Published By: Teradata
Published Date: Jan 22, 2014
Today's agile businesses are seeking to expand analytics fast, gain flexible analytic deployment options and smooth cash flows. Download this paper to learn how Teradata understands these needs and has engineered a cloud solution that meets organizations' analytic needs.
Learn about FlexPod Select with Hadoop and how this enterprise class Hadoop has validated, pre-configured components that allow for faster deployment, higher reliability, and smoother integration with your existing applications and infrastructure.
Download this solutions guide to get a technical overview on building Hadoop on NetApp E-series storage and learn how it will effectively help deliver big analytics with pre-engineered, compatible, and supported solutions ultimately reducing the cost, schedule, and risk of do-it-yourself systems.
Learn why NetApp Open Solution for Hadoop is better than clusters built on commodity storage. This ESG lab report details the reasons why NetApp's use of direct attached storage for Hadoop improves performance, scalability and availability compared to typical internal hard drive Hadoop deployments.
Published By: SnowFlake
Published Date: Jul 08, 2016
In the era of big data, enterprise data warehouse (EDW) technology continues to evolve as vendors focus on innovation and advanced features around in-memory, compression, security, and tighter integration with Hadoop, NoSQL, and cloud. Forrester identified the 10 most significant EDW software and services providers — Actian, Amazon Web Services (AWS), Hewlett Packard Enterprise (HPE), IBM, Microsoft, Oracle, Pivotal Software, SAP, Snowflake Computing, and Teradata — in the category and researched, analyzed, and scored them. This report details our findings about how well each vendor fulfills our criteria and where they stand in relation to each other to help enterprise architect professionals select the right solution to support their data warehouse platform.
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. With the
right end-user tools, a data lake can enable the self-service data practices that both technical and business users need. These practices wring business value from big data, other new data sources, and burgeoning enterprise da
Published By: Pentaho
Published Date: Jan 16, 2015
This ebook is recommended for IT managers, developers, data analysts, system architects, and similar technical workers, who are faced with having to replace current systems and skills with the new set required by NoSQL and Hadoop, or those who want to deepen their understanding of complementary technologies and databases. Sponsored by Pentaho.
These traditional analytical systems are often based on a classic pattern where data from multiple operational systems is captured, cleaned, transformed and integrated before loading it into a data warehouse.
Different types of data have different data retention requirements. In establishing information governance and database archiving policies, take a holistic approach by understanding where the data exists, classifying the data, and archiving the data. IBM InfoSphere Optim™ Archive solution can help enterprises manage and support data retention policies by archiving historical data and storing that data in its original business context, all while controlling growing data volumes and improving application performance. This approach helps support long-term data retention by archiving data in a way that allows it to be accessed independently of the original application.
There is a lot of discussion in the press about Big Data. Big Data is traditionally defined in terms of the three V’s of Volume, Velocity, and Variety. In other words, Big Data is often characterized as high-volume, streaming, and including semi-structured and unstructured formats.
Healthcare organizations have produced enormous volumes of unstructured data, such as the notes by physicians and nurses in electronic medical records (EMRs). In addition, healthcare organizations produce streaming data, such as from patient monitoring devices. Now, thanks to emerging technologies such as
Hadoop and streams, healthcare organizations are in a position to harness this Big Data to reduce costs and improve patient outcomes. However, this Big Data has profound implications from an Information Governance perspective. In this white paper, we discuss Big Data Governance from the standpoint of three case studies.
With the advent of big data, organizations worldwide are attempting to use data and analytics to solve problems previously out of their reach. Many are applying big data and analytics to create competitive advantage within their markets, often focusing on building a thorough understanding of their customer base.
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.
Learn why advanced analytics tools are essential to sustain a competitive advantage. This white paper reveals seven strategic objectives that can be attained to their full potential only by employing predictive analytics.
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