data warehouse design

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Published By: IBM     Published Date: Sep 22, 2011
This white paper highlights the performance and scalability potential of InfoSphere DataStage 8.1 based on a benchmark test simulating a data warehouse scenario. The benchmark is designed to use the profiled situation to provide insight about how InfoSphere DataStage addresses key questions customers frequently ask when designing their information integration architecture.
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ibm, infosphere, data performance and scalability, customers, datastage
    
IBM
Published By: Teradata     Published Date: Jan 30, 2015
It is hard for data and IT architects to understand what workloads should move, how to coordinate data movement and processing between systems, and how to integrate those systems to provide a broader and more flexible data platform. To better understand these topics, it is helpful to first understand what Hadoop and data warehouses were designed for and what uses were not originally intended as part of the design.
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teradata, data, big, data, analytics. insights, solutions, business opportunities, challenges
    
Teradata
Published By: Attunity     Published Date: Feb 12, 2019
How can enterprises overcome the issues that come with traditional data warehousing? Despite the business value that data warehouses can deliver, too often they fall short of expectations. They take too long to deliver, cost too much to build and maintain, and cannot keep pace with changing business requirements. If this all rings a bell, check out Attunity’s knowledge brief on data warehouse automation with Attunity Compose. The solution automates the design, build, and deployment of data warehouses, data marts and data hubs, enabling more agile and responsive operation. The automation reduces time-consuming manual coding, and error-prone repetitive tasks. Read the knowledge brief to learn more about your options.
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dwa, data warehouse automation, etl development, extract transform load tools, etl tools, data warehouse, data marts, data hubs data warehouse lifecycle
    
Attunity
Published By: Group M_IBM Q418     Published Date: Oct 15, 2018
The enterprise data warehouse (EDW) has been at the cornerstone of enterprise data strategies for over 20 years. EDW systems have traditionally been built on relatively costly hardware infrastructures. But ever-growing data volume and increasingly complex processing have raised the cost of EDW software and hardware licenses while impacting the performance needed for analytic insights. Organizations can now use EDW offloading and optimization techniques to reduce costs of storing, processing and analyzing large volumes of data. Getting data governance right is critical to your business success. That means ensuring your data is clean, of excellent quality, and of verifiable lineage. Such governance principles can be applied in Hadoop-like environments. Hadoop is designed to store, process and analyze large volumes of data at significantly lower cost than a data warehouse. But to get the return on investment, you must infuse data governance processes as part of offloading.
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Group M_IBM Q418
Published By: AWS     Published Date: Aug 20, 2018
A modern data warehouse is designed to support rapid data growth and interactive analytics over a variety of relational, non-relational, and streaming data types leveraging a single, easy-to-use interface. It provides a common architectural platform for leveraging new big data technologies to existing data warehouse methods, thereby enabling organizations to derive deeper business insights. Key elements of a modern data warehouse: • Data ingestion: take advantage of relational, non-relational, and streaming data sources • Federated querying: ability to run a query across heterogeneous sources of data • Data consumption: support numerous types of analysis - ad-hoc exploration, predefined reporting/dashboards, predictive and advanced analytics
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AWS
Published By: AWS     Published Date: Jun 20, 2018
Data and analytics have become an indispensable part of gaining and keeping a competitive edge. But many legacy data warehouses introduce a new challenge for organizations trying to manage large data sets: only a fraction of their data is ever made available for analysis. We call this the “dark data” problem: companies know there is value in the data they collected, but their existing data warehouse is too complex, too slow, and just too expensive to use. A modern data warehouse is designed to support rapid data growth and interactive analytics over a variety of relational, non-relational, and streaming data types leveraging a single, easy-to-use interface. It provides a common architectural platform for leveraging new big data technologies to existing data warehouse methods, thereby enabling organizations to derive deeper business insights. Key elements of a modern data warehouse: • Data ingestion: take advantage of relational, non-relational, and streaming data sources • Federated q
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AWS
Published By: Vertica     Published Date: Feb 20, 2010
For over a decade, IT organizations have been plagued by high data warehousing costs, with millions of dollars spent annually on specialized, high-end hardware and DBA personnel overhead for performance tuning. The root cause: using data warehouse database management (DBMS) software, like Oracle and SQLServer that were designed 20-30 years ago to handle write-intensive OLTP workloads, not query-intensive analytic workloads.
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vertica, ec2, cdr, elastic, saas, cloud computing, data management, ad-hoc
    
Vertica
Published By: IBM     Published Date: Dec 30, 2008
Most long-standing data warehouses are designed to support a relatively small number of users who access information to support strategic decisions, financial planning and the production of standard reports that track performance. Today, many more users need to access information in context and on demand so that critical functions are optimized to run efficiently. Learn how to create a roadmap for a truly dynamic warehousing infrastructure, and move ahead of your competition with your business intelligence system
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warehousing infrastructure, ibm, business intelligence, data warehouse, dynamic warehousing, data warehouse model, master data
    
IBM
Published By: SnowFlake     Published Date: Jul 08, 2016
Today’s data, and how that data is used, have changed dramatically in the past few years. Data now comes from everywhere—not just enterprise applications, but also websites, log files, social media, sensors, web services, and more. Organizations want to make that data available to all of their analysts as quickly as possible, not limit access to only a few highly-skilled data scientists. However, these efforts are quickly frustrated by the limitations of current data warehouse technologies. These systems simply were not built to handle the diversity of today’s data and analytics. They are based on decades-old architectures designed for a different world, a world where data was limited, users of data were few, and all processing was done in on-premises data centers.
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snowflake, data, technology, enterprise, application, best practices, social media
    
SnowFlake
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