Taneja Group | HP+Haven
Join Newsletter
Forgot
password?
Register
Trusted Business Advisors, Expert Technology Analysts

Items Tagged: HP+Haven

news

Data lakes swim with golden information for analytics

First we had data. Then we had big data. Now we have data lakes. Will the murky depths prove bountiful?

  • Premiered: 04/14/15
  • Author: Mike Matchett
  • Published: TechTarget: Search Data Center
Topic(s): TBA data lake TBA analytics TBA Big Data TBA Mike Matchett TBA TechTarget TBA Hadoop TBA Business Intelligence TBA BI TBA OLAP TBA OLTP TBA NoSQL TBA SQL TBA Optimization TBA ETL TBA IoT TBA Internet of Things TBA MapR TBA Project Myriad TBA YARN TBA Virtualization TBA Business Continuity TBA Disaster Recovery TBA DR TBA BC TBA data swamp TBA BlueData TBA Dataguise TBA HDFS TBA Hadoop Distributed File System TBA IBM
news

Navigate data lakes to manage big data

While the data lake concept appeals to business today, IT administrators must exercise caution prior to a full-scale implementation.

  • Premiered: 06/05/15
  • Author: Mike Matchett
  • Published: TechTarget: Search Storage
Topic(s): TBA data lake TBA Storage TBA Big Data TBA storage infrastructure TBA Data protection TBA big data lake TBA analysis TBA HDFS TBA Hadoop TBA Hadoop virtualization TBA Virtualization TBA Hadoop Distributed File System TBA software-defined TBA software-defined storage TBA BI TBA Business Intelligence TBA Disaster Recovery TBA Business Continuity TBA BC TBA DR TBA analytics TBA Spark TBA HP TBA Vertica TBA HP Haven TBA Haven TBA OLAP TBA data-aware
news

Big data analytics applications impact storage systems

Analytics applications for big data have placed extensive demands on storage systems, which Mike Matchett says often requires new or modified storage structures.

  • Premiered: 09/03/15
  • Author: Mike Matchett
  • Published: TechTarget: Search Storage
Topic(s): TBA Mike Matchett TBA Big Data TBA analytics TBA Storage TBA Primary Storage TBA scalability TBA Business Intelligence TBA BI TBA AWS TBA Amazon AWS TBA S3 TBA HPC TBA High Performance Computing TBA High Performance TBA ETL TBA HP Haven TBA HP TBA Hadoop TBA Vertica TBA convergence TBA converged TBA IOPS TBA Capacity TBA latency TBA scale-out TBA software-defined TBA software-defined storage TBA SDS TBA YARN TBA Spark
Profiles/Reports

HP Converges to Mine Big Value from Big Data

The promise of Big Data is engaging the imagination of corporations everywhere, even before looking to big data solutions to help handle the accelerated pressures of proliferating new data sources or in managing tremendously increasing amounts of raw and unstructured data. Corporations have long been highly competitive about analytically extracting value from their structured transactional data streams, but are now trying to competitively differentiate with new big data applications that span multiple kinds of data types, run in business interactive timeframes, and deliver more operational-focused (even transactional) values based on multiple types of processing.

This has led to some major re-thinking about the best approach, or journey, to success with Big Data. As mainstream enterprises are learning how and where their inevitable Big Data opportunities lie (and they all have them – ignoring them is simply not a viable strategy), they are also finding that wholesale adoption of a completely open source approach can lead to many unexpected pitfalls, like data islands, batch-analytical timeframes, multiplying scope, and constrained application value. Most of all, IT simply cannot completely halt existing processes and overnight transition to a different core business model or data platform.

But big data is already here. Companies must figure out how to process different kinds of data, stay on top of their big data “deluge”, remain agile, mine value, and yet hopefully leverage existing staff, resources and analytical investments. Some of the important questions include:

1.How to build the really exciting and valuable applications that contain multiple analytical and machine learning processing across multiple big data types?

2.How to avoid setting up two, three, or more parallel environments that require many copies of big data, complex dataflows and far too many new highly skilled experts?

We find that HP Haven presents an intriguing, proven, and enterprise-ready approach by converging structured, unstructured, machine-generated and other kinds of analytical solutions, many already proven world-class existing solutions on their own, into a single big data processing platform. This enables leveraging existing data, applications and existing experts while offering opportunities to analyze data sets in multiple ways. With this solution it’s possible to build applications that can take advantage of multiple data sources, multiple proven solutions, and easily “mash-up” whatever might be envisioned. However, the HP Haven approach doesn’t force a monolithic adoption but rather can be deployed and built-up as a customer’s big data journey progresses.

To help understand the IT challenges of big data and explore this new kind of enterprise data center platform opportunity, we’ve created this special vendor spotlight report. We start with a significant extract from the premium Taneja Group Enterprise Hadoop Infrastructure Market Landscape report to help understand the larger Hadoop market perspective. Then within that context we will review the HP Haven solution for Big Data and look at how it addresses key challenges while presenting a platform on which enterprises can develop their new big data opportunities.

Publish date: 03/16/15