Hi [[ session.user.profile.firstName ]]

DataTorrent

  • Date
  • Rating
  • Views
  • Top 3 RFP Criteria for Streaming Big Data Top 3 RFP Criteria for Streaming Big Data Teddy Rusli, Senior Product Manager at DataTorrent Recorded: Nov 30 2016 30 mins
    Enterprises need a reliable streaming analytics engine that can graduate from a lab project to going into a production application.

    Learn top 3 RFP criteria you need when you evaluate a streaming engine for your enterprise.

    Teddy Rusli, Senior Product Manager at DataTorrent, has vast experience in different aspects and roles in bringing analytics to enterprises.
  • Data in Motion: It All Starts With Ingestion Part 2 Data in Motion: It All Starts With Ingestion Part 2 Gordon Hung, Senior Account Executive at DataTorrent Recorded: Aug 25 2016 11 mins
    Ingesting data into Hadoop is a frustrating, time-consuming activity. Further, the growth of data has created immense challenges that are not met by traditional legacy systems. Not only do you have to ingest structured data but unstructured data as well - at scale. Also, this ingestion needs to happen 24x7, never go down nor lose data.

    Having a simplified big data application that collects, aggregates and moves volumes of data to and from Hadoop is necessary for an efficient data processing pipeline.
  • Data in Motion: It All Starts With Ingestion Data in Motion: It All Starts With Ingestion Gordon Hung, Account Executive at DataTorrent Recorded: Jul 27 2016 16 mins
    Ingesting and extracting data from Hadoop can be a frustrating, time consuming activity for many enterprises. DataTorrent Data Ingestion is a standalone big data application that simplifies the collection, aggregation and movement of large amounts of data to and from Hadoop for a more efficient data processing pipeline. DataTorrent Data
    Ingestion makes configuring and running Hadoop data ingestion and data extraction a point and click process enabling a smooth, easy path to your Hadoop-based big data project.
  • Harnessing Value from Data in Motion in Real-Time Harnessing Value from Data in Motion in Real-Time Mike Gualtieri, Principal Analyst at Forrester. Larry Neumann, SVP of Marketing at Solace Systems. Recorded: Jul 20 2016 53 mins
    Today, most enterprises perform analytics on data at rest resulting in slow, outdated insights and untimely decisions. However, in today’s hyper-connected digital world where speed and real-time decision making really matters, enterprises need the ability to capture and act on moving data streams aka data in motion in real-time.

    Join guest speakers Mike Gualtieri, Principal Analyst at Forrester and Larry Neumann, SVP of Marketing at Solace System to learn how enterprises prepare for and use real-time streaming analytics platforms
    to capture, analyze, and act on data in motion at the very moment that data is created.

    Key agenda items:

    - Trends in big data and fast data

    - Why enterprises need to have a data in motion strategy

    - A primer on real-time streaming analytics technology – how streaming analytics is different

    - How your infrastructure requirements change for data in motion vs. data at rest

    - Architecture considerations on selecting real-time streaming analytics platform for your data and application needs

    - Real customer use cases of finding insights from data in motion and building next gen apps with DataTorrent's real-time streaming analytics platform
  • 360° Real-Time Business Insights with Native Hadoop Big Data Platform 360° Real-Time Business Insights with Native Hadoop Big Data Platform Teddy Rusli, Senior Product Manager; Ian Gomez, Audience Marketing Manager at DataTorrent Recorded: Jun 16 2016 36 mins
    To achieve excellence in customer service, you will need to gain a thorough understanding of customer behaviors and usage patterns. Real-time streaming technology can be used to not only capture the customer data from various sources as it's being created but also delivers faster time to insights and action for an improved customer experience. In this webinar, we will demonstrate how DataTorrent’s real-time native Hadoop stream processing platform enables telco providers to conduct a detailed real-time analysis of Call Data Records (CDR) to obtain deeper visibility of customer usage patterns and customer service intelligence. Those real-time insights can then be leveraged by telco providers to enhance the customer centricity program, improve customer satisfaction and reduce customer churn.

