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As data extraction, transformation, and loading (ETL) becomes increasingly costly and difficult to scale, you'll need a lower cost alternative that can scale to manage increasing data volumes while maximizing investments in existing technologies.
Sponsored By MapR
Enterprises can gain serious traction by taking advantage of the scalability, processing power, and lower costs that Hadoop 2.0/YARN offers. YARN closes the functionality gap by opening Hadoop to mature enterprise-class data management capabilities. With a lot of data quality functionality left outside of Hadoop 1, and a lot of data inside HDFS originating outside the enterprise, the quality of the data residing in the Hadoop cluster is sometimes poor.
Sponsored By RedPoint Global
The most direct path to making big data—and Hadoop—a first-class citizen will be through an "embrace and extend" approach that not only maps to existing skill sets, data center policies and practices, and business use cases, but also extends them.
Sponsored By Cloudera
Among 18 vendors, Dell Statistica tied for second place with IBM and SAS. See the detailed comparisons in this easy-to-read buyers' guide.
Sponsored By Dell Software
Although the term big data has only recently come into vogue, IBM has designed solutions for decades that are capable of handling very large quantities of data, leading the way with data integration, management, security, and analytics solutions known for their reliability, flexibility, and scalability. The end-to-end information integration capabilities of IBM InfoSphere Information Server are designed to help organizations understand, cleanse, monitor, transform, and deliver data—as well as collaborate to bridge the gap between business and IT.
Sponsored By IBM
To help enterprises create trusted insight as the volume, velocity, and variety of data continue to explode, IBM offers several solutions designed to help organizations uncover previously unavailable insights and use them to support and inform decisions across the business. Combining the power of IBM InfoSphere Master Data Management (MDM) with the IBM big data portfolio creates a valuable connection: big data technology can supply insights to MDM, and MDM can supply master data definitions to big data.
Sponsored By IBM
Hadoop enables organizations to extract valuable insight from large volumes of structured, unstructured, and semi-structured data. The need for large, up-front investments and concerns about flexibility, coupled with special challenges involved in evaluating the technology and developing Hadoop skills, often prevent organizations from adopting and deploying Hadoop across the enterprise. It also becomes impractical to use Hadoop on an occasional basis for high-impact projects that do not have a need for continuous processing. There is good news, though. You can overcome these capital requirements barriers through cloud computing.
Sponsored By IBM
SAS and Hadoop are natural complements. Hadoop provides distributed storage and processing power, while SAS treats Hadoop as just another data source and complements it with data management, data discovery, and advanced analytics. Integrating the two provides important benefits for extracting the most value from your big data assets: accuracy, scalability, and productivity.
Sponsored By SAS