SQL Syntax. It also dramatically simplifies the (re)export of data from Elasticsearch back into external SQL environments with out-of-the-box JDBC support. By allowing Elasticsearch to understand SQL through a RESTful interface, SQL for Elasticsearch lets you query your Elasticsearch data using SQL syntax, returns results to those queries in a tabular form consistent with traditional SQL engines and provides a user interface to explore the data. SQL for Elasticsearch was introduced last year as a concept and will soon be available in an alpha and beta release.
- Canvas: Canvas provides an exciting new concept for the next generation of data visualization and data impressionism. With the incredible growth in the popularity of Kibana, Canvas represents a new way of exposing the insight gained from within the Elasticsearch data to live, real-time dashboards, presentations, and infographics. Canvas enables users to express the story of their Elasticsearch data like never before, eliminating the exhaustive, repetitive and time consuming process of exporting data into Excel to build a PowerPoint. Canvas is also pluggable, allowing users to bring in new data sources, visualization types, and UI components. Canvas was introduced last year as a concept and currently is in technology preview.
- Rollups: Commonly associated with metrics and logging use cases when storing data for long periods of time is required, rollups enable users to store a limited set of data, reducing the disk usage of historical data. An Elasticsearch rollup job allows users to configure periodic jobs that “rollup” or pre-aggregate data, and store the rollup in an index. One example is a metric like “average load time returned by the web server per hour,” of which, the average data is rolled up and stored, but other raw data attributes like the specific user, page, and IP information are not. This will be available soon in a beta for Elasticsearch and later with Kibana support.
- Flexible Deployment Configurations: As customers put more and more data into Elasticsearch and expand their use cases, Elastic introduces the concept of “sliders” to give users the ability to customize their cluster configurations. Available for Elastic Cloud and Elastic Cloud Enterprise (ECE) customers, some of the new capabilities include: support for multiple classes of hardware; support for cluster templates and hot/warm clusters; and the ability to add machine learning, dedicated master nodes, and APM nodes to existing cluster configurations. These new features will be available soon in both Elastic Cloud and Elastic Cloud Enterprise.
- Logstash Azure Monitoring Module: Built in collaboration with Microsoft, the Logstash Azure Monitoring module is the easiest way to monitor your Azure infrastructure and services with the Elastic Stack. This new module integrates with Azure’s centralized logging service to normalize Azure logs and metrics into JSON; uses Logstash to consume the data into Elasticsearch; and with Kibana, users can analyze infrastructure changes and authorization failures; identify suspicious activity and potential malicious actors; perform root-cause analysis by investigating user activity; and monitor and optimize SQL DB deployments. This will be available soon as a beta release.
Lastly, Elastic announced a new, official Elastic certification program. Fueled by user demand to have professional accreditation, Elastic will be offering new training curriculum designed for users to become experts and be certified by Elastic. New courses, Elasticsearch Engineer I and Elasticsearch Engineer II, will give users first-hand knowledge of installing, managing and optimizing Elasticsearch clusters, as well as, developing new solutions for analyzing their data. These courses are the foundation to becoming an Elastic Certified Engineer, which includes a hands-ons, technical and performance-based certification exam and an official digital Elastic certification badge for users who pass the exam.
Elastic builds software to make data usable in real time and at scale for search, logging, security, and analytics use cases. Founded in 2012, the company develops the open source Elastic Stack (Elasticsearch, Kibana, Beats, and Logstash), X-Pack (commercial features), and Elastic Cloud (a hosted offering). To date, there have been more than 225 million cumulative downloads. Backed by Benchmark Capital, Index Ventures, and NEA with more than $100 million in funding, Elastic has a distributed workforce with more than 800 employees in 30 countries. Learn more at elastic.co.
Reidy Communications for Elastic
Communications @ Elastic