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By: Waterline Data & Research Partners     Published Date: Nov 07, 2016
For many years, traditional businesses have had a systematic set of processes and practices for deploying, operating and disposing of tangible assets and some forms of intangible asset. Through significant growth in our inquiry discussions with clients, and in observing increased attention from industry regulators, Gartner now sees the recognition that information is an asset becoming increasingly pervasive. At the same time, CDOs and other data and analytics leaders must take into account both internally generated datasets and exogenous sources, such as data from partners, open data and content from data brokers and analytics marketplaces, as they come to terms with the ever-increasing quantity and complexity of information assets. This task is clearly impossible if the organization lacks a clear view of what data is available, how to access it, its fitness for purpose in the contexts in which it is needed, and who is responsible for it.
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     Waterline Data & Research Partners
By: Group M_IBM Q2'19     Published Date: Apr 01, 2019
Power Systems are built for the most demanding, data-intensive, computing on earth. Our cloud-ready servers help you unleash insight from your data pipeline—from managing mission-critical data, to managing your operational data stores and data lakes, to delivering the best server for cognitive computing.
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     Group M_IBM Q2'19
By: Group M_IBM Q3'19     Published Date: Jun 27, 2019
Organizations continue to rush down the digital transformation path. Whether by modernizing their IT infrastructures, leveraging the cloud, or becoming data-centric and data-driven, organizations must become more agile in their business practices and within their IT infrastructure stack to effectively compete in today’s dynamic business environment. Between the speed and distributed nature of modern businesses, as well as the expectation of instantaneous access to data from everyday users, it’s not surprising that nearly one in three organizations are looking into ways to improve data analytics for real-time business intelligence and customer insight.
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     Group M_IBM Q3'19
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: 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: IBM Watson Health     Published Date: Nov 10, 2017
To address the volume, velocity, and variety of data necessary for population health management, healthcare organizations need a big data solution that can integrate with other technologies to optimize care management, care coordination, risk identification and stratification and patient engagement. Read this whitepaper and discover how to build a data infrastructure using the right combination of data sources, a “data lake” framework with massively parallel computing that expedites the answering of queries and the generation of reports to support care teams, analytic tools that identify care gaps and rising risk, predictive modeling, and effective screening mechanisms that quickly find relevant data. In addition to learning about these crucial tools for making your organization’s data infrastructure robust, scalable, and flexible, get valuable information about big data developments such as natural language processing and geographical information systems. Such tools can provide insig
Tags : population health management, big data, data, data analytics, big data solution, data infrastructure, analytic tools, predictive modeling
     IBM Watson Health
By: StreamSets     Published Date: Sep 24, 2018
The advent of Apache Hadoop™ has led many organizations to replatform their existing architectures to reduce data management costs and find new ways to unlock the value of their data. One area that benefits from replatforming is the data warehouse. According to research firm Gartner, “starting in 2018, data warehouse managers will benefit from hybrid architectures that eliminate data silos by blending current best practices with ‘big data’ and other emerging technology types.” There’s undoubtedly a lot to ain by modernizing data warehouse architectures to leverage new technologies, however the replatforming process itself can be harder than it would at first appear. Hadoop projects are often taking longer than they need to create the promised benefits, and often times problems can be avoided if you know what to avoid from the onset.
Tags : replatforming, age, data, lake, apache, hadoop
     StreamSets
By: StreamSets     Published Date: Sep 24, 2018
Imagine you’re running a factory but without a supply chain management system or industrial controls. Instead, you expect your customers to find and fix your delivery and quality problems. Sound ludicrous? Well, in many enterprises that’s the current “supply chain management” process for big and fast data. It relies on the lightly monitored dumping of unsanitized data into a data lake or cloud store, forcing data scientists and business users to deal with failures from data availability and accuracy issues.
Tags : dataflow, operations, factory, industrial
     StreamSets
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: 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: Dell EMC     Published Date: Jun 29, 2016
IDC believes that EMC Isilon is indeed an easy to operate, highly scalable and efficient Enterprise Data Lake Platform. IDC validated that a shared storage model based on the Data Lake can in fact provide enterprise-grade service-levels while performing better than dedicated commodity off-the-shelf storage for Hadoop workloads.
