White Paper

Transforming Ab Initio ETL workloads to Spark or Cloud

Transforming Ab Initio ETL workloads to Spark or Cloud

Leading companies across sectors from telecommunications, insurance, and finance to healthcare, retail, e-commerce, and others have been using Ab Initio to solve the most complex data processing problems. However, advanced cloud platforms are now challenging Ab Initio’s popularity.

In this white paper, we will share why enterprises are moving from Ab Initio and share a roadmap for efficiently modernizing your Ab Initio ETL graphs to the cloud, on-premise, or a hybrid model automatically. We will also detail how the Impetus Workload Transformation Solution can securely, seamlessly, and automatically transform workloads, queries, and logic to the target cloud platform.

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EDW to Snowflake cloud data warehouse transformation: An automated approach

EDW to Snowflake cloud data warehouse transformation: An automated approach

Traditional data warehouses are constrained by performance, scalability, and elasticity issues. Enterprises are increasingly moving to the next-generation cloud data warehouses like Snowflake to reduce infrastructure administration overheads, achieve business agility, and enable uncompromising simplicity.

However, moving workloads from legacy environments to Snowflake is complicated and error-prone, if handled manually. Automation toolsets can effectively deal with the intricacies to make the migration streamlined, risk-free, and seamless.

In this white paper, we will detail the key considerations and challenges while migrating to Snowflake, and how the Impetus Workload Transformation Solution helps enterprises get there faster and in a cost-effective way.

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Getting Real Value from Real-Time Streaming Analytics

Getting Real Value from Real-Time Streaming Analytics

Enterprises and IoT applications can benefit immensely from real-time streaming analytics by visualizing the business in real-time, cutting preventable losses, detecting urgent situations and automating immediate actions.

Highlights:

  • What is real-time streaming analytics?
  • What are distinctive characteristics of real-time streaming analytics?
  • How real-time streaming analytics can add value to business?
  • Recommendations on design, approaches and implementation of real-time streaming analytics

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A Future-Proof Architecture for Streaming Data Analytics

A Future-Proof Architecture for Streaming Data Analytics

Are you ready to reap the benefits of streaming analytics, but are worried about investing in new technology that could rapidly be surpassed or become outdated?

Learn how to leverage the steady flow of advanced real-time streaming data analytics (RTSA) application development technologies and future-proof the solution.

Highlights:

  • Benefits of real- time streaming analytics (RTSA)
  • Architecture of an RTSA platform
  • How to future-proof an RTSA platform?

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The Build vs. Buy Decision: Exploring a Third Alternative

The Build vs. Buy Decision: Exploring a Third Alternative

As enterprises realize the power of real-time information, streaming analytics platforms are fast becoming a must-have technology for enterprises. This whitepaper explores the classic IT conundrum of build vs buy, specifically in terms of acquiring a real-time streaming analytics platform.

Highlights:

  • Pros and cons of doing-it-yourself against the option of buying a pre-built solution
  • Four categories of buying options
  • The Third Alternative: “The Best of Both Worlds”

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Real-Time Streaming Analytics

Real-Time Streaming Analytics

Here is a one stop source for all that you wanted to learn about real-time streaming analytics.

This comprehensive guide explores the genesis of streaming analytics and the business value of real-time streaming for enterprises for different use cases. The paper shares how businesses are selecting a streaming architecture along with a few case studies. It also provides an overview on StreamAnalytix- industry’s only multi-engine streaming analytics platform based on open source technology stack.

Read this guide to learn how you can implement and derive business value from real- time streaming analytics.

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Driving Business Value from Real-Time Streaming Analytics

Driving Business Value from Real-Time Streaming Analytics

Driving Business Value from Real-Time Streaming Analytics

Is your data value decreasing with each increment of delay, becoming less a force for rapid response than a resource for diagnostics and descriptive reports of the past?

Learn how enterprises are tapping into unstructured, real-time data sources that create a potential goldmine of information.

This white paper explores different real-time use cases of real-time streaming analytics across various industry verticals.

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Data Science for Real-Time Streaming Analytics

Data Science for Real-Time Streaming Analytics

Are you looking to extract business value in real-time from data in motion? Data analytics forms a crucial part of Real-time Streaming Analytics (RTSA) that allows for the collection, integration, analysis and visualization of data in real-time.

This white paper explores different data science models and methods for streaming analytics.

Highlights:

  • Types of data analytics
  • Batch vs. streaming data analytics
  • How to extract insights from data in motion
  • Offline vs. online modeling
  • Challenges posed by streaming analytics

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IoT: The Trigger for Streaming Analytics

IoT: The Trigger for Streaming Analytics

An Ovum white paper for Impetus StreamAnalytix

The Internet of Things (IoT) is becoming ubiquitous. More than just a curious trend, enterprises have a huge stake in harnessing IoT, as it could provide greater visibility into the operations and challenges that they already face.

While the IoT data helps enterprises improve their ability to solve the operational challenges and create new business opportunities, processing the data and using it to drive actionable strategy in real-time is a challenge. Low-cost streaming analytics plays a vital role interpreting IoT data.

This white paper explores different use cases for streaming analytics involving IoT spanning a wide range of industries.

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Top 6 Considerations for a Streaming Analytics Platform

Top 6 Considerations for a Streaming Analytics Platform

All data is first generated in real-time, be it financial transactions, sensor data, web clickstreams, geolocation data, weather reports, market data, social media, and other event streams. A real-time streaming analytics platform can help business gain insights from the high-velocity flow of data as they originate, optimize decisions, improve business insights, and accelerate responses to critical events.

This white paper explores the key considerations while choosing a real-time streaming analytics platform.

Highlights:

  • Visual low code development
  • Application lifecycle management
  • Support for ‘real-time’, ‘near real-time’, and batch processing
  • Technology agnostic and open source enabled

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