Smart Partitioning with Apache Apex

DataTorrent

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.
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Spotlight

With respect to data quality, many factors come into play. Raw data from claims or from an EMR database are not suitable for analysis. Turning raw data into usable information requires preparation, including normali-zation and validation. Only then can an organization gain trustworthy insights from the information and put it to use in maximizing patient care, reducing risk and strengthening a business’s bottom line. While the concept of data quality is widely accepted, most health care organizations define “good data” in different ways. One common thread, however, is the overwhelming need to gather and analyze information from one end of the spectrum to the other.


OTHER ON-DEMAND WEBINARS

The Emergence of Converged Data Platforms and the Role of In-Memory Computing

GridGain Systems

Organizations today typically have heterogeneous IT infrastructures which include a variety of database technologies and a wide variety of applications drawing on that data. Many organizations are dealing with massive and rapidly growing amounts of data with end users requiring immediate access to this real-time big data for both transactional and BI applications. Technology providers are responding to these evolving needs by moving towards a converged data platform which includes:In this webinar, Matt Aslett from 451 Research will discuss the drivers behind the need for a converged data platform and the current state of the evolution of these solutions.
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ENRICH A 360-DEGREE CUSTOMER VIEW WITH APACHE HADOOP AND SPLUNK

Hortonworks

Attend this webinar with Splunk and Hortonworks and see examples of how marketing, business and operations analysts can reach across disparate data sets in Hadoop to spot new opportunities for up-sell and cross-sell. We’ll also cover examples of how to measure buyer sentiment and changes in buyer behavior. Along with best practices on how to use data in Hadoop with Splunk to assign customer influence scores that online, call-center, and retail brances can use to customize more compelling products and promotions.
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Harnessing 2017’s biggest database trends

451 Research UpDown HCI

Recent years have seen an explosion in the number of different platforms and approaches used to store, process and analyze data in multiple formats from multiple sources. An abundance of data platforms (relational and non-relational databases, NoSQL, NewSQL, Hadoop, database as a service, etc.) has created a complex data management landscape that relies on the integration of multiple interdependent platforms and analysis tools. This trend is expected to continue in 2017. This webinar will preview how the database market is expected to change and what database professionals can do to use these changes to their advantage.
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Insights into Clinical and Business Intelligence at Mayo Clinic

HIMSS

This presentation provides an overview of and insights into clinical and business intelligence at Mayo Clinic. Chief Information Officer, Cris Ross, shares how Mayo Clinic applies data and analytics to support daily operations of this large integrated academic medical organization, and how data and analytics are used for the discovery, translation and application of medical insights to clinical practice. Mr. Ross explores new frontiers in predictive analytics, machine learning, and artificial intelligence.
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Spotlight

With respect to data quality, many factors come into play. Raw data from claims or from an EMR database are not suitable for analysis. Turning raw data into usable information requires preparation, including normali-zation and validation. Only then can an organization gain trustworthy insights from the information and put it to use in maximizing patient care, reducing risk and strengthening a business’s bottom line. While the concept of data quality is widely accepted, most health care organizations define “good data” in different ways. One common thread, however, is the overwhelming need to gather and analyze information from one end of the spectrum to the other.

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