The new offering is designed to automate real-time change data capture, delivery and refinement for analytics, with live demonstrations at AWS re:Invent 2018
Attunity Ltd., a leading provider of data integration and big data management software solutions, announced a new solution , Attunity for Data Lakes on Amazon Web Services (AWS) designed to automate streaming data pipelines on AWS. The offering is designed to support streaming real-time data from major enterprise databases, mainframes, and applications such as SAP, to accelerate near real-time analytics, machine learning (ML) and artificial intelligence (AI) initiatives. These new capabilities are being demonstrated live this week in Attunity booth 630 at AWS re:Invent 2018.
Enterprises are moving to cloud data lakes as they provide greater agility and elasticity but continue to be challenged to efficiently create analytics-ready data sets from heterogeneous data sources. Such integration can be a manually intensive and complex endeavor, challenging to assemble and often resulting in outdated data when it’s finally ready for business consumption. Attunity helps overcome these challenges with a solution leveraging Apache Spark technology to further accelerate and automate data pipelines – from the generation of source system data streams right through to the creation of analytics-ready data sets.
Attunity’s support for data lakes on AWS helps enterprises to:
- Improve operational efficiency and increase ROI – Using Apache Spark as a high-performance engine, the solution is designed to automate the generation of transformations and allow for analytics-ready data sets. This means that data engineers can expect to quickly create reusable, automated data pipelines that streamline the delivery of analytics-ready data sets to end users, lessening the need for manual coding or expensive development resources.
- Provide transactional data for analytics efficiently and in near real-time – Continuously streaming data and metadata updates powered by Attunity’s change data capture (CDC) technology means that data sets are accurate and current, and also support a broad range of data sources from on-premises and cloud databases, to data warehouses, SAP and mainframe systems.
- Provision trusted data sets to promote confidence – Users discover managed data sets through a catalog which includes metadata such as data lineage. As a result, users can be confident in the origin and preparation of their analytics-ready data sets.
- Establish best practices for real-time data pipeline delivery – Attunity’s offering is designed to automate the necessary steps in a multi-zone data lake and provide a full historical data store from which analytic-specific data sets are derived.
- Readily adapt to changing technologies – The solution’s graphical interface and data pipeline management functionality readily adapt to changes in underlying data lake technology and new AWS offerings with just a simple drag-and-drop.
Customer Fanatics shared its success to date using Attunity for data ingestion to a data lake on AWS:
“With Attunity, we’re able to better analyze immense data volumes from disparate applications including our e-commerce, transactional and back office systems. It has provided new efficiencies by making our data available immediately on a data lake on AWS, ultimately expediting the analytics process and allowing access to the freshest data possible for real-time decision making,” said Kiran Nagarur, Vice President, Data Science and Engineering at Fanatics.
“With this new data pipeline automation offering, Attunity for Data Lakes on AWS, we continue to strengthen our relationship with AWS, empowering enterprises to easily and quickly automate their data lakes for more timely analytics,” said Itamar Ankorion, Chief Marketing Officer at Attunity. “Our Attunity for Data Lakes on AWS solution will help our customers to continually reap the benefits of having their data on AWS, and allow for real-time analytics using data we make available in Amazon Simple Storage Service (Amazon S3), Amazon Redshift, Amazon Kinesis, Snowflake, and other analytics data infrastructure running on AWS.”