data lake

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Published By: Amazon Web Services     Published Date: Oct 09, 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. Data Lakes are a new and increasingly popular way to store and analyse data that addresses many of these challenges. Data Lakes allow an organization to store all of their data, structured and unstructured, in one, centralized repository.
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cost effective, data storage, data collection, security, compliance, platform, big data, it resources
    
Amazon Web Services
Published By: IBM     Published Date: Jul 06, 2017
Companies today increasingly look for ways to house multiple disparate forms of data under the same roof, maintaining original integrity and attributes. Enter the Hadoop-based data lake. While a traditional on-premise data lake might address the immediate needs for scalability and flexibility, research suggests that it may fall short in supporting key aspects of the user experience. This Knowledge Brief investigate the impact of a data lake maintained in a cloud or hybrid infrastucture.
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data lake, user experience, knowledge brief, cloud infrastructure
    
IBM
Published 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
Published 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
Published 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.
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data lake, big data, analytics
    
IBM APAC
Published By: IBM APAC     Published Date: Jul 09, 2017
This Knowledge Brief investigates the impact of a data lake maintained in a cloud or hybrid infrastructure.
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data lake, cloud, hybrid
    
IBM APAC
Published 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.
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data lake, data warehouse, enterprise data, migration, enterprise use, data lake strategy, business value, data management
    
Teradata
Published By: RedPoint Global     Published Date: May 11, 2017
While they’re intensifying, business-data challenges aren’t new. Companies have tried several strategies in their attempt to harness the power of data in ways that are feasible and effective. The best data analyses and game-changing insights will never happen without the right data in the right place at the right time. That’s why data preparation is a non-negotiable must for any successful customer-engagement initiative. The fact is, you can’t simply load data from multiple sources and expect it to make sense. This white paper examines the shortcomings of traditional approaches such as data warehouses/data lakes and explores the power of connected data.
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customer engagement, marketing data, marketing data analytics, customer data platform
    
RedPoint Global
Published By: Teradata     Published Date: May 02, 2017
Kylo overcomes common challenges of capturing and processing big data. It lets businesses easily configure and monitor data flows in and through the data lake so users have constant access to high-quality data. It also enhances data profiling while offering self-service and data wrangling capabilities.
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cost reduction, data efficiency, data security, data integration, financial services, data discovery, data accessibility, data comprehension
    
Teradata
Published By: IBM     Published Date: Jan 27, 2017
Companies today increasingly look for ways to house multiple disparate forms forms of data under the same roof, maintaining original integrity and attributes. Enter the Hadoop-based data lake. While a traditional on-premise data lake might address the immediate needs for scalability and flexibility, research suggests that it may fall short in supporting key aspects of the user experience. This Knowledge Brief investigates the impact of a data lake maintained in a cloud or hybrid infrastructure.
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IBM
Published By: IBM     Published Date: Apr 18, 2017
The data integration tool market was worth approximately $2.8 billion in constant currency at the end of 2015, an increase of 10.5% from the end of 2014. The discipline of data integration comprises the practices, architectural techniques and tools that ingest, transform, combine and provision data across the spectrum of information types in the enterprise and beyond — to meet the data consumption requirements of all applications and business processes. The biggest changes in the market from 2015 are the increased demand for data virtualization, the growing use of data integration tools to combine "data lakes" with existing integration solutions, and the overall expectation that data integration will become cloud- and on-premises-agnostic.
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data integration, data security, data optimization, data virtualization, database security, data analytics, data innovation
    
IBM
Published By: Waterline Data & Research Partners     Published Date: Nov 07, 2016
Today, businesses pour Big Data into data lakes to help them answer the big questions: Which product to take to market? How to reduce fraud? How to retain more customers? People need to get these answers faster than ever before to reduce “time to answer” from months to minutes. The data is coming in fast and the answers must come just as fast. The answer is self-service data preparation and analytics tools, but with that comes an expectation that the right data is going to be there. Only by using a data catalog can you find the right data quickly to get the expected insight and business value. Download this white paper to learn more!
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Waterline Data & Research Partners
Published 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
Published 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.
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data management, productivity, hadoop, interactive, enterprise, enterprise applications
    
Teradata
Published 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
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population health management, big data, data, data analytics, big data solution, data infrastructure, analytic tools, predictive modeling
    
IBM Watson Health
Published 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.
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network, storage, data, best practices
    
Dell EMC
Published 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.
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emc isilon, storage, best practices, data
    
Dell EMC
Published 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.
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storage, data, enterprise, best practices, platform
    
Dell EMC
Published 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.
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emc, data lake, emc isilon, network, storage, enterprise, business technology
    
Dell EMC
Published 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.
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emc, emc isilon, data lake, storage, network, unstructured data, business technology
    
Dell EMC
Published 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.
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emc, emc isilon, data lake, storage, network, big data, business technology
    
Dell EMC
Published 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.
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emc, data lake, big data, storage, infrastructure, business technology
    
Dell EMC
Published By: Amazon Web Services     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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Amazon Web Services
Published 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.
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ema, hadoop, big data analytics, predictive insights, data lake architecture, hadoop adoption, enterprise management, business intelligence
    
Enterprise Management Associates
Published 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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