Data Sheets

Rearchitect your legacy workloads to AWS

Rearchitect your legacy workloads to AWS

As a modern cloud platform, AWS empowers enterprises with improved scalability, greater elasticity, and reduced costs. However, the journey of modernization can be time-consuming and error-prone.

Learn how our Automated Workload Transformation Solution de-risks the transformation of workloads onto AWS.

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StreamAnalytix Lite

StreamAnalytix Lite

StreamAnalytix Lite makes Spark easy and expands the base of users that can build Spark based applications. It offers an easy-to-use visual Integrated Development Environment (IDE) to build, deploy and manage Spark based enterprise grade applications.

StreamAnalytix Lite is engineered to run Spark on a single node with no core limits. It allows developing, validating and running applications in a controlled local environment with an option to scale out pipelines using the Enterprise version of StreamAnalytix.

Read the Data Sheet to learn more.

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StreamAnalytix Platform Overview

StreamAnalytix Platform Overview

StreamAnalytix™ enables enterprises to analyze and respond to events in real-time at big data scale using stream processing and machine learning. With support for Apache Storm and Apache Spark in both batch and streaming modes, it is currently the industry’s only multi-engine product that provides a visual developer studio for end-end data processing support: ingest, cleanse, blend, enrich, analyze, load and real-time visualization. It includes a run-time DevOps platform for application monitoring and performance management.

This data sheet will provide you a snapshot of:

  • Industry’s only product that provides the powerful advantage of a visual integrated development environment (IDE) with end-to-end Data 360 support
  • Business benefits that enterprises can derive with StreamAnalytix
  • A detailed view into the StreamAnalytix architecture
  • Read the Data Sheet to learn more.

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Transform Your Legacy Workloads to Azure.

Transform Your Legacy Workloads to Azure.

Are you looking to transform your ETL workloads from legacy data warehouses to the cloud?

What if you could do it through an automated approach, with a 75% reduction in time-to-production and 50% cost savings?

Learn how you can automate and de-risk your workload transformation to Azure with the Impetus Workload Transformation Solution.

Read the data sheet here.

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Automated SAS Workload Transformation to the Cloud

Automated SAS Workload Transformation to the Cloud

Is your organization looking to reduce its dependence on the SAS platform?

Offloading your SAS workloads to a distributed processing paradigm will help you cut costs while simultaneously establishing a vendor agnostic, easily scalable analytics platform. But the manual migration process can be long, complicated, and risky.

What if there’s a solution to automate and accelerate your transition from SAS to the cloud?

Read this solution brief to find out how you can avoid the tedious manual migration process by adopting an automated, accelerated approach.

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Ab Initio ETL Transformation to Cloud

Ab Initio ETL Transformation to Cloud

Out-of-the-box ETL transformation accelerators

Need to transform your ETL workloads from legacy data warehouses to big data and cloud? It can be complicated, tedious, and error-prone.

What if you could automatically transform the data model, security model, and data to the target big data platform?

Learn how you can transform your Ab Initio ETL graphs to big data on cloud, on-premise or hybrid automatically.

Read the product brief here.

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Transform Your Warehouse

Transform Your Warehouse

Automated Transformation to Big Data

Need to transform your legacy data warehouse (EDW) to a modern enterprise data lake? It can be complicated, time-consuming, error-prone and expensive. What if you could automate your transformation?

Now you can. Learn how you can take on your workload transformation challenges using a proven, repeatable data transformation strategy that is simple, fast, affordable and automated.

Read the product brief here.

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