lake

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By: Teradata     Published Date: Jan 30, 2015
Our goal is to share best practices so you can understand how designing a data lake strategy can enhance and amplify existing investments and create new forms of business value.
Tags : data lake, data warehouse, enterprise data, migration, enterprise use, data lake strategy, business value, data management
     Teradata
By: Snowflake     Published Date: Apr 14, 2015
Learn why Snowflake is reinventing the data warehouse to meet the changing nature of data and its usage within the enterprise.
Tags : snowflake, data warehouse, cloud computing, big data
     Snowflake
By: Snowflake     Published Date: Apr 14, 2015
Six things that are critical to evaluate for data warehousing in the cloud.
Tags : snowflake, data warehousing, cloud computing, best practices, data processing
     Snowflake
By: CDW     Published Date: Apr 04, 2016
Read more to learn why Salt Lake Community College relies on APC’s power and cooling technology to keep its brand-new Center for Arts and Media data center humming.
Tags : best practices, business optimization, case study, technology
     CDW
By: CDW - APC     Published Date: Apr 07, 2016
Read more to learn why Salt Lake Community College relies on APC’s power and cooling technology to keep its brand-new Center for Arts and Media data center humming.
Tags : best practices, business optimization, case study, technology
     CDW - APC
By: Dell EMC     Published Date: Jun 29, 2016
EMC Isilon scale-out network-attached storage (NAS) is a simple and scalable platform to build a scale-out data lake and persist enterprise files of all sizes that scale from terabytes to petabytes in a single cluster. It enables you to consolidate storage silos, improve storage utilization, reduce costs, while providing you a future proofed platform to run today and tomorrow's workloads.
Tags : network, storage, data, best practices
     Dell EMC
By: Dell EMC     Published Date: Mar 18, 2016
EMC Isilon scale-out network-attached storage (NAS) is a simple and scalable platform to build out a scale-out data lake and persist enterprise files of all sizes that scale from terabytes to petabytes in a single cluster.
Tags : emc, data lake, emc isilon, network, storage, enterprise
     Dell EMC
By: Teradata     Published Date: May 01, 2015
Creating value in your enterprise undoubtedly creates competitive advantage. Making sense of the data that is pouring into the data lake, accelerating the value of the data, and being able to manage that data effectively is a game-changer. Michael Lang explores how to achieve this success in “Data Preparation in the Hadoop Data Lake.” Enterprises experiencing success with data preparation acknowledge its three essential competencies: structuring, exploring, and transforming. Teradata Loom offers a new approach by enabling enterprises to get value from the data lake with an interactive method for preparing big data incrementally and iteratively. As the first complete data management solution for Hadoop, Teradata Loom enables enterprises to benefit from better and faster insights from a continuous data science workflow, improving productivity and business value. To learn more about how Teradata Loom can help improve productivity in the Hadoop Data Lake, download this report now.
Tags : data management, productivity, hadoop, interactive, enterprise, enterprise applications
     Teradata
By: Attunity     Published Date: Nov 15, 2018
IT departments today face serious data integration hurdles when adopting and managing a Hadoop-based data lake. Many lack the ETL and Hadoop coding skills required to replicate data across these large environments. In this whitepaper, learn how you can provide automated Data Lake pipelines that accelerate and streamline your data lake ingestion efforts, enabling IT to deliver more data, ready for agile analytics, to the business.
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     Attunity
By: SnowFlake     Published Date: Jul 08, 2016
Data today comes from diverse sources in diverse forms and needs to be analyzed by ever more users as quickly as possible. Those demands are stressing the limitations of traditional data warehouses and data platforms. Snowflake has reinvented the data warehouse, making it possible to bring all your business data together in a single system that can support all your users and workloads. Built from the cloud up as a software service, Snowflake eliminates the cost, complexity, and inflexibility of existing solutions while allowing you to use the tools and skills you already have.
Tags : snowflake, data, technology, best practices, solutions, cloud support, storage
     SnowFlake
By: PolarLake USA     Published Date: Feb 23, 2008
A paper that looks at the promise of XML, Web Services and the Enterprise Service Bus as well as the various XML standards being developed for and used in the Financial Services industry.
Tags : polarlake, enterprise application integration, eai, reference data management, reference data distribution, xml based integration, financial integrations
     PolarLake USA
By: SAS     Published Date: Oct 18, 2017
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
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     SAS
By: SAS     Published Date: Apr 25, 2017
Organizations in pursuit of data-driven goals are seeking to extend and expand business intelligence (BI) and analytics to more users and functions. Users want to tap new data sources, including Hadoop files. However, organizations are feeling pain because as the data becomes more challenging, data preparation processes are getting longer, more complex, and more inefficient. They also demand too much IT involvement. New technology solutions and practices are providing alternatives that increase self-service data preparation, address inefficiencies, and make it easier to work with Hadoop data lakes. This report will examine organizations’ challenges with data preparation and discuss technologies and best practices for making improvements.
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     SAS
By: Amazon Web Services     Published Date: Jul 25, 2018
Defining the Data Lake “Big data” is an idea as much as a particular methodology or technology, yet it’s an idea that is enabling powerful insights, faster and better decisions, and even business transformations across many industries. In general, big data can be characterized as an approach to extracting insights from very large quantities of structured and unstructured data from varied sources at a speed that is immediate (enough) for the particular analytics use case.
