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Published By: Alteryx, Inc.     Published Date: Sep 06, 2017
In order to improve competitive advantage, decision making, and operational efficiencies, many of today’s leading organizations are creating an Office of the Chief Data Officer. According to Gartner, “90 percent of large companies will have a CDO role by the end of 2019”.
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Alteryx, Inc.
Published By: Alteryx, Inc.     Published Date: Sep 06, 2017
Predictive analytics is on the verge of widespread adoption as enterprises become more interested in deploying predictive capabilities. In fact, a recent 2017 TDWI education survey, ranked predictive analytics the top analytics-related topic respondents wanted to learn about. The TDWI Navigator Report- Predictive Analytics provides a comprehensive overview of the state of the predictive analytics market. Download this report to better understand: Opportunities and obstacles of implementing predictive analytics Market forces and trends driving the adoption of predictive analytics Features and market landscapes that define predictive analytics Download this report today to get a better sense on how your organization can take advantage of predictive analytics to drive change in your business.
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Alteryx, Inc.
Published By: Alteryx, Inc.     Published Date: Sep 06, 2017
Organizations that want to apply consistent data management, governance and security practices across their organization can learn a lot from Ford Motor Company. Data-driven decision making led Ford Motor Company to perfect the vehicle assembly line in 1913. A century later, Ford had more than 4,600 data sources, and a wide range of analytical tools, methods, and processes in use throughout the company, and needed a more strategic approach to decision-making.
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Alteryx, Inc.
Published By: Alteryx, Inc.     Published Date: Sep 06, 2017
From small organizations using spreadsheets and visual discovery tools to large enterprises trying to improve data quality and delivery, data preparation difficulties are a major concern. Download your complimentary copy of the full report so you can tackle your data preparation challenges.
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Alteryx, Inc.
Published By: Alteryx, Inc.     Published Date: Sep 06, 2017
Read this whitepaper and see why the shift toward self-service data analytics is empowering leading analysts and analytic teams to improve processes, eliminate repetitive tasks, build better relationships with IT, and deliver deeper insights faster.
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Alteryx, Inc.
Published By: Alteryx, Inc.     Published Date: Sep 06, 2017
According to Forrester, the level of analytic satisfaction within organizations is on the decline.* The traditional multiple-step, multi-tool legacy approach is a slow, time-consuming, and in most cases, a costly process that prevents organizations from making faster decisions with confidence. Data analysts today need an agile solution that empowers them to take charge of the entire analytics process. Download The Definitive Guide to Self-Service Data Analytics to: Understand why traditional analytic tools designed for data scientists are not ideal for data analysts like you Learn how self-service data analytics delivers the ease of use, speed, flexibility, and scalability you require See how Alteryx stacks up against traditional data prep and analytics tools Find out how self-service data analytics bridges the gap across skills, speed, and depth of analysis to empower you to achieve ever-greater insights without coding or depending on other departments.
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Alteryx, Inc.
Published By: Alteryx, Inc.     Published Date: Sep 07, 2017
Download now and learn why your peers at Experian, EMC Corporation, Cardinal Health, and many other organizations are using Alteryx Server to deploy and share analytics at scale.
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Alteryx, Inc.
Published By: Alteryx, Inc.     Published Date: Sep 07, 2017
Download now and learn how Alteryx and Microsoft Power BI help accelerate data blending and advanced analytics consumption for everyone in your organization.
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Alteryx, Inc.
Published By: Alteryx, Inc.     Published Date: Sep 07, 2017
Learn how organizations like Southwest Airlines are using self-service data analytics to make faster, more accurate decisions.
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Alteryx, Inc.
Published By: Alteryx, Inc.     Published Date: Sep 07, 2017
Download this paper now and start to deliver the self-service data analytics that your organization requires.
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Alteryx, Inc.
Published By: Alteryx, Inc.     Published Date: Sep 07, 2017
Download this paper now and start to deliver the self-service data analytics that your organisation requires.
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Alteryx, Inc.
Published By: Altiscale     Published Date: Oct 19, 2015
In this age of Big Data, enterprises are creating and acquiring more data than ever before. To handle the volume, variety, and velocity requirements associated with Big Data, Apache Hadoop and its thriving ecosystem of engines and tools have created a platform for the next generation of data management, operating at a scale that traditional data warehouses cannot match.
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big data, analytics, nexgen, hadoop, apache, networking
    
Altiscale
Published By: Amazon     Published Date: Nov 07, 2013
This planning and implementation guide discusses planning topics, architectural considerations, and configuration steps relevant before and after launching the necessary AWS services such as Amazon Elastic Compute Cloud (Amazon EC2) and Amazon Virtual Private Cloud (Amazon VPC) to run a high-availability and site-resilient Exchange architecture.
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amazon web services, microsoft exchange, aws cloud, cloud, web services, servers, processor, memory, storage, log replication, network traffic, resiliency, availability, configuration, deployment
    
Amazon
Published By: Amazon Web Services     Published Date: Oct 09, 2017
Amazon Web Services has a long history of successfully supporting Microsoft workloads, with hundreds of thousands of customers across a wide variety of industries running their Microsoft workloads on AWS. Customers have successfully deployed virtually every Microsoft application available on the AWS Cloud, including (but not limited to) Microsoft Office, Microsoft Windows Server, Microsoft SQL Server, Microsoft Exchange Server, Microsoft SharePoint Server, Microsoft Skype for Business, Microsoft Dynamics products, and Microsoft Remote Desktop Services. This guide covers the benefits of moving Microsoft workloads to the Cloud and how to get started.
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cloud, amazon, web services, microsoft, security, customer success
    
