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Accelerating time to value with a modern data integration approach

As C-suite and line-of-business leaders look for new ways to drive growth in the post-Covid business recovery era, they need to find faster and better ways to turn data into value.

There are rich opportunities for companies to integrate customer data from different data sets (transactional, marketing, customer service) to help gain a deeper understanding of customers and their preferences and to help create ‘hyper-personalized’ next best offers for them.

In addition, as organizations accelerate their adoption of SaaS and cloud platforms, technology leaders are increasingly utilizing cloud-managed data integration tools and approaches to expedite results and lower their costs.

Discover the key business and operational drivers behind ETL modernization initiatives, and understand how modern data integration platforms help technology leaders solve major business challenges.

Join experts from HMG Strategy, Bloor Research, and Gathr.ai to learn about:

Apache Spark: The new enterprise backbone for ETL, batch and real-time streaming

Despite investments in big data lakes, there is widespread use of expensive proprietary products for data ingestion, integration, and transformation (ETL) while bringing and processing data on the lake.

However, enterprises have successfully tested Apache Spark for its versatility and strengths as a distributed computing framework that can handle end-to-end needs for data processing, analytics, and machine learning workloads.

In this webinar, we will discuss why Apache Spark is a one stop shop for all data processing needs. We will also demo how a visual framework on top of Apache Spark makes it much more viable.

The following scenarios will be covered:

On-Prem

  • Data quality and ETL with Apache Spark using pre-built operators

  • Advanced monitoring of Spark pipelines

On Cloud

  • Visual interactive development of Apache Spark Structured Streaming pipelines

  • IoT use case with event-time, late-arrival and watermarks

  • Python based predictive analytics running on Spark

Streaming analytics for IoT with apache spark

Modern IoT operations can drive digital transformation by analyzing the unprecedented amounts of data generated from devices and sensors in real-time.

Apache Spark is a widely used stream processing engine for real-time IoT applications. Spark streaming offers a rich set of APIs in the areas of ingestion, cloud integration, multi-source joins, blending streams with static data, time-window aggregations, transformations, data cleansing, and strong support for machine learning and predictive analytics.

Join us in our webinar to learn about the rapid development and operationalization of real-time IoT applications covering an end-to-end flow of ingestion, insights, actions, and feedback.

Simplify spark-based ETL workflows on the cloud

Learn how you can visually design and manage Spark-based workflows by using Gathr.ai on popular cloud platforms like AWS, Azure, and Databricks.

ETL on the cloud is more relevant than ever, as most businesses now operate either partially or fully in the cloud. However, there’s a lack of solutions that can simplify and automate the complex cloud ETL process. Moreover, traditional tools are unable to leverage cloud-native services.

Gathr.ai offers the must-haves of a modern ETL solution and the ability to accelerate your shift to the cloud. With an intuitive visual interface, Gathr.ai simplifies building and running Spark-based ETL workflows on the cloud.

Join our webinar where you’ll learn about:

Ensure successful data ingestion on the cloud: Strategies for 2021

Despite investing heavily in infrastructure and solutioning, enterprises often struggle to efficiently process their data on the cloud and derive value from it. There is a critical need to simplify and de-risk cloud transitions and implement a versatile, scalable solution blueprint for large-scale deployments.

Our webinar outlines a 3-step strategy for building a robust analytics solution that empowers enterprise teams with clean and consumable data.

Unfolding the winning approach to a modern data management strategy

The business requirements of modern enterprises are evolving at a rapid pace, but legacy integration tools can’t keep up with the velocity, variety, and volume of data in the cloud-first world. Market-leading organizations need to intelligently manage and modernize their data stacks to advance their cloud analytics and warehousing initiatives.

Join our speakers, Forrester analyst Noel Yuhanna and in-house expert Amit Assudani, in our upcoming fireside chat to unravel an intelligent, future-proof data management strategy for modern enterprises. Take a walkthrough of business-specific use cases, covering a spectrum of modern data management solutions and learn how to adapt to the changing landscape of modern data stacks.

Don’t just migrate Modernize your legacy ETL

Legacy ETL platforms are not equipped to solve the complex business problems of real-time enterprises. High cost of ownership, slow workflows, and lack of scalability make them unsustainable in the cloud-first world.

For forward-looking organizations, ETL migration and modernization must start with a simple question – “where do I want to be”? Reimagining your new data stack is a critical part of the modernization journey.

Join our fireside chat featuring Forrester to discover how modern data management platforms can help you unlock new insights, reduce time-to-market, and fuel data-driven growth.

Drive innovation and growth with intelligent data pipelines

With massive amounts of data pouring in, there is a pressing need for businesses to swiftly derive mission-critical business insights. For this, you need a robust data architecture that supports multiple sources, enables processing at scale, and powers diverse analytics use cases. Learn why modern data management platforms like Gathr.ai are the pillars of a modern architecture and how they help improve business outcomes.