hadoop as service

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Published By: Altiscale     Published Date: Dec 16, 2014
This webinar will explore the merits of the Hadoop SaaS approach, relative to competing cloud and on-premise Hadoop solutions that exist today.
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altiscale, hadoop, saas, hadoop saas, cloud service, infrastructure, enterprise software, data warehousing
    
Altiscale
Published By: Altiscale     Published Date: Mar 30, 2015
This industry analyst report describes important considerations when planning a Hadoop implementation. While some companies have the skill and the will to build, operate, and maintain large Hadoop clusters of their own, a growing number are choosing not to make investments in-house and are looking to the cloud. In this report Gigaom Research explores: • How large Hadoop clusters behave differently from the small groups of machines developers typically use to learn • What models are available for running a Hadoop cluster, and which is best for specific situations • What are the costs and benefits of using Hadoop-as-a-Service With Hadoop delivered as a Service from trusted providers such as Altiscale, companies are able to focus less on managing and optimizing Hadoop and more on the business insights Hadoop can deliver.
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hadoop implementation, hadoop cluster, hadoop-as-a-service, managing and optimizing, it management
    
Altiscale
Published By: Altiscale     Published Date: May 28, 2015
Altiscale’s Hadoop-as-a-Service can reduce costs and improve data scientist productivity, resulting in products, services, and insights realized sooner.
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hadoop-as-a-service, data improvement, data productivity, economic benefits, economic impact, big data, forrester, it management
    
Altiscale
Published By: Altiscale     Published Date: Aug 25, 2015
Weren't able to attend Hadoop Summit 2015? No sweat. Learn more about the latest Big Data technologies in these technical presentations at this recent leading industry event. The Big Data experts at Altiscale - the leader in Big Data as a Service - have been busy at conferences. To see all four presentations (in slides and youtube video), click here. https://www.altiscale.com/educational-slide-kit-2015-big-data-conferences-nf/ • Managing Growth in Production Hadoop Deployments • Running Spark & MapReduce Together in Production • YARN and the Docker Ecosystem • 5 Tips for Building a Data Science Platform
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hadoop, hadoop technologies, hadoop information
    
Altiscale
Published By: Altiscale     Published Date: Aug 25, 2015
Hype abounds about Big Data. And it's hard to know how to effectively exploit its potential. Learn how to separate fact from fiction in this new webinar + research note titled "Amazon EMR is Easy and 7 Other Myths." If you're considering launching a new Big Data initiative, or if you are currently struggling with Amazon EMR, view this 30 minute on-demand webinar + research note and dispel the most common untruths about Amazon EMR, as determined by leading Hadoop experts. Specifically, you'll learn: • The differences between Hadoop-as-a-Service and Amazon EMR • Why Hadoop on Amazon is not elastic • Why EMR is not a "plug-n-play" application • How costs get out of control with Amazon EMR
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amazon emr, big data, haddop
    
Altiscale
Published By: Attunity     Published Date: Nov 15, 2018
With the opportunity to leverage new analytic systems for Big Data and Cloud, companies are looking for ways to deliver live SAP data to platforms such as Hadoop, Kafka, and the Cloud in real-time. However, making live production SAP data seamlessly available wherever needed across diverse platforms and hybrid environments often proves a challenge. Download this paper to learn how Attunity Replicate’s simple, real-time data replication and ingest solution can empower your team to meet fast-changing business requirements in an agile fashion. Our universal SAP data availability solution for analytics supports decisions to improve operations, optimize customer service, and enable companies to compete more effectively.
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Attunity
Published By: BlueData     Published Date: Aug 19, 2015
Many organizations seeking to get started with Hadoop implementation have resorted to the public cloud to avoid the complexities of deployment. Organizations are always looking for an opportunity to take advantage of the simplicity of cloud deployment and the efficiencies of multi-tenant operations on premise. Download this white paper to see why there is a greater need to simplify on-premise deployment and see how this will greatly improve manageability and security.
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big data, hadoop, hadoop-as-a-service, multi-tenant, and infrastructure, data center
    
BlueData
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: 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
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