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Category: XOps

Gathr Named in The Forrester Wave™: Streaming Analytics, Q2, 2021

Posted on by Saurabh Singhal

We’re thrilled – Impetus’ all-in-one data pipeline platform, Gathr (formerly known as StreamAnalytix) has been named as a contender by Forrester in its latest report, The Forrester Wave™: Streaming Analytics Q2, 2021. Gathr is among the 14 most significant streaming analytics providers to be featured.

Describing Gathr, the report states, “Gathr is an end-to-end, unified data platform that handles ingestion, integration/ETL (extract, transform, load), streaming analytics, and machine learning. Gathr offers strengths in usability, data connectors, tools, and extensibility. Enterprises currently using or wishing to use Apache Spark for streaming analytics should strongly consider Gathr because it adds tooling and enterprise features that are starkly missing from the open source platform.”

The Forrester Wave™ report is perhaps the most reputable and comprehensively evaluated market insight report focused on streaming analytics today, making it even more exciting. The report recommends evaluating Gathr to enterprises that need a single visual platform for leveraging popular open source, big data platforms for streaming ETL and advanced analytics. It validates our strong position as a unified, cloud-native, no-code data pipeline platform that meets enterprises’ end-to-end data integration and engineering needs.

5 must-haves in a modern data pipeline tool

Posted on by Saurabh Singhal

In the digital world, enterprises leverage data pipelines to move, transform, and store massive volumes of data. These pipelines form the backbone of business analytics and play a strategic role in enabling data-driven decision-making. Simple data pipeline use cases include ingesting data from multiple sources and delivering to a data warehouse/data lake on the cloud. More complex use cases involve building custom applications, training datasets for machine learning, and more. With datasets growing in size and complexity, modern data pipeline tools play a major role in helping enterprises reduce time to insight and gain a competitive advantage. Here’s our take on what to look for in a next-gen data pipeline platform:

This year’s top three trends for data and analytics leaders

Posted on by Saurabh Singhal

The unprecedented events of 2020 have profoundly impacted and accelerated technology trends across the world. COVID-19 brought digital transformation center stage, driving organizations to redefine their digital strategies across the enterprise at breakneck speed. Business models underwent a sea change, with secondary channels for sales, distribution, and customer engagement becoming primary. From off-price retailers going online overnight to apparel and tech giants offering curbside and in-store pick-ups — the market landscape saw tectonic shifts.

The surge in digital transformation has been accompanied by an ever-increasing appetite for data and data-driven insights, which, in turn, is impacting the responsibilities of data and analytics teams in a big way. Here are three important trends that will shape the data landscape in 2021:

Exploring MLOps? Here’s a Brief Introduction

Posted on by Saurabh Singhal

In the last few years, whether it’s about fraud detection in financial institutions, product recommendations in e-commerce websites, preventive maintenance in manufacturing and supply chain, or enabling cars with self-driving capabilities, ML-based applications have made an impact everywhere. The availability of vast volumes of data sets for training machine learning (ML) models, on-demand computing power, and increased collaboration between data scientists and developers have made ML-based applications ubiquitous. It has also made organizations realize that the processes driving the implementation of machine learning in the business space now need to be more production-oriented, instead of being just research-oriented. The need for higher efficiency, stability and end-to-end visibility and control has led to an increased interest in Machine Learning Operations or MLOps.

Key considerations for moving ETL workloads and enabling self-service ETL on cloud

Posted on by Saurabh Singhal

The exponential growth of data across industries is fuelling the evolution of extract, transform, and load (ETL) processes. However, traditional on-premise environments are inadequate to support the ever-increasing data volumes due to high operational costs, rigid storage capacity, and limited infrastructure. The cloud, on the other hand, is fast transforming the way enterprises discover, mine, store, and analyze data.

By 2021, over 75% of mid-size and large organizations will have adopted a multi-cloud or hybrid IT strategy. – Gartner

Modernize your ETL processes with Gathr

Posted on by Saurabh Singhal

Businesses are struggling with huge volumes of data to solve complex business problems while relying on their legacy data platform infrastructure. However, traditional ETL tools that were designed two decades ago are not equipped to solve the business problems of 2020.

Challenges with traditional ETL Tools:

  • Costly
  • Non-flexible
  • Non-scalable
  • Built for on-premise
  • Cannot transform before landing

To address these challenges, enterprises are looking to transform their ETL workloads from legacy data warehouses to the cloud. The trend has gathered momentum in recent times. According to Forbes, 80% of the data warehouse tools used by organizations are now cloud-based versus on-premise.

Modern ETL tools have evolved as an obvious choice as they come packed with features to extract value from huge datasets. These tools offer the following advantages:

  • Performant
  • Resilient
  • Scalable
  • Intuitive
  • Secure and compliant
  • End-to-end data processing and analytics

Boosting customer experience with real-time streaming analytics in the travel industry

Posted on by Saurabh Singhal

A large US-based airline use case
A recent study by Harvard Business Review revealed that 60% of enterprise business leaders believe real-time customer analytics is crucial to provide personalization at scale. According to the study, the number is expected to increase to 79% by 2020.

As mobile-first becomes the driving force for customer experiences, airlines are battling it out to make every customer journey personalized and customized in real-time. With every customer choosing different channels to interact at different points in their engagement journey, the challenge for airlines is to tap all these points of interaction to create an experience that’s personalized and relevant.

As passenger data becomes more readily available, airlines can have a granular insight into individual travelers to create tailored services for specific groups. Real-time use of this data can boost revenues, improve customer satisfaction, and enable proactive resolution of issues raised with the contact center.

Did you know?

  • 36% of consumers are willing to pay more for personalized experiences
  • Nearly 60% of consumers believe that their travel experience should deploy the use of AI and base their search results on past behaviors and personal preferences
  • 50% of global travelers say that personalized suggestions for destinations and things to do encourage them to book a trip

Real-time analysis of weather impact on New York City taxi trips in minutes

Posted on by Saurabh Singhal

In this post, we will see how easy it is read data from a streaming source, apply data transformations, enrich data with external data sources and create real-time alerts in minutes with Gathr.

We will use the drag and drop interface and self-service features of Gathr to build a streaming pipeline (image 1) to analyze the impact of weather conditions on New York City taxi trips. This pipeline can be accessed and run on Gathr.

We will analyze two aspects; impact of weather conditions on the taxi trip (time taken to pick-up and drop-off the rider in co-relation to distance traveled), and the mode used to make payments (cash or card) to create alerts for cash payments beyond a set threshold.

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