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data lakes

Results 1 - 18 of 18Sort Results By: Published Date | Title | Company Name
Published By: Amazon Web Services     Published Date: Jul 25, 2018
What is a Data Lake? 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 analyze data that addresses many of these challenges. A Data Lakes allows an organization to store all of their data, structured and unstructured, in one, centralized repository. Since data can be stored as-is, there is 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. Download to find out more now.
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Amazon Web Services
Published By: Amazon Web Services     Published Date: Jul 25, 2018
Organizations are collecting and analyzing increasing amounts of data making it difficult for traditional on-premises solutions for data storage, data management, and analytics to keep pace. Amazon S3 and Amazon Glacier provide an ideal storage solution for data lakes. They provide options such as a breadth and depth of integration with traditional big data analytics tools as well as innovative query-in-place analytics tools that help you eliminate costly and complex extract, transform, and load processes. This guide explains each of these options and provides best practices for building your Amazon S3-based data lake.
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Amazon Web Services
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: Paxata     Published Date: Nov 14, 2018
This eBook provides a step-by-step best practices guide for creating successful data lakes.
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data lakes, governance, monetization
    
Paxata
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: 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: 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: 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
    
Dell EMC
Published 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
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
Published By: SAS     Published Date: Mar 06, 2018
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
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SAS
Published 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.
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analytics, technology, digital transformation, data lake, always-on data lake, ibm, cloud-based analytics
    
IBM
Published By: SAS     Published Date: Aug 28, 2018
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: 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: NetApp     Published Date: Jun 27, 2016
"Gartner: Moving Toward the All Solid-State Storage Data Center Are you only using solid-state arrays for your primary data? If so, you’re missing out on the benefits flash can deliver to other applications, such as active archives, data lakes, and big data infrastructures. In this independent report, Gartner finds that progressive I&O leaders are already moving toward an all solid-state data center and predicts that others will soon follow. Read the report here."
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NetApp
Published By: NetApp     Published Date: Aug 26, 2016
Gartner: Moving Toward the All Solid-State Storage Data Center Are you only using solid-state arrays for your primary data? If so, you’re missing out on the benefits flash can deliver to other applications, such as active archives, data lakes, and big data infrastructures. In this independent report, Gartner finds that progressive I&O leaders are already moving toward an all solid-state data center and predicts that others will soon follow. Read the report here.
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netapp, database performance, flash storage, data management, cost challenges
    
NetApp
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