Deploying Mission Critical Applications on Hadoop, On-premises and in the Cloud

WANdisco

Global enterprises have quietly funneled enormous amounts of data into Hadoop over the last several years. Hadoop has transformed the way organizations deal with big data. By making vast quantities of rich unstructured and semi-structured data quickly and cheaply accessible, Hadoop has opened up a host of analytic capabilities that were never possible before, to drive business value.The challenges have revolved around operationalizing Hadoop to enterprise standards, and leveraging cloud-based Hadoop as a service (HaaS) options offering a vast array of analytics applications and processing capacity that would be impossible to deploy and maintain in-house.
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Spotlight

Hospitals and health systems are pursuing predictive analytics to transform patient care, but face di culty predicting future outcomes due to challenges with interoperability and talent. That’s according to a 2018 survey of health care business and IT leaders conducted by HIMSS Media for the Center for Connected Medicine. HERE’S WHERE THEY STAND: DATA ANALYTICS USE. 69% More effective using data to describe past health events.


OTHER ON-DEMAND WEBINARS

REAL TIME DB2 TO BIG DATA

A key requirement of data analytics is that the information being mined should be as current as possible. Companies who operate against day old (or more) data are at a serious disadvantage to those who leverage current information.

Big Data Drives the Need for a Strategic Information Management Approach

Organizations know the potential business benefits of big data, but many are struggling to get started. In addition, the volume, variety, velocity and complexity of today's data have forced organizations to reconsider their entire data management approach and infrastructure.

Western Digital Tech Talk - Hadoop® Compatible File System – HCFS w/ Objectstore

Western Digital

Technical deep dive of Hadoop® Compatible File System. Running HCFS with ActiveScale™ Object StoreThe current 1.0 runtime specification specifies the filesystem as a specific version of HDFS (2.7.x) which, beyond not meeting the ultimate goal of ODPi, is highly restrictive for hadoop distributions that choose alternative filesystems, such as S3, MapR filesystem, Isilon, and GPFS (to name a few).

Choosing the Right Database for Time Series Data

MemSQL

In this webinar, MemSQL Product Marketing Manager Mike Boyarski describes the growth in popularity of time series data and talks about the best options for a time series database, including a live Q&A. You can view the webinar and download the slides here. Here at MemSQL, we’ve had a lot of interest in our blog posts on time series data and choosing a time series database, as well as our O’Reilly time series ebook download. However, this webinar does a particularly good job of explaining what you would want in a time series database, and how that fits with MemSQL. We encourage you to read this blog post, then view the webinar.

Spotlight

Hospitals and health systems are pursuing predictive analytics to transform patient care, but face di culty predicting future outcomes due to challenges with interoperability and talent. That’s according to a 2018 survey of health care business and IT leaders conducted by HIMSS Media for the Center for Connected Medicine. HERE’S WHERE THEY STAND: DATA ANALYTICS USE. 69% More effective using data to describe past health events.

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