Apache Spark is one of the most popular Big Data frameworks today. It is fast becoming the de facto technology choice for stream processing, real-time analytics, data science and machine learning applications at scale. It has moved well beyond the early-adopter phase, is supported by a vibrant open source community and is enjoying accelerated adoption in enterprises.
Join our guest speaker from Forrester Research, VP & Principal Analyst, Mike Gualtieri and Gathr.ai, Product Head, Anand Venugopal for a discussion on the trends and directions defining the growing importance of Apache Spark for stream processing, machine learning and other advanced data analytics applications.
The webinar will cover the following topics:
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What is driving Spark adoption? What are the influencers, trends, compelling capabilities and use cases?
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What are some of the challenges or inhibitors?
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Impetus to introduce Visual Spark Studio – a free, newly downloadable IDE that offers break-through productivity to learn, develop and deploy Spark based real-time and advanced analytical applications.
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Impetus customer success stories around real-time solutions with Spark/Gathr.ai.
The adoption of Apache Spark to analyze data in real-time is increasing with its ability to handle sophisticated analytical requirements and a common framework for streaming and batch. However, most organizations are also looking for “true streaming” features like lower latency and the ability to process out-of-order data.
Structured Streaming, a new high-level API, introduced in Apache Spark 2.0 promises these and other enhancements to the Spark approach to streaming data processing.
In this webinar, Anand Venugopal (Product Head) and other technical experts from Gathr.ai, will be speaking about the promising developments in Apache Spark 2.0 and how organizations can leverage structured streaming to make timely and accurate decisions and stay competitive.
In this webinar you will learn:
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Evolution of Spark and its functionality to date including version 2.2
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Structured Streaming – Technical overview, benefits and limitations
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How to integrate Structured Streaming with the surrounding stack
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Talent Vs Tooling
Streaming data ingestion, ETL, and integration along with real-time machine learning are becoming more and more critical capabilities as enterprises look to create and monetize the real-time enterprise.
In this webinar, our guest, Principal Analyst and VP at Forrester Research, Mike Gualtieri – Author of the Forrester Wave for Streaming Analytics and Anand Venugopal – Business Head of the Gathr.ai platform at Impetus Technologies will address the following topics:
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What is a “Streaming architecture” and how is it different from the traditional enterprise Architecture
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Why is it an absolute necessity for the real-time enterprise
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What are the top 10 attributes to look for in a streaming analytics platform/architecture
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A process for teams to discover and deliver business value from real-time data processing and analytics
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Success stories from some well-known large enterprises
Enterprises have realized that a consistent, complete, and real-time view of customers across systems and channels is the key to transforming the customer experience. However, achieving an accurate and real-time customer 360 view is a big challenge.
In our upcoming webinar, our experts will talk about how Apache Spark is becoming a de-facto engine to overcome challenges in building an accurate customer 360 view and to deliver compelling experiences in the moment.
Join this session to learn more about:
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What is “not” real-time customer 360
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Why you should invest in real-time customer 360 now
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How Apache Spark based architecture addresses the data challenges of real-time customer 360
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Live demo of implementing a customer 360 use case with Apache Spark
Most enterprises are undertaking a digital transformation initiative. Data and analytics modernization is an integral part of this journey. On-premise legacy systems like Hadoop clusters and data warehouses limit innovation and growth due to their old architectures.
New cloud-based platforms are becoming an inevitable consideration for many such enterprises. However, they are seeking to reduce the risk and complexity of manual migration from their conventional ETL tools and data lakes to a modern, future state.
The million $ question: How can enterprise IT teams avoid costly failures and delays when undertaking these transformation projects?
Impetus’ Gathr.ai and Databricks are helping enterprises with a successful pathway to the cloud with a unified data and analytics platform that serves BI, ML, and AI use-cases in one place.
In this upcoming webinar, we will demonstrate and deep dive into some key scenarios like:
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Visually migrate a traditional on-premise ETL workflow to the cloud (Azure) as Spark pipelines running on Databricks and Delta Lake
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Change-data-capture from relational database sources to the cloud on Databricks and Delta Lake via Gathr.ai
The session will conclude with a Q&A with our expert panelist.
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