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Hadoop framework 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.
June 20, 2017
IBM pulled the plug on its distribution of Hadoop in favor of reselling Hortonworks' bundle of big data technologies, a decision that reduces the number of Hadoop vendors to four.
May 12, 2017
Kafka is a linchpin in many on-premises big data pipelines. Now, software vendor Confluent is offering a Kafka cloud service to ease use of the messaging and data streaming system in the cloud.
April 28, 2017
Data lakes offer a more expansive alternative to data warehouses for analytics uses. TDWI analyst Philip Russom offers advice on how to get things right in a data lake architecture.
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Apache Spark is an open source parallel processing framework for running large-scale data analytics applications across clustered computers. It can handle both batch and real-time analytics and data processing workloads. Continue Reading
Big data analytics is the process of examining large and varied data sets -- i.e., big data -- to uncover hidden patterns, unknown correlations, market trends, customer preferences and other useful information that can help organizations make ... Continue Reading
Big data architectures typically involve multiple processing platforms. In this essential guide, you'll find information and advice on managing Hadoop, Spark and other big data technologies. Continue Reading
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Getting into the big data market? Hadoop offers a platform for dealing with very large data sets and the technology's vendors offer training and support for channel partners. Continue Reading
It started as a messaging bus, handling LinkedIn's big data. Now, Kafka underpins wider efforts. In a Q&A, Confluent CTO Neha Narkhede marks Kafka's path to a data streaming platform. Continue Reading
A data lake can provide lots of analytics value to an organization. This quiz can tell you if you know what it takes to deploy and manage one; it also contains info to help boost your knowledge. Continue Reading
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The data preparation process is becoming harder to manage effectively, especially for big data analytics applications involving large amounts of data from a variety of sources. Continue Reading
Organizations with big data environments are starting to prepare data for analysis before making it available to data scientists and other users, instead of leaving the work to them. Continue Reading
Deploying big data systems is just the first step for IT and data management teams. The next big challenge is setting up systems and processes for governing data lakes. Continue Reading
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Data management teams are asking more questions about data security, Gartner analyst Merv Adrian says in a Q&A. But new factors are complicating the process of securing databases, he adds. Continue Reading
Moving relational data into Hadoop isn't a slam dunk. To avert programming complexities, a major Texas university turned to commercial tools to stock its Hadoop data lake. Continue Reading
The challenges encountered in deriving business benefits from big data are huge, but so are the rewards. Hadoop and related technologies are easing those challenges to the point where companies are willing to graduate from experimental to full-blown... Continue Reading