Rapidly Build and Run Apache Spark Applications in the Cloud with StreamAnalytix on AWS Marketplace

StreamAnalytix is an Apache Spark based big __data formats, and use cases.

By Impetus. Sponsored Post.
Apache Spark, a fast in-memory data processing engine and a distributed computing framework, can handle all needs for batch and streaming data processing, analytics and machine learning. Large data-driven enterprises are using Spark for tasks ranging from ingestion, ETL, and data quality processing to advanced analytics and machine learning.

Despite its growing popularity, Spark is complex, and the learning curve is steep. Developing code, integrating and testing with Spark is often time-consuming.

StreamAnalytix, a visual big data analytics platform offers a low-code solution to the complexities of building enterprise-grade Spark applications.

StreamAnalytix is now available on the AWS Marketplace. StreamAnalytix allows users to rapidly build and operationalize Apache Spark applications (up-to 10x faster vs. hand-coding), while working with single or multiple Spark nodes. StreamAnalytix on AWS Marketplace further simplifies Spark development in the cloud, making it easy for existing enterprise teams to build applications instantly with basic Spark understanding and minimal set-up time.

Impetus Spark Made Easy

StreamAnalytix is available on AWS marketplace in two tiers:

  • A free to use single node version that offers you a full range of data processing and analytics functionality to build, test, and run Apache Spark applications on any single node.
  • A full-scale enterprise edition that enables the use of multiple Spark clusters with no limit to the number of connected nodes.

Select the instance on which you want to deploy the StreamAnalytix AMI and be up-and-running in a few minutes.

User benefits

  • An array of drag-and-drop operators in an intuitive visual development environment
  • Build, train, calibrate, deploy, and monitor machine learning models on batch and real-time data
  • Built-in operators for Spark MLlib, Spark ML, PMML, H2O, and TensorFlow
  • Introduce custom logic in the language of choice, including Java, Python, Scala, and SQL
  • Built-in connectors for Apache Kafka, Amazon S3, Elasticsearch, Apache HBase, and Apache Hive