data lake

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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: Attunity     Published Date: Feb 12, 2019
Read this checklist report, with results based on the Eckerson Group’s survey and the Business Application Research Center (BARC), on how companies using the cloud for data warehousing and BI has increased by nearly 50%. BI teams must address multiple issues including data delivery, security, portability and more before moving to the cloud for its infinite scalability and elasticity. Read this report to understand all 7 seven considerations – what, how and why they impact the decision to move to the cloud.
Tags : cloud, business intelligence, analytics, cloud data, data lake, data warehouse automation tools, dwa, data warehouse, security and compliance, data movement, hybrid cloud, hybrid cloud environment, cross-platform automation, portability
     Attunity
By: Paxata     Published Date: Nov 14, 2018
This eBook provides a step-by-step best practices guide for creating successful data lakes.
Tags : data lakes, governance, monetization
     Paxata
By: AWS     Published Date: Dec 17, 2018
Watch this webinar to learn best practices from Zaloni for creating flexible, responsive, and cost-effective data lakes for advanced analytics that leverage Amazon Web Services (AWS).
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     AWS
By: Larsen & Toubro Infotech(LTI)     Published Date: Jan 31, 2019
LTI helped a leading global bank digitize its traditional product ecosystem for AML transaction monitoring. With the creation of a data lake and efficient learning models, the bank successfully reduced false positives and improved customer risk assessment. Download Complete Case Study.
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     Larsen & Toubro Infotech(LTI)
By: Amazon Web Services     Published Date: Jul 25, 2018
Organisationen müssen heute mit immer größeren Datenmengen zurechtkommen, die aus mehr Datenquellen stammen und mehr Datentypen enthalten als jemals zuvor. Angesichts massiver, heterogener Datenmengen stellen viele Organisationen fest, dass sie eine Datenspeicher- und Analyselösung benötigen, die höhere Geschwindigkeit und mehr Flexibilität als ältere Systeme bietet, um rechtzeitig geschäftliche Erkenntnisse liefern zu können. Ein Data Lake ist eine neue und zunehmend populäre Möglichkeit zur Speicherung und Analyse von Daten, die viele dieser Herausforderungen meistert, indem sie es einer Organisation ermöglicht, alle Daten in einem zentralen Repository zu speichern. Da Daten in ihrem ursprünglichen Format gespeichert werden können, besteht kein Bedarf, sie vor der Übernahme in ein vordefiniertes Schema zu konvertieren, wodurch Sie die Möglichkeit erhalten, all Ihre Daten, sowohl strukturiert als auch unstrukturiert, mit minimaler Vorlaufzeit zu speichern.
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     Amazon Web Services
By: Attunity     Published Date: Feb 12, 2019
This technical whitepaper by Radiant Advisors covers key findings from their work with a network of Fortune 1000 companies and clients from various industries. It assesses the major trends and tips to gain access to and optimize data streaming for more valuable insights. Read this report to learn from real-world successes in modern data integration, and better understand how to maximize the use of streaming data. You will also learn about the value of populating a cloud data lake with streaming operational data, leveraging database replication, automation and other key modern data integration techniques. Download this whitepaper today for about the latest approaches on modern data integration and streaming data technologies.
Tags : streaming data, cloud data lakes, cloud data lake, data lake, cloud, data lakes, streaming data, change data capture, cloud computing, modern data integration, data integration, data analytics, cloud-based data lake, enterprise data, self-service data
     Attunity
By: AWS     Published Date: Oct 26, 2018
Today’s organisations are tasked with analysing multiple data types, coming from a wide variety of sources. Faced with massive volumes and heterogeneous types of data, organisations are finding that in order to deliver analytic 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 enormous amounts of data in a central location, so it’s readily available to be categorised, processed, analysed, and consumed by diverse groups within an organisation? 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.
Tags : data, lake, amazon, web, services, aws
     AWS
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: 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.
Tags : cost effective, data storage, data collection, security, compliance, platform, big data, it resources
     Amazon Web Services
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
By: Oracle     Published Date: Jan 16, 2018
Download this webinar to gain insight on the Data Lake. Learn about the definitions and drivers as well as barriers to Data Lake Success, and Cloud Object Storage.
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     Oracle
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
By: Cask     Published Date: Jun 28, 2016
A recent Gartner survey on Hadoop cited the two biggest challenges in working with Hadoop: “Skills gaps continue to be a major adoption inhibitor for 57% of respondents, while deciding how to get value from Hadoop was cited by 49% of respondents.” Cask is the company that makes building and deploying big data apps easy, allowing for 5 times faster time to value. To find out more, read about Cask Hydrator, a self-service, open source framework that lets data scientists easily develop and operate data pipelines using a graphical interface.
Tags : cask hydrator, hadoop, gartner survey, self-service data lakes
     Cask
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
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: 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: Zaloni     Published Date: Apr 23, 2019
Although data and analytics are highlighted throughout the popular press as well as in trade publications, too many managers think the value of this data processing is limited to a few numerically intensive fields such as science and finance. In fact, big data and the insights that emerge from analyzing it will transform every industry, from “precision farming” to manufacturing and construction. Governments must also be alert to the value of data and analytics as the enabler for smart cities. Institutions that master available data will leap ahead of their less statistically adept competitors through many advantages: finding hidden opportunities for efficiency, using data to become more responsive to clients, and developing entirely new and unanticipated product lines. The average time spent by most companies on the S&P 500 Index has decreased from an average of 60 to 70 years to only 22 years. There are winners and losers in the changes that come with the evolution of both technology
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     Zaloni
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
Tags : 
     SAS
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
Tags : 
     SAS
By: Gigaom     Published Date: Oct 15, 2019
Data pipelines are a reality for most organizations. While we work hard to bring compute to the data, to virtualize and to federate, sometimes data has to move to an optimized platform. While schema-on-read has its advantages for exploratory analytics, pipeline-driven schema-on-write is a reality for production data warehouses, data lakes and other BI repositories. But data pipelines can be operationally brittle, and automation approaches to date have led to a generation of unsophisticated code and triggers whose management and maintenance, especially at-scale, is no easier than the manually-crafted stuff. But it doesn’t have to be that way. With advances in machine learning and the industry’s decades of experience with pipeline development and orchestration, we can take pipeline automation into the realm of intelligent systems. The implications are significant, leading to data-driven agility while eliminating denial of data pipelines’ utility and necessity. To learn more, join us fo
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     Gigaom
By: EMC     Published Date: Jun 13, 2016
EMC Isilon Cloudpools software provides policy-based automated tiering that lets you seamlessly integrate with the cloud as on additional storage tier for the isilon cluster at your data center.
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     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: EMC     Published Date: Jun 13, 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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     EMC
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
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