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Learn how you can take advantage of our customized cloud services, innovative automation levers, advanced monitoring tools, and security best practices to seamlessly manage mission-critical use cases across on-premise and cloud environments.
As a modern cloud platform, GCP empowers enterprises with improved scalability, greater elasticity, and reduced costs. However, the modernization journey can be time-consuming and error-prone.
Learn how our Automated Workload Transformation Solution de-risks the transformation of workloads onto GCP.
Transform the decision-making capabilities of your business with accelerated insights from all your data. Our end-to-end solution helps you build a single, integrated pipeline to transform, ingest, and consume trillions of rows of data at a speed that was impossible before. It can read data from multiple sources in real-time, cleanse and enrich it quickly, and make it available for instant, multidimensional analytics across hundreds of dimensions and measures. Advanced algorithms, machine learning capabilities, and an automated workflow ensure that your data is put to work as soon as it arrives, and you can leverage every bit of it to expand your business.
Download our solution brief to learn how you can:
Enterprises with significant investments in Teradata have been missing the cost and flexibility advantages of a modern big data architecture. To make the move, enterprises need to confront the complexity of converting Teradata workloads. Manual identification and transformation of EDW, ETL, analytical and reporting workloads is complicated, time-consuming, and error-prone.
Download our solution brief to learn how Impetus Teradata Workload Transformation Solution can simplify, automate and accelerate the ETL and EDW conversion process for your enterprise.
Continuous Integration and Delivery (CI/CD) is a set of automated SDLC practices and methods that enable frequent and error-free releases of change in code or data, with extensive visibility and traceability. Many enterprises are adopting an automated approach to accelerate and simplify the entire process of Extract, Transform, and Load (ETL).
StreamAnalytix is a self-service ETL and analytics tool that comprises of various features to support CI and CD. You can build production-grade continuous applications, which makes it easier to manage out-of-sync data, maintain greater consistency within data streams, and join streams with static data sources more efficiently.
This solution brief will provide you a snapshot of how to build, deploy, and deliver at high velocity with StreamAnalytix.
Read the solution brief to learn more.
Visually build and deploy streaming and batch processing use cases rapidly, with the best-of-breed open source technologies, both on-premise and in the cloud.
StreamAnalytix is a multi-engine, enterprise-grade, visual platform for unified streaming and batch data processing, and machine learning. Use compute engines like Apache Spark (and more) as the underlying technology to ingest, blend, and process high-velocity data streams as they arrive. Run machine learning models, train and refresh models in real-time or in batch mode, visualize results on real-time dashboards, and raise corresponding real-time alerts and action triggers.
Why StreamAnalytix?
Build and operationalize Apache Spark (and engines like Storm, Flink, and TensorFlow) based applications in the cloud 10x faster using an intuitive drag-and-drop interface, an exhaustive set of pre-built operators, full application lifecycle support, and one-click options for on-premise and cloud deployments.
Key features
To learn more, download the Solution Brief.
Enterprises moving to Snowflake can experience benefits such as full SQL support, serverless architecture, strong partnerships with BI and ETL tools, and ease of maintenance. However, moving workloads from legacy environment to cloud has its own complexities.
Find out how the Impetus Snowflake Workload Transformation Solution can help you address these challenges and ensure agility and elasticity while moving to Snowflake.
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.
To make the move to big data / cloud, you’ll need to confront the complexity of transforming Netezza warehouse workloads. Manual identification and transformation of EDW, ETL, analytical and reporting workloads is complicated, tedious, time-consuming, and error-prone. Read our solution brief to learn more about how we are simplifying that effort.
Data-driven decision making is changing the way businesses operate, and the data warehouse is at the core of an enterprise’s big data and analytics strategy. Existing data warehouses are neither easily scalable to accommodate exploding data volumes nor analytically flexible for business users and analysts.
As the traditional data warehouse falls short of today’s business requirements, there is a driving need to move to a cloud, on-premise, or hybrid big data warehouse environment. It is essential that we also eliminate the data silos that exist today and bring together enterprise-wide data to create a comprehensive single source of truth across the business.
This solution brief describes how you can:
