Infoworks, the leading provider of agile data engineering software platforms, today announced the release of Infoworks’ Autonomous Data Engine (ADE) 2.4, the latest release of the company’s end-to-end automated software platform for agile data engineering.
Infoworks ADE automates the entire data engineering process, from the data source to the point of consumption by analytics applications, delivering the agility required for businesses to easily and quickly handle new analytics use cases, new data driven applications, and new data sources, on-premises and in the cloud.
“Infoworks’ latest release expands the limits of what is possible on big data environments today,” said Ramesh Menon, VP of Products at Infoworks. “Our new capabilities dramatically simplify the implementation of big data use cases and deliver analytics agility to enterprises. With Infoworks Autonomous Data Engine, enterprises can build and launch analytics use cases into production ten to one hundred times faster while using one-tenth of the resources required by any alternate approach.”
“While the ecosystem of big data processing projects has the potential to improve business insight and agility, the various components can also be complex and costly to configure, deploy and manage,” said Matt Aslett, Research Director, Data Platforms and Analytics, 451 Research. “Automation of data processing pipelines has a key role to play in helping enterprises deliver on the promise of big data by simplifying configuration and management and reducing the amount of time spent on data preparation, rather than analysis.”
The new features and capabilities added to the Infoworks Autonomous Data Engine include:
· Real-Time Streaming Data Ingestion:
Infoworks has added support for ingestion and processing of real-time, streaming data from a variety of data sources using streaming platforms such as Apache Kafka. Customers can now deploy new use cases including IoT, real-time fraud analytics, offer targeting, and more. Coupled with new platform capabilities such as the continuous CDC and merge capabilities, streaming data is continuously available for analytics with the Infoworks platform.
· Automated Continuous CDC and Merge Process for Data Ingestion:
Infoworks now allows data engineers with minimal to no Hadoop experience to quickly ingest source data and keep the data continuously synchronized into big data and cloud environments. Infoworks fully automates the handling of source data/schema changes as well as slowly changing dimensions (SCD Type I & II) from a variety of data sources including Oracle, Teradata, SQL Server and others without requiring hand coding. The Infoworks CDC and merge process is designed for high performance and scales with data. It can merge millions of change records into billions of base table records in seconds.
· Data Acceleration Stack:
Infoworks dramatically accelerates the response time for big data queries through a single query interface that optimally supports all analytic use cases: business intelligence, machine learning, ad hoc and batch. Queries are automatically routed to one of three data access layers that deliver different performance and scalability characteristics:
o Big data cubes for use cases that requires sub second response times, suitable for interactive dashboards
o In-memory data models that provides response times in few seconds, suitable for reporting and machine learning use cases
o Optimized data models held in data lake storage for query response times in tens of seconds, suitable for ad-hoc queries and batch use cases on massively scalable data sets
· Automated Orchestration:
Customers can now automatically promote data pipelines from their development and sandbox environments into a production environment with a single click of a mouse. Infoworks ADE Orchestrator provides automatic fault tolerance, monitoring and optimization of big data pipelines. Infoworks ADE Orchestrator also provides the ability to start, pause, and restart production pipelines from the last completed step, providing efficient operational controls. This is the first time such capabilities have been made available to run on all major Hadoop and big data cloud environments.
· Data Masking for GDPR:
The Autonomous Data Engine now supports data masking to ensure personally identifiable information is managed in accordance with GDPR and other government and industry regulations.