hadoop

A Java application that will successfully be able to retrieve, insert & delete data from our database which will be implemented in HBase along with.Basically the idea is to provide much faster, safer method to transmit & receive huge amounts of data

When designed well, a data lake is an effective data-driven design pattern for capturing a wide range of data types, both old and new, at large scale. By definition, a data lake is optimized for the quick ingestion of raw, detailed source data plus on-the-fly processing of such data for exploration, analytics and operations. Even so, traditional, latent data practices are possible, too. Organizations are adopting the data lake design pattern (whether on Hadoop or a relational database) because lakes provision the kind of raw data that users need for data exploration and discovery-oriented forms of advanced analytics. A data lake can also be a consolidation point for both new and traditional data, thereby enabling analytics correlations across all data. To help users prepare, this TDWI Best Practices Report defines data lake types, then discusses their emerging best practices, enabling technologies and real-world applications. The report’s survey quantifies user trends and readiness f

Learn how CIOs can set up a system infrastructure for their business to get the best out of Big Data. Explore what the SAP HANA platform can do, how it integrates with Hadoop and related technologies, and the opportunities it offers to simplify your system landscape and significantly reduce cost of ownership.

In midsize and large organizations, critical business processing continues to depend on relational databases including Microsoft® SQL Server. While new tools like Hadoop help businesses analyze oceans of Big Data, conventional relational-database management systems (RDBMS) remain the backbone for online transaction processing (OLTP), online analytic processing (OLAP), and mixed OLTP/OLAP workloads.

This TDWI Best Practices report explains the benefits that Hadoop and Hadoop-based products can bring to organizations today, both for big data analytics and as complements to existing BI and data warehousing technologies.

This report explains the benefits that Hadoop and Hadoop-based products can bring to organizations today, both for big data analytics and as complements to existing BI and data warehousing technologies based on TDWI research plus survey responses from 325 data management professionals across 13 industries. It also covers Hadoop best practices and provides an overview of tools and platforms that integrate with Hadoop.

Although the phrase “next-generation platforms and analytics” can evoke images of machine learning, big data, Hadoop, and the Internet of things, most organizations are somewhere in between the technology vision and today’s reality of BI and dashboards. Next-generation platforms and analytics often mean simply pushing past reports and dashboards to more advanced forms of analytics, such as predictive analytics. Next-generation analytics might move your organization from visualization to big data visualization; from slicing and dicing data to predictive analytics; or to using more than just structured data for analysis.

This business-oriented white paper summarizes the wide-ranging benefits of the Hadoop platform, highlights common data processing use cases and explores examples of specific use cases in vertical industries. The information presented here draws on the collective experiences of three leaders in the use of Hadoop technologies—Dell and its partners Cloudera and Intel.

While the EDW plays an all-important role in the effort to leverage big data to drive business value, it is not without its challenges. In particular, the typical EDW is being pushed to its limits by the volume, velocity and variety of data. Download this whitepaper and see how the Dell™ | Cloudera™ | Syncsort™ Data Warehouse Optimization – ETL Offload Reference Architecture can help.

The information presented here draws on the collective experiences of three leaders in the use of Hadoop technologies—Dell® and its partners Cloudera® and Intel®.

This paper discusses how the many Dell | Cloudera Hadoop solutions help organizations of all sizes, and with a variety of needs and use cases, tackle their big data requirements.

In order to protect big data today, organizations must have solutions that address four key areas: authentication, authorization, audit and lineage, and compliant data protection.

To compete in this new multi-channel environment, we’ve seen in this guide how retailers have to adopt new and innovative strategies to attract and retain customers. Big data technologies, specifically Hadoop, enable retailers to connect with customers through multiple channels at an entirely new level by harnessing the vast volumes of new data available today. Hadoop helps retailers store, transform, integrate and analyze a wide variety of online and offline customer data—POS transactions, e-commerce transactions, clickstream data, email, social media, sensor data and call center records—all in one central repository.

Download this white paper to learn how the company deployed a Dell and Hadoop cluster based on Dell and Intel® technologies to support a new big data insight solution that gives clients a unified view of customer data.

This business-oriented white paper explains four options for starting your Hadoop journey. This paper also outlines the benefits of Hadoop and highlights some of the many use cases for this new approach to managing, storing and processing big data.

In a recent report, Aberdeen's research suggests that Hadoop usage could be a catalyst for an enhanced and well-rounded data strategy. Read on to find out more.

Download the white paper, A Highly-Tuned Hadoop Cluster, to learn how to add real-time optimization technology to performance tuning strategy to: • Monitor and control the actual use of (and demand for) each kind of hardware resource by task • Identify ‘holes’ in the cluster where a node could temporarily do more work • Eliminate the need to physically separate workloads to ensure performance

Download this whitepaper to learn how real-¬time cluster optimization technology can be used in multi¬-tenancy Hadoop environments to: • Eliminate the expense of having to physically isolate workloads • Enforce service level agreements (SLAs) based on customer¬ defined policies • Enable cluster usage tracking by job, task and user to speed troubleshooting and help with chargebacks

IBM InfoSphere Information Server connects to many new ‘at rest’ and streaming big data sources, scales natively on Hadoop using partition and pipeline parallelism, automates data profiling, provides a business glossary, and an information catalog, plus also supports IT.

Althrough Hadoop and related technologies have been with us for several years, most business intelligence (BI) professionals and their business counterparts still harbor a few misconceptions that need to be corrected about Hadoop and related technologies such as MapReduce. This webcast presents the 10 most common myths about Hadoop, then corrects them. The goal is to clarify what Hadoop is and does relative to BI, as well as in which business and technology situations Hadoop-based BI, data warehousing and analytics can be useful.

This TDWI Checklist Report discusses adjustments to DW architectures that real-world organizations are making today, so that Hadoop can help the DW environment satisfy new business requirements for big data management and big data analytics.

Hadoop is the rising star of the business technology agenda for a simple reason — it disrupts the economics of data, analytics, and someday soon, all enterprise applications; it is secretly becoming an application platform too. Application development and delivery (AD&D) professionals should be aware of and take action on these eight predictions, including the disruptive power of “Hadooponomics,” Hadoop’s current killer app, the closing data management gap, and the emergence of brand new distros.

Enterprise Hadoop Implementation with Teradata and Hortonworks

This report is about two of those architectures: Apache™ Hadoop® YARN and Teradata® Aster® Seamless Network Analytical Processing (SNAP) Framework™. In the report, each architecture is described; the use of each in a business problem is illustrated; and the results are compared.

It is hard for data and IT architects to understand what workloads should move, how to coordinate data movement and processing between systems, and how to integrate those systems to provide a broader and more flexible data platform. To better understand these topics, it is helpful to first understand what Hadoop and data warehouses were designed for and what uses were not originally intended as part of the design.