Archives: Resources Library

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Accelerating digital transformation with DataOps

To stay ahead of the curve in today’s competitive world, businesses need to streamline and fast-track their analytics processes. DataOps helps build and deliver trusted, consumption-ready data pipelines to analytics teams across the enterprise. Learn how DataOps uses Agile and DevOps best practices to automate and optimize data analytics capabilities.

Accelerate business initiatives by modernizing your data pipelines

To enable intelligent decision-making, modern enterprises need a holistic view of their enterprise data. This demands fast and easy access to data from multiple sources, including databases, applications, CRM, and ERP systems etc. Learn how modern data management platforms like Gathr.ai help extract large volumes of structured and unstructured data, apply complex transformations, and seamlessly load the transformed data into a data store.

Achieving hyper-personalization using modern DI & ML

3 on-ground examples of how marketers used data to achieve hyper-personalization and create an edge for their organization.

Hyper-personalization is the future of marketing. It helps brands differentiate themselves, increase customer engagement, and improve conversion. Unlike traditional personalization, hyper-personalization interprets customer intent based on behavioral and real-time data like browsing behavior, in-application behavior, and engagement data.

How can hyper-personalization empower marketers?
According to a report by Deloitte, 80% of customers are more likely to purchase from a company that offers personalized experiences, and well-executed hyper-personalization can deliver 8x ROI.

In this session, we share real-world examples of how brands use advanced analytics and machine learning to enable hyper-personalization seamlessly.

Powering next-best-action through advanced DI & ML

See how a Fortune 500 bank used advanced analytics to identify “next-best-action” for effective upselling and customer retention.

Customers are the new market makers, challenging traditional industry dynamics, and transforming how businesses compete. To determine the most relevant offer for each customer, enterprises need actionable insights on their past transactions, current interests, and potential needs.

In this session, we share on-ground example of how a leading bank used cutting-edge data integration and machine learning approaches to improve and scale their marketing efforts.

Build Spark-based ETL Workflows on Cloud in Minutes

Learn how you can visually design and manage Spark-based workflows on Amazon EMR and Databricks with Gathr.ai.
ETL on 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 on Amazon EMR or Databricks.

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

Join our upcoming webinar where you’ll learn about:

  • The must-haves of a modern ETL solution on cloud

  • How you can accelerate application development and minimize hand coding

  • How to deploy a cloud-based ETL application in minutes

  • Smarter ways to operationalize your ETL workflow

Anomaly Detection: Real World Scenarios, Approaches, and Live Implementation

Detecting anomalous patterns in real-time data can lead to significant actionable insights in a wide variety of application domains, such as fraud detection, network traffic management, predictive healthcare, energy monitoring and many more.

However, detecting anomalies accurately can be difficult. What qualifies as an anomaly is continuously changing and anomalous patterns are unexpected. An effective anomaly detection system needs to continuously self-learn without relying on pre-programmed thresholds.

Join our speakers Ravishankar Rao Vallabhajosyula, Senior Data Scientist, Gathr.ai and Saurabh Dutta, Technical Product Manager – Gathr.ai, in a discussion on:
 

  • Importance of anomaly detection in big data, types of anomalies, and challenges

  • Prominent real-time anomaly detection application areas

  • Approaches, techniques and algorithms for big data anomaly detection

Spark Streaming Made Easy!

Real-time streaming analytics and IoT seem to be the next big thing in the modern data management industry. As enterprises adopt Apache Spark and Spark Streaming widely, IT teams are facing the challenge to provide the tools and the framework needed to make Apache Spark Streaming an easy-to-use, robust, scalable and multi-tenant service.

Join this webinar from the Gathr.ai team to see how this problem is being solved at many Fortune 1000 companies.

This webinar will cover:

  • An overview of the stream processing landscape

  • The need for a “Streaming platform” integrated with the Hadoop data lake

  • A visual IDE approach for building applications on Spark Streaming

  • The usage of various Spark Streaming operators in sample applications – Spark SQL, Window, ML Lib, Join, Custom-Scala-code etc.

  • Real-time Dashboards, App Deployment & Monitoring

Self Service Pre-built Pipelines for Building Real-time Streaming Apps

Streaming analytics is fast becoming a must-have technology for enterprises seeking to gain competitive advantage from Data.

There is a growing demand for these new real-time applications and use-cases to be created and deployed quickly. In order to be efficient, enterprises need to take creative and collaborative approaches which maximize re-use.

Join this webinar by the Gathr.ai team of experts who are helping Fortune 1000 Enterprises implement real-time streaming analytics.

During this webinar session, you will get:

  • An overview of re-usable patterns in streaming analytics applications development

  • Introduction to an enterprise level streaming analytics strategy for 2017 and beyond

  • A visual IDE based approach to build, maintain and operate stream-processing applications

  • How to leverage multiple technologies like Apache Storm and Spark Streaming within a single real-time pipeline

  • A demo of A-B testing of predictive models and performing run-time model upgrades with no down-time

Real-time Data360 on Apache Spark

‘Data360’ is a new term and is being used to represent a one-stop shop for all your Big data processing needs.

Enterprise IT teams are faced with the challenge of choosing one vendor for data ingest, another for data wrangling, a third one for machine learning/analytics and yet another for visualization.

Shouldn’t it be really easy to do all the modern data management in a unified way, especially if you have already chosen to go with Spark as your Big Data platform? It can be; however the powerful usage of Spark still needs very skilled Scala/Java programmers. A different approach is needed.

During this webinar you will get to know about:

  • A powerful all-in-one Apache Spark strategy for the enterprise and an implementation approach for end-to-end big data analytics processing

  • The elements of a real-time Data360 solution – Ingest, Cleanse, Transform, Blend, Analyze, Load and Visualize

  • A combination of tools and tactics used for Data360 on streaming and historical data, using Apache Spark and Apache Spark Streaming

  • How use cases like anomaly detection, customer 360, IoT and log analytics, fraud and security analytics and many more can be achieved using this approach