Welcome to ZigmaData, software that eliminates many manual steps from the process and enables a smooth, automated flow of data from one station to the next. It starts by defining what, where, and how data is collected. It automates the processes involved in extracting, transforming, combining, validating, and loading data for further analysis and visualization. It provides an indexed engine and visualization component. ZigmaData allows use of polyglot tools on polyglot data hiding all complexities and making data transparent to end user. Finally ZigmaData has a model comparison tool to manage efficiency of ML models generated from different tools and languages. It provides end-to-end velocity by eliminating errors and combating bottlenecks or latency. It can process multiple data streams at once. In short, it is an absolute necessity for today’s data-driven enterprise.

ZigmaData is a complete solution for the enterprise data needs. It has three primary components.

1) CONNECT: A UI based interface to configure your data sources, inclusive of schema on read and schema on write systems, API connections, OData interfaces, JDBC and streaming sources.

2) COMPOSE: You write a simple ANSI SQL query and apply functions to transform your data to accompany your need. The query can be iteratively executed, scheduled or stored in a storage backed by S3, on premise or on cloud. The scheduler has all features for managing the data pipelines, including those ingestion handled by Spark or any custom code.

3) CONSUME:  Powered by a JDBC connector, you can access the data from R, Spark, Python, Java and Scala in your favorite editors.We also supply a powerful indexed store and a visualization platform.

All the above are implemented by stable and proven open source tools, intelligently integrated together. Your cost for production application will be sub optimal.

To use a fully containerized ZigmaData platform, it will take just 30 minutes, and will handle petabytes of workload efficiently.

A data pipeline views all data as streaming data and it allows for flexible schemas. Regardless of whether it comes from static sources (like a flat-file database) or from real-time sources (such as online retail transactions), the data pipeline divides each data stream into smaller chunks that it processes in parallel, conferring extra computing power.

The data pipeline does not require the ultimate destination to be a data warehouse. It can route data into another application, such as a visualization tool or ML application. Think of it as the ultimate assembly line.

To know more, please write to us