Tags : storage, data, enterprise, best practices, platform
     Dell EMC
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: 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: 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: Dell EMC     Published Date: Mar 18, 2016
The EMC Isilon Scale-out Data Lake is an ideal platform for multi-protocol ingest of data. This is a crucial function in Big Data environments, in which it is necessary to quickly and reliably ingest data into the Data Lake using protocols closest to the workload generating the data. With OneFS it is possible to ingest data via NFSv3, NFSv4, SMB2.0, SMB3.0 as well as via HDFS. This makes the platform very friendly for complex Big Data workflows.
Tags : emc, emc isilon, data lake, storage, network, big data
     Dell EMC
By: Dell EMC     Published Date: Mar 18, 2016
Big Data brings unquestionable value to any organization. But as it continues to grow in volume, more sources and increased storage capacity needs grow with it, taxing the performance of existing infrastructures. But you don’t have to be stuck in such a dismal place. Make the trip to Data Lake instead.
Tags : emc, data lake, big data, storage, infrastructure
     Dell EMC
By: AWS     Published Date: Nov 02, 2017
Today’s organizations are tasked with managing multiple data types, coming from a wide variety of sources. Faced with massive volumes and heterogeneous types of data, organizations are finding that in order to deliver insights in a timely manner, they need a data storage and analytics solution that offers more agility and flexibility than traditional data management systems. A data lake is an architectural approach that allows you to store massive amounts of data into a central location, so it’s readily available to be categorized, processed, analyzed, and consumed by diverse groups within an organization. Since data - structured and unstructured - can be stored as-is, there’s no need to convert it to a predefined schema and you no longer need to know what questions you want to ask of your data beforehand.
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     AWS
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: Jan 25, 2018
If you’re considering your first or next data warehouse, this complimentary eBook explains the cloud data warehouse and how it compares to other data platforms. Download Cloud Data warehouse for Dummies and learn how to get the most out of your data. Highlights include: What a cloud data warehouse is Trends that brought about the adoption of cloud data warehousing How the cloud data warehouse compares to traditional and noSQL offerings How to evaluate different cloud data warehouse solutions Tips for choosing a cloud data warehouse
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     Snowflake
By: Snowflake     Published Date: Jan 25, 2018
To thrive in today’s world of data, knowing how to manage and derive value from of semi-structured data like JSON is crucial to delivering valuable insight to your organization. One of the key differentiators in Snowflake is the ability to natively ingest semi-structured data such as JSON, store it efficiently and then access it quickly using simple extensions to standard SQL. This eBook will give you a modern approach to produce analytics from JSON data using SQL, easily and affordably.
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     Snowflake
By: Snowflake     Published Date: Jan 25, 2018
Compared with implementing and managing Hadoop (a traditional on-premises data warehouse) a data warehouse built for the cloud can deliver a multitude of unique benefits. The question is, can enterprises get the processing potential of Hadoop and the best of traditional data warehousing, and still benefit from related emerging technologies? Read this eBook to see how modern cloud data warehousing presents a dramatically simpler but more power approach than both Hadoop and traditional on-premises or “cloud-washed” data warehouse solutions.
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     Snowflake
By: AWS - ROI DNA     Published Date: Jun 12, 2018
Traditional databases and data warehouses are evolving to capture new data types and spread their capabilities in a hybrid cloud architecture, allowing business users to get the same results regardless of where the data resides. The details of the underlying infrastructure become invisible. Self-managing data lakes automate the provisioning, reliability, performance and cost, enabling data access and experimentation.
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     AWS - ROI DNA
By: AWS - ROI DNA     Published Date: Jun 12, 2018
Achieving a 360-degree view of customers has become increasingly challenging as companies embrace omni-channel strategies, engaging customers across websites, mobile, call centers, social media, physical sites, and beyond. Learn how software solutions in AWS Marketplace can automate data lake analysis, enabling self-service platforms for analysis that expand and enhance personalization while deepening customer understanding so you can spend more time acting on insights.
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     AWS - ROI DNA
By: Enterprise Management Associates     Published Date: Aug 25, 2015
This webinar talks about various issues organization's deal with on a daily basis and how Hadoop can offer solutions.
Tags : ema, hadoop, big data analytics, predictive insights, data lake architecture, hadoop adoption, enterprise management, business intelligence
     Enterprise Management Associates
By: EMA Analyst Research     Published Date: Jun 07, 2016
By viewing this on-demand webinar, you will also discover: • How organizations view their big data initiatives and how they compare with their actual implementation maturity. • Are data lakes becoming a brackish data swamp or a reliable location for data management practices? • How organizations are continuing the trend of implementing the EMA Hybrid Data Ecosystem in association with their big data initiatives.
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     EMA Analyst Research
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