    You will also learn how DataTorrent’s real-time analytics platform can help telco providers to:

    • Quickly ingest large amounts of Call Data Records
    • Perform forensics on dropped calls by zip code for a given region
    • Reduce customer wait times for service calls
    • Maximize average revenue per user (ARPU)
  • Architectural Comparison of Apache Apex and Spark Streaming Architectural Comparison of Apache Apex and Spark Streaming Thomas Weise, Co-Founder & Architect, PMC Member, Apache Apex. Recorded: Jun 8 2016 63 mins
    Apache Apex is a native Hadoop data-in-motion platform. In this presentation, we will discuss architectural differences between Apache Apex features with Spark Streaming. We will discuss how these differences effect use cases like ingestion, fast real-time analytics, data movement, ETL, fast batch, very low latency SLA, high throughput and large scale ingestion.

    We will cover fault tolerance, low latency, connectors to sources/destinations, smart partitioning, processing guarantees, computation and scheduling model, state management and dynamic changes. We will also discuss how these features affect time to market and total cost of ownership.
  • Smart Partitioning with Apache Apex Smart Partitioning with Apache Apex Pramod Immaneni, Architect; Thomas Weise, Architect & Co-founder at DataTorrent Recorded: May 19 2016 64 mins
    Processing big data often requires running the same computations parallelly in multiple processes or threads, called partitions, with each partition handling a subset of the data. This becomes all the more necessary when processing live data streams where maintaining SLA is paramount. Furthermore, multiple different computations make up an application and each of them may have different partitioning needs. Partitioning also needs to adapt to changing data rates, input sources and other application requirements like SLA.

    In this talk, we will introduce how Apache Apex, a distributed stream processing platform on Hadoop, handles partitioning. We will look at different partitioning schemes provided by Apex some of which are unique in this space. We will also look at how Apex does dynamic partitioning, a feature unique to and pioneered by Apex to handle varying data needs with examples. We will also talk about the different utilities and libraries that Apex provides for users to be able to affect their own custom partitioning.
  • Hadoop Ingestion Made Easy Hadoop Ingestion Made Easy Dr. Sandeep Deshmukh, Committer Apache Apex, DataTorrent Engineer Recorded: May 12 2016 54 mins
    Ingesting petabytes of data at scale in the native Hadoop environment encounters quite a few problems that need to be handled by a platform. Some of the known issues include handling of failure, parallel reading of the data and considering updates while the data is being ingested.

    This presentation will deep dive into ingesting unbounded file data into Hadoop using the Apache Apex platform.
  • Productization of Big Data Streaming Analytics Productization of Big Data Streaming Analytics Mike Gualtieri, Principal Analyst, Forrester Research. Amol Kekre, CTO & Co-Founder, DataTorrent. Recorded: Apr 18 2016 51 mins
    Most Hadoop projects fail. There is a need for a platform that focuses on operational success and time to market. Big Data streaming analytics is critical, and enterprises must succeed in operationalizing it.

    In this webinar, you will learn about Big Data streaming analytics and where the industry is heading.

    1. Scalability & Performance
    2. Analytical Operators & Connectors
    3. Feed Data Lakes
    4. Operable & Enterprise Ready
    5. Time to Market
  • IOT Ingestion & Analytics Using Apache Apex - A Native Hadoop Platform IOT Ingestion & Analytics Using Apache Apex - A Native Hadoop Platform Pramod Immaneni, PPMC Member & Architect at DataTorrent - Ian Gomez, Audience Marketing Manager at DataTorrent Recorded: Apr 6 2016 62 mins
    Internet of Things (IoT) devices are becoming more ubiquitous in consumer, business and industrial landscapes. They are being widely used in applications ranging from home automation to the industrial internet. They pose a unique challenge in terms of the volume of data they produce, and the velocity with which they produce it, and the variety of sources they need to handle. The challenge is to ingest and process this data at the speed at which it is being produced in a real-time and fault tolerant fashion. Apache Apex is an industrial grade, scalable and fault tolerant big data processing platform that runs natively on Hadoop. In this webinar, you will see how Apex is being used in IoT applications and also see how the enterprise features such as dimensional analytics, real-time dashboards and monitoring play a key role.