Big data query languages News
July 31, 2017
Data management startup Dremio has aimed its Apache Arrow expertise at the problem of self-service data delivery. In-column caches and optimization speed queries across varied data stores.
March 24, 2016
The Strata + Hadoop World conference focuses on big data management and analytics technologies, in particular the Hadoop distributed processing framework and Spark processing engine.
August 31, 2015
Hadoop is one of the most popular big data technologies. But when it comes to predictive modeling and data visualization, its utility is somewhat limited, users of HP's Vertica system say.
July 27, 2015
Metanautix Quest 2.0 is an upgraded query engine and data integration platform. Its designers helped create Google Dremel, a precursor for new big data query tools.
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The latest version of IBM BigInsights offers several value-add services that can be used with its core distribution of open source Hadoop for managing big data. Continue Reading
The Pivotal Greenplum open source shared-nothing data warehouse delivers high query performance and throughput, and provides rapid analytics on big data. Continue Reading
Apache Spark is about more than just data processing. The software libraries included in the big data platform make it suitable for a variety of analytics applications. Continue Reading
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Processing in big data systems can slow to a crawl if queries are not properly tuned or workloads not well balanced -- issues that call for careful monitoring of clusters. Continue Reading
Big data initiatives can help companies improve operational efficiency, create new revenue and gain a competitive advantage. But traditional data processing often can't deal with the mountains of structured, semi-structured and unstructured data that needs to be mined for value. That leaves big data initiatives hungry for new tools and technologies to ease and speed data processing and predictive analytics functions.
In this e-book, get insight on useful tools for big data projects. The first chapter provides real-world examples of organizations using SQL-on-Hadoop engines to simplify the process of querying and analyzing Hadoop data. The second defines Spark -- including its capabilities and limitations -- and offers advice on deploying, managing and using the big data processing engine. And the third chapter focuses on using the open source R analytical programming language and commercial tools such as SAS and IBM SPSS to run analytical applications against Hadoop data sets.Continue Reading