querying data

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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: Nov 15, 2018
Businesses are generating staggering amounts of data—and extracting the most value from this information is paramount. Amazon Redshift provides organizations what they’re looking for: Affordability and flexibility combined with a powerful feature set. Download our solution overview covering some of the best practices on loading data and making the most of Amazon Redshift, including: • Loading data for faster results • Querying data for gaining actionable insights • Creating a schema to forgo complicated queries, saving time
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AWS
Published By: Vertica     Published Date: Feb 23, 2010
Ovum takes a deep-dive technology audit of Vertica's Analytic Database that is designed specifically for storing and querying large datasets.
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ovum, vertica, analytical databases, dbms, technology audit, mpp, rdbms
    
Vertica
Published By: IBM     Published Date: May 02, 2014
This paper introduces you to Big SQL, answering many of the common questions that relational database management system (DBMS) users have about this IBM technology.
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ibm, big data, big sql, querying data, database management technology, apache hadoop, data administrators, infosphere
    
IBM
Published By: IBM     Published Date: May 02, 2014
These traditional analytical systems are often based on a classic pattern where data from multiple operational systems is captured, cleaned, transformed and integrated before loading it into a data warehouse.
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ibm, big data platform, architecting big data, analytics, intelligent business strategies, data complexity, data types, workload growth
    
IBM
Published By: Basho     Published Date: Apr 07, 2015
This whitepaper looks at why companies choose Riak over a relational database. We focus specifically on availability, scalability, and the key/value data model. Then we analyze the decision points that should be considered when choosing a non-relational solution and review data modeling, querying, and consistency guarantees. Finally, we end with simple patterns for building common applications in Riak using its key/value design, dealing with data conflicts that emerge in an eventually consistent system, and discuss multi-datacenter replication.
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basho, riak, relational database, nosql, common applications, simple deployments
    
Basho
Published By: Villamation     Published Date: Jun 07, 2011
A practical alternative to the traditional method of querying a database over the Internet.
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search, search portals, xml, javascript, web developer, browser, optimized search, traditional agencies
    
Villamation
Published By: IBM     Published Date: May 02, 2014
Learn more about Forrester’s results, and how these organizations are realizing both economic and operational benefits with InfoSphere Optim solutions.
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ibm, big data, big sql, querying data, database management technology, apache hadoop, data administrators, infosphere
    
IBM
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