Amazon Web Services
Published By: Amazon Web Services     Published Date: Oct 09, 2017
As easy as it is to get swept up by the hype surrounding big data, its just as easy for organisations to become discouraged by the challenges they encounter while implementing a big data initiative. Concerns regarding big data skill sets (and the lack thereof), security, the unpredictability of data, unsustainable costs, and the need to make a business case can bring a big data initiative to a screeching halt. However, given big data's power to transform business, it's critical that organisations overcome these challenges and realise the value of big data. The cloud can help organisations to do so. Drawing from IDG's 2015 Big Data and Analytics Survey, this white paper analyses the top five challenges companies face when undergoing a big data initiative and explains how they can effectively overcome them.
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amazon, web services, intel, migration, data warehousing, organization optimization, security, software
    
Amazon Web Services
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: 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.
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predictive, analytics, data lake, infrastructure, natural language processing, amazon
    
Amazon Web Services
Published By: Amazon Web Services     Published Date: Apr 16, 2018
Since SAP introduced its in-memory database, SAP HANA, customers have significantly accelerated everything from their core business operations to big data analytics. But capitalizing on SAP HANA’s full potential requires computational power and memory capacity beyond the capabilities of many existing data center platforms. To ensure that deployments in the AWS Cloud could meet the most stringent SAP HANA demands, AWS collaborated with SAP and Intel to deliver the Amazon EC2 X1 and X1e instances, part of the Amazon EC2 Memory-Optimized instance family. With four Intel® Xeon® E7 8880 v3 processors (which can power 128 virtual CPUs), X1 offers more memory than any other SAP-certified cloud native instance available today.
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Amazon Web Services
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: Amazon Web Services     Published Date: Jul 25, 2018
Defining the Data Lake “Big data” is an idea as much as a particular methodology or technology, yet it’s an idea that is enabling powerful insights, faster and better decisions, and even business transformations across many industries. In general, big data can be characterized as an approach to extracting insights from very large quantities of structured and unstructured data from varied sources at a speed that is immediate (enough) for the particular analytics use case.
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Amazon Web Services
Published By: Amazon Web Services     Published Date: Apr 11, 2018
Effectively monitoring and managing cloud infrastructure configuration changes and performing on-going vulnerability analyses through forensics, troubleshooting, audits, and automation are key activities to help secure your AWS environment.
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Amazon Web Services
Published By: Amazon Web Services     Published Date: Sep 05, 2018
Big data alone does not guarantee better business decisions. Often that data needs to be moved and transformed so Insight Platforms can discern useful business intelligence. To deliver those results faster than traditional Extract, Transform, and Load (ETL) technologies, use Matillion ETL for Amazon Redshift. This cloud- native ETL/ELT offering, built specifically for Amazon Redshift, simplifies the process of loading and transforming data and can help reduce your development time. This white paper will focus on approaches that can help you maximize your investment in Amazon Redshift. Learn how the scalable, cloud- native architecture and fast, secure integrations can benefit your organization, and discover ways this cost- effective solution is designed with cloud computing in mind. In addition, we will explore how Matillion ETL and Amazon Redshift make it possible for you to automate data transformation directly in the data warehouse to deliver analytics and business intelligence (BI
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Amazon Web Services
Published By: Amazon Web Services     Published Date: Sep 05, 2018
Today’s businesses generate staggering amounts of data, and learning to get the most value from that data is paramount to success. Just as Amazon Web Services (AWS) has transformed IT infrastructure to something that can be delivered on-demand, scalably, quickly, and cost-effectively, Amazon Redshift is doing the same for data warehousing and big data analytics. Amazon Redshift offers a massively parallel columnar data store that can be spun up in just a few minutes to deal with billions of rows of data at a cost of just a few cents an hour. Organizations choose Amazon Redshift for its affordability, flexibility, and powerful feature set: • Enterprise-class relational database query and management system • Supports client connections with many types of applications, including business intelligence (BI), reporting, data, and analytics tools • Execute analytic queries in order to retrieve, compare, and evaluate large amounts of data in multiple-stage operations
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Amazon Web Services
Published By: Amazon Web Services     Published Date: Sep 05, 2018
Just as Amazon Web Services (AWS) has transformed IT infrastructure to something that can be delivered on demand, scalably, quickly, and cost-effectively, Amazon Redshift is doing the same for data warehousing and big data analytics. Redshift offers a massively parallel columnar data store that can be spun up in just a few minutes to deal with billions of rows of data at a cost of just a few cents an hour. It’s designed for speed and ease of use — but to realize all of its potential benefits, organizations still have to configure Redshift for the demands of their particular applications. Whether you’ve been using Redshift for a while, have just implemented it, or are still evaluating it as one of many cloud-based data warehouse and business analytics technology options, your organization needs to understand how to configure it to ensure it delivers the right balance of performance, cost, and scalability for your particular usage scenarios. Since starting to work with this technolog
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Amazon Web Services
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