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     Amazon Web Services
By: Dell EMC     Published Date: Mar 18, 2016
This white paper provides an introduction to the EMC Isilon scale-out data lake as the key enabler to store, manage, and protect unstructured data for traditional and emerging workloads. Business decision makers and architects can leverage the information provided here to make key strategy and implementation decisions for their storage infrastructure.
Tags : emc, emc isilon, data lake, storage, network, unstructured data
     Dell EMC
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.
Tags : forrester, enterprise, data, technology, best practices, innovation, security
     SnowFlake
By: IBM APAC     Published Date: Jul 09, 2017
Organizations today collect a tremendous amount of data and are bolstering their analytics capabilities to generate new, data-driven insights from this expanding resource. To make the most of growing data volumes, they need to provide rapid access to data across the enterprise. At the same time, they need efficient and workable ways to store and manage data over the long term. A governed data lake approach offers an opportunity to manage these challenges. Download this white paper to find out more.
Tags : data lake, big data, analytics
     IBM APAC
By: Snowflake     Published Date: Jan 25, 2018
"The forces that gave rise to data warehousing in the 1980s are just as important today. However, history reveals the benefits and drawbacks of the traditional data warehouse and how it falls short. This eBook explains how data warehousing has been re-thought and reborn in the cloud for the modern, data-driven organization."
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     Snowflake
By: Snowflake     Published Date: Apr 14, 2015
Find out why data professionals are NOT replacing their data warehouse with Hadoop—read the survey
Tags : dimensional research, data professionals, data warehouse, cloud computing, big data, new technology
     Snowflake
By: Amazon Web Services     Published Date: Feb 01, 2018
Moving Beyond Traditional Decision Support Future-proofing a business has never been more challenging. Customer preferences turn on a dime, and their expectations for service and support continue to rise. At the same time, the data lifeblood that flows through a typical organization is more vast, diverse, and complex than ever before. More companies today are looking to expand beyond traditional means of decision support, and are exploring how AI can help them find and manage the “unknown unknowns” in our fast-paced business environment.
Tags : predictive, analytics, data lake, infrastructure, natural language processing, amazon
     Amazon Web Services
By: Dell EMC     Published Date: Jun 29, 2016
Traditional DAS or Scale-out NAS for Hadoop Analytics? Here are our top 8 reasons to choose a Scale-Out Data Lake on EMC Isilon for Hadoop Analytics.
Tags : emc isilon, storage, best practices, data
     Dell EMC
By: Zaloni     Published Date: Apr 24, 2019
Why your data catalog won’t deliver significant ROI According to Gartner, organizations that provide access to a curated catalog of internal and external data assets will derive twice as much business value from their analytics investments by 2020 than those that do not. That’s a ringing endorsement of data catalogs, and a growing number of enterprises seem to agree. In fact, the global data catalog market is expected to grow from US$210.0 million in 2017 to US$620.0 million by 2022, at a Compound Annual Growth Rate (CAGR) of 24.2%. Why such large and intensifying demand for data catalogs? The primary driver is that many organizations are working to modernize their data platforms with data lakes, cloud-based data warehouses, advanced analytics and various SaaS applications in order to grow profitable digital initiatives. To support these digital initiatives and other business imperatives, organizations need more reliable, faster access to their data. However, modernizing data plat
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     Zaloni
By: Gigaom     Published Date: Sep 16, 2019
We’ve heard it before. A data warehouse is a place for formally-structured, highly-curated data, accommodating recurring business analyses, whereas data lakes are places for “raw” data, serving analytic workloads, experimental in nature. Since both conventional and experimental analysis is important in this data-driven era, we’re left with separate repositories, siloed data, and bifurcated skill sets. Or are we? In fact, less structured data can go into your warehouse, and since today’s data warehouses can leverage the same distributed file systems and cloud storage layers that host data lakes, the warehouse/lake distinction’s very premise is rapidly diminishing. In reality, business drivers and business outcomes demand that we abandon the false dichotomy and unify our data, our governance, our analysis, and our technology teams. Want to get this right? Then join us for a free 1-hour webinar from GigaOm Research. The webinar features GigaOm analyst Andrew Brust and special guest, Dav
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     Gigaom
By: IBM     Published Date: Nov 30, 2017
Analyst firm, Enterprise Strategy Group, examines how companies can leverage cloud-based data lakes and self-service analytics for timely business insights that weren’t possible until now. And learn how IBM Cloud Object Storage, as a persistent storage layer, powers analytics and business intelligence solutions on the IBM Cloud. Complete the form to download the analyst paper.
Tags : analytics, technology, digital transformation, data lake, always-on data lake, ibm, cloud-based analytics
     IBM
By: PolarLake USA     Published Date: Jun 09, 2008
This paper provides an overview of the benefits and challenges for firms wishing to implement XML-based systems and explores how PolarLake can be used to address these challenges.
Tags : polarlake, xml, reference data management, reference data distribution, xml based integration, financial integrations
     PolarLake USA
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