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SnapLogic Introduces SnapLogic eXtreme to Help Data Engineers Operationalize Cloud-Based Big Data Integrations

SnapLogic, the leader in self-service application and data integration, today announced SnapLogic eXtreme, a new solution to support complex data processes on cloud big data services like Amazon Elastic MapReduce (EMR), Microsoft HDInsight, and Google Cloud. Through SnapLogic's drag and drop interface, data integrators and big data engineers can now use SnapLogic eXtreme together with the company's flagship Enterprise Integration Cloud platform to build powerful Apache Spark pipelines and manage cloud data architectures without having to write complex code. By significantly decreasing the operational time and cost and reducing the skills gap needed to take advantage of cloud-based data lakes and services, SnapLogic eXtreme makes the immense power of cloud-based big data viable for businesses everywhere for the first time.

"Just as we democratized data and app integration for IT and citizen integrators, we are extending our capabilities to bring the same time and cost benefits to data engineers integrating big data services," said James Markarian, CTO of SnapLogic. "Companies are beginning to hit the data, power, and cost limits of conducting big data computations on-premises, and while cloud big data services offer limitless resources at scale they require cost prohibitive coding and maintenance needs. With SnapLogic eXtreme, we automate those processes and allow trailblazing companies to future proof their AI, ML, IoT or any other competitive advantages big data gives them."

As companies see increased business benefits from big data they are creating or migrating their big data architecture to the cloud to take advantage of tremendous operational cost savings, nearly limitless data processing power and instant scaling options the cloud provides. In the report, "Move Your Big Data Into The Public Cloud: You Won't Be Able To Keep Up With Customers If You Don't," Forrester notes, "Exponential reductions in cost and increases in analytics power are planting the seeds of disruption." But connecting cloud-based big data environments with diverse data sources, while also creating Apache Spark pipelines to transform that data, requires highly technical knowledge and continuous coding resources from data engineers and core IT groups that result in prohibitive operational costs and longer time-to-value.

Now data engineers can use SnapLogic eXtreme to lower the prohibitive cost and resource requirements many companies face when building and operating big data architectures in the cloud. Via SnapLogic's graphical user interface, data engineers can land data in storage services like Amazon S3 or Azure Data Lake Store using over 450 pre-built connectors, just with a couple of clicks. The engineers can then quickly create transformative Apache Spark pipelines with SnapLogic's ephemeral plex capabilities to more easily process large volumes of data from various endpoints using managed big data services like Amazon EMR, Microsoft HDInsights, or Google Cloud. This results in a substantially lowered barrier to creating cloud big data architectures for data engineers, while empowering companies with massive cloud compute power and cost efficiencies.   

"Our customers see tremendous potential in taking advantage of big data cloud services but they are struggling to overcome the time, expertise, and cost needed to use those services. As an early adopter of SnapLogic eXtreme, we are incredibly excited to partner with SnapLogic to help eliminate these barriers," said Raj Babu, CEO at Agilisium, a leading digital systems integrator.

To take advantage of SnapLogic eXtreme, customers can soon access this new big data solution from their existing Enterprise Integration Cloud platform. SnapLogic eXtreme is in beta this month, and will be generally available to all customers over the summer. To learn more about SnapLogic's solutions for cloud-based big data integrations, please visit snaplogic.com/big-data.

Published Wednesday, May 02, 2018 9:42 AM by David Marshall
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