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  • Spark Structured Streaming on the Cloud: Introduction to Internals
    Spark Structured Streaming on the Cloud: Introduction to Internals Vikram Agrawal Recorded: Feb 7 2018 30 mins
    Register now to see the on-demand recording of this webinar.

    Apache Spark has been gaining steam, with rapidity, both in the headlines and in real-world adoption. Spark was developed in 2009, and open sourced in 2010. Since then, it has grown to become one of the largest open source communities in big data with over 200 contributors from more than 50 organizations. This open source analytics engine stands out for its ability to process large volumes of data significantly faster than contemporaries such as MapReduce, primarily owing to in-memory storage of data on its own processing framework. That being said, one of the top real-world industry use cases for Apache Spark is its ability to process ‘streaming data‘.

    With so much data being processed on a daily basis, it has become essential for companies to be able to stream and analyze it all in real time, and Spark Streaming has the capability to handle this extra workload. Some experts even theorize that Spark could become the go-to platform for stream-computing applications, no matter the type. The reason for this claim is that Spark Streaming unifies disparate data processing capabilities, allowing developers to use a single framework to accommodate all their processing needs. Among the general ways that Spark Streaming is being used by businesses today are Streaming ETL, Data Enrichment, Trigger Event Detection and Complex Session Analysis. In this webinar, we will cover an introduction, internals and industry use cases of ‘Structured Streaming in Spark’.

    Key Takeaways:
    - Understanding of Data Processing Architecture
    - Why and When to use Spark’s Structured Streaming
    - Spark’s Structured Streaming Programming Paradigm
    - Internals of Spark’s Structured Streaming
    - Spark Structured Streaming in the Real World – examples of how customers of Qubole use it
  • What's Ahead in Big Data and Analytics
    What's Ahead in Big Data and Analytics Paul Nelson, Leena Joshi, and Balaji Mohanam Recorded: Dec 12 2017 61 mins
    We have come a long way since the term "Big Data" swept the business world off its feet as the next frontier for innovation, competition and productivity. Hadoop, NoSQL and Spark have become members of the enterprise IT landscape, data lakes have evolved as a real strategy and migration to the cloud has accelerated across service and deployment models.

    On the road ahead, the demand for real-time analytics will continue to skyrocket alongside growth in IoT, machine learning, and cognitive applications. Meeting the speed and scalability requirements of these types of workloads requires more flexible and efficient data management processes – both on-premises and in the cloud. Flexible deployment and integration options will become a must-have for projects.

    Finally, the need for data governance and security is intensifying as businesses adopt new approaches to expand their data storage and access via data lakes and self-service analytics programs. As data, along with its sources and users, continues to proliferate, so do the risks and responsibilities of ensuring its quality and protection.

    Join us to watch the replay of "What's Ahead in Big Data and Analytics" to get real direction and practical advice on the challenges and opportunities to tackle in 2018.
  • Power your Big Data Infrastructure with Data Intelligence for Analytics and Data
    Power your Big Data Infrastructure with Data Intelligence for Analytics and Data Balaji Mohanam, Senior Product Manager, Qubole Recorded: Nov 15 2017 46 mins
    Discover the newly launched features in Qubole, powered by Data Intelligence, that automates mundane Data Model performance appraisal and simplifies Data Ops. This session will provide a detailed walkthrough of Qubole’s latest offering in Data Intelligence that includes Data Model insights and Recommendations including Partitioning, Formatting, and Sorting that helps optimize data models for improved performance and computing resources. In addition, learn about Qubole’s latest offering in self-service analytics and how it can improve analysts productivity by making data discovery easy through column and table name auto-suggestion and completion, and insights preview.
  • Fireside Chat: Lessons Learned from Facebook
    Fireside Chat: Lessons Learned from Facebook Ashish Thusoo, CEO/Co-Founder, Qubole & Horia Margarit, Resident Data Scientist, Qubole Recorded: Sep 22 2017 48 mins
    In the final session of Data Platforms Online 2017, Ashish Thusoo will offer some of his highlights from the week’s sessions, pick out some emerging themes and trends, and answer questions from the audience. Ashish built the original data team at Facebook, is a co-author of Apache Hive, co-author of “Building a Data Driven Enterprise with DataOps” and CEO of Qubole. He’ll be moderated by Horia Margarit, resident Data Scientist at Qubole. Get your questions ready for what will be a lively and entertaining discussion!
  • Big Data Trends & Oracle Cloud Infrastructure
    Big Data Trends & Oracle Cloud Infrastructure Andrew Reichman, Sr. Director of Cloud Strategy, Oracle Recorded: Sep 22 2017 36 mins
    Cloud has changed the game when it comes to data analytics. Previously, organizations had to lock themselves into a particular architecture and level of capacity for three to seven years and do all the lifting themselves. Cloud on the other hand allows them to experiment with different hardware and software options, get more of the solution as a service and scale up and down to meet project spikes and accelerate busy jobs at will. This makes it much more viable for any company to get the advantages of advanced analytics against large data sets, without an oversize IT staff or huge capital investments.

