By using the Eventador Platform, customers no longer need to provision expensive, often slow database infrastructure and can instead query streaming data directly using materialized views.
AUSTIN, Texas (PRWEB) March 18, 2020
Eventador.io, the streaming data engine for building killer applications, unveiled the Eventador Platform version 2.0, the first end-to-end, produce-to-consume stream processing platform that gives companies the ability to quickly and easily build applications from the firehose of streaming data. With Eventador, data science, developer, and data engineering teams can unlock new value and power from data streams in their models, applications, and ETL flows.
Eventador Platform v2.0 solves the complex problem of providing a queryable, time-consistent state of streams via materialized views. Views are defined using ANSI SQL, are automatically indexed and maintained, and arbitrarily queried via RESTful endpoints. Users can query by secondary key, perform range scans, and can utilize a suite of common operators against these views.
With the new release, the Eventador Platform removes the need for additional database, web server, load balancing, or other complex infrastructure, which means developing streaming applications is now faster and less costly than with current, often piecemeal stream processing systems. This not only provides organizations the lowest possible TCO for their streaming platforms but also increases innovation and access to new revenue streams with faster application time-to-market.
“Companies have adopted Apache Kafka as the de facto data bus for streaming data,” said Kenny Gorman, Co-founder and CEO of Eventador, “By using the Eventador Platform, customers no longer need to provision expensive, often slow database infrastructure and can instead query streaming data directly using materialized views. The Eventador Platform is a bespoke solution, not a simple add-on to Kafka, that solves the disconnect between streaming data and applications.”
About the Eventador Platform v2.0 Release:
The Eventador Platform is a high performance, enterprise-grade engine for running continuous stream processing jobs. Users can now materialize stateful and queryable views of data using ANSI SQL for use in applications, notebooks, machine learning models, and more.
The Eventador Platform includes:
- A production-grade, Continuous SQL engine that lets users easily interact with streaming data just like they would with a database. With an interactive SQL parser, users can easily inspect, and reason about streams of data. Users can also create Materialized Views on streams of data that are continuously updated, indexed, and maintained. Teams can easily query materialized views via REST, with a rich set of operators and capabilities using a powerful query builder UI.
- A simple, secure and fully managed Apache Flink platform that allows you to write Apache Flink jobs in Java/Scala and that process streaming data to/from any source or sink including Apache Kafka. It also provides rich features including team management, stop/restart/clone from savepoint, job metadata, Github integration and automatic job builds, and more.
Eventador Platform Availability:
The Eventador Platform is currently available with a 14-day free trial at https://eventador.cloud/register. Or for more information on the Eventador Platform, you can contact firstname.lastname@example.org.
- Introducing Materialized Views on Data Streams Blog Post
- Eventador Platform 2.0 Explained Podcast
- Continuous SQL Explained
- Sample Data Sets and Examples for the Eventador Platform
The Eventador Platform is the streaming data engine that data engineering, data science, and developer teams use to unlock the value of streaming data and build killer apps. Headquartered in Austin, Texas, Eventador.io is dedicated to the mission of providing the platform that FinTech, IoT, Netsec and other real-time applications can be built on using simple SQL. To find out more, visit https://eventador.io or register for a free trial at https://eventador.cloud/register.