Informatica’s New Cloud Data Integration Engine Promises Up to 5X Data Processing Speed


Enterprise cloud data management leader InformaticaOpens a new window recently took a big step towards data democratization by rolling out its new Spark-based Cloud Data Integration Engine. According to Informatica, the engine will empower users to operationalize data analytics and data science projects at scale to gain faster business-critical insights.

Informatica’s new offeringOpens a new window is powered by Apache Spark in-memory computing framework with graphical processor units (GPUs) from NVIDIA. The aim is to leverage an NVIDIA GPU’s inherent parallel processing capabilities that processes data as much as five times faster. The Redwood City-based software company has integrated RAPIDS Accelerator for Apache Spark with NVIDIA accelerated computing to support compute-intensive AI workloads.

According to Gartner, 41% of corporate IT employees are no longer just ‘end users’ and are involved in customizing datasets. Armed with this knowledge, Informatica has promptly tapped into this potentially profitable segment by improving the performance of compute-intensive AI and machine learning workload through NVIDIAOpens a new window GPU and software.

“Data science is the backbone of AI, as it is key to transforming oceans of enterprise data into business opportunities,” said Manuvir Das, Head of Enterprise Computing, NVIDIA. He added that this new offering will enable customers to speed their data science and AI pipelines across their cloud and on-premise data centers.

Learn More: Thoma Bravo To Buy Data Integration Leader Talend For $2.4B

Informatica’s new Cloud Data Integration Engine fundamentally changes the way data is processed, the company claims. It will allow users to access end-to-end machine learning operations (MLOps) capabilities by operationalizing machine learning models. Bringing Spark code and the GPUs has significantly made the process more efficient and economical.

CDI to Make Serverless Computing Simpler, Faster and Cost-Efficient

According to Informatica, Cloud Data Integration processes data faster and accelerates data democratization across the enterprise. The new product applies NVIDIA’s GPU acceleration capabilities to Informatica’s MLOps and DataOps workloads, and eliminates the need to write a code to invoke a GPU, thereby processing data up to 5X faster and at scale. The engine also enables customers to accelerate their data delivery, saving up to 72% in TCO.

The cloud data management leader also claims to have democratized GPU access to data consumers at large. The Cloud Data Integration Engine essentially wipes out complexity by converting simple mappings to sophisticated Spark code that can execute on GPUs at scale. The service will soon be available on Microsoft Azure and Google Cloud Platform.

The Cloud Data Integration offering also supports more than 3,000 metadata-aware connectors for various file types, including JSON, XML, logs, and clickstream data. The offering supports ETL and ELT workloads and features more than 100 prebuilt function templates for common data mappings and transformations.

Learn More: Top 4 Considerations for Choosing a Data Integration Tool for WFH World

This offering also makes Informatica the first cloud data management company to offer low cost, no/low-coding data access through serverless multi-cloud data management to citizen integrators, data engineers, machine learning engineers, and data scientists.

“Data democratization is the holy grail of digital transformation initiatives,” said Jitesh Ghai, Chief Product Officer, Informatica. “You can’t leverage the power of data and gain valuable insights if you are restricted in your data access. Our collaboration with NVIDIA is valuable to ensuring enterprise-scale data democratization, and narrowing the gap between the data-haves and the data-have-nots within the enterprise. 

“This important milestone with NVIDIA shows our continued commitment to unlock the value of data embedded in organizations across all levels. More importantly, it empower all key users to gain faster business-critical insights and operationalize data analytics and data science projects at scale.”

Let us know if you liked this news on LinkedInOpens a new window , TwitterOpens a new window , or FacebookOpens a new window . We would love to hear from you!