    Oracle cloud is specifically designed to help enterprises take advantage of cloud for data analytics—it offers massive non-variable performance, predictable low cost and broad choice of deployment and software options. Oracle and Qubole work together to deliver a new breed of data platform—capable of taming the scale, performance, cost and complexity issues associated with gaining business insight from data of all types.

    Watch this webinar to understand:
    - Summary of industry trends for big data on the cloud
    - How Oracle Cloud Infrastructure is optimized for big data workloads from a cost, performance and flexibility perspective
    - How Oracle Cloud Big Data solutions compare with on-premises and competing cloud options
  • Untangling the Cloud Services Hairball
    Untangling the Cloud Services Hairball James Curtis, Senior Analyst - Data Platforms & Analytics, 451 Research Recorded: Sep 22 2017 25 mins
    The question is not much whether to migrate to the cloud or not. That question has likely already been answered by many organizations and the answer is a resounding full steam ahead. But the start of the journey can be daunting especially with a lot of ‘as-a-service’ terminology floating around. Please join James Curtis, senior analyst at 451 Research, as he discusses not only some industry trends and what many organizations are doing but also a simplified approach to understanding cloud services and how that might best fit your organization. Because it’s not so much buyer beware; it’s more about buyer understand.
  • BS-free Data Science
    BS-free Data Science Aman Naimat, Senior Vice President, Technology, Demandbase Recorded: Sep 22 2017 45 mins
    There is a surge in hype around Artificial Intelligence. Startups are raising hundreds of millions of dollars by bedazzling investors with Deep Learning, word embeddings, and reinforcement learning. This is a distraction from the very real problems that data and AI can solve if done right. By working across dozens of machine learning problems that are live in the real world, I’ve worked out the most common problems encountered and recurring design patterns on how to solve real-world problems using AI as a tool. This talk will arm you with a perspective on how to get pragmatic solutions with AI today.
  • Get "Datopia": Transforming to an Agile Data Culture
    Get "Datopia": Transforming to an Agile Data Culture Tripp Smith, Chief Technical Officer, Clarity Recorded: Sep 22 2017 35 mins
    This webinar is focused on:
    - Increasing collaborative friction between engineers, analysts, and business
    - Process-driven iteration i.e. balancing agility with discipline
    - Making the quantitative business case for moving from big bang to continuous enhancement (convincing your CFO/CIO to shift from CapEx to OpEx)
    - Case studies and outcomes from our clients
  • Modern Data Architecture with AWS
    Modern Data Architecture with AWS Pratap Ramamurthy, Partner Solution Architect, Amazon Web Services Recorded: Sep 21 2017 29 mins
    Today’s organizations are tasked with managing multiple data types, coming from a wide variety of sources. Faced with massive volumes and heterogeneous types of data, organizations are finding that in order to deliver insights in a timely manner, they need a data storage and analytics solution that offers more agility and flexibility than traditional data management systems. Every use case might be different and different use cases might need different tools. AWS provides a variety of options for your needs from RDS, EMR, Redshift, Athena and Quicksight. In this talk we will discuss the different technologies available on AWS and its application.
  • Amplifying Retail with Big Data and The Cloud
    Amplifying Retail with Big Data and The Cloud Carter Bradford, Senior Vice President, Precocity Recorded: Sep 21 2017 29 mins
    Once considered the "black magic" of digitally-born retailers like Amazon, personalizing the customer experience has now become table stakes for any retailer interested in surviving in the era of digital transformation. the techniques, tools and scalable platforms necessary to optimize customer interactions are now available and accessible for use by companies of any shape or size. We'll discuss how the use of big data technology in the cloud eases the implementation of common retail use cases as well as how it helps to avoid typical pitfalls.

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