Executive Summary: Integrazione Hadoop in Business Intelligence e Data Warehousing

AppId is over the quota AppId is over the quota Hadoop promises to assist with the toughest challenges in BI today, including big data, advanced analytics, and multi-structured data. Download this TDWI Best Practices Report to learn how to integrate Hadoop into your business intelligence, analytics, data integration, and data warehousing technology stacks.

Apache Hadoop is an open source software project administered by the Apache Software Foundation (ASF). The Hadoop family of products includes the Hadoop Distributed File System (HDFS), MapReduce, Pig, Hive, HBase, and so on. These products are available as open source from ASF, as well as from several software vendors. The number of vendor products that integrate with Hadoop products increases almost daily. In this report, the term “Hadoop” usually means the entire Hadoop family of products, regardless of their open source or vendor origins. Some discussions focus specifically on HDFS.

Business intelligence (BI) professionals’ interest in Hadoop has been driven up in recent years because Hadoop has proved its usefulness with the toughest challenges in BI today, namely big data, advanced analytics, and multi-structured data. For that reason, TDWI anticipates that Hadoop technologies will soon become a common complement to (but not a replacement for) established products and practices for business intelligence (BI), data warehousing (DW), data integration (DI), and analytics. Therefore, a wide range of user organizations need to prepare for Hadoop usage. Although it’s true that Hadoop can be valuable as an analytic silo, most organizations will prefer to get the most business value out of Hadoop by integrating it with—or into—their BI, DW, DI, and analytics technology stacks.

According to this report’s survey, users with hands-on Hadoop experience say it’s still immature and needs serious improvements in security, administrative tools, high availability, and real-time operation. These and other problems are being addressed by the open source community of technical users, which continues to infuse innovation into existing Hadoop products as well as introduce new ones via ASF’s incubation process. The pace of Hadoop innovation has accelerated because a number of software vendor firms now contribute to Hadoop’s open source. The first wave of support for Hadoop technologies by vendor tools and platforms is already in place, with subsequent waves coming soon. The number of technical users conversant in Hadoop is increasing steadily.

Executive Summary: Raggiungimento di una maggiore agilità con Business Intelligence

AppId is over the quota The information that flows from BI, analytics, and data warehousing systems can help organizations find the right decision-making balance that avoids the extremes of rigid snap decisions and processes. This new survey-based research report examines benefits, barriers, technology solutions, and recommendations for achieving greater agility with business intelligence.

By David StodderJanuary 4, 2013

Faster decision cycles, competitive pressures to seize fleeting opportunities, and the continuing need to adjust to upheavals in an interconnected global economy are driving demand for business intelligence and analytics that better support business agility. Organizations need flexibility; yet, even as some organizational structures are shifting to support fluid decision making and faster response to changing conditions, the BI systems that deliver vital data and provide the raw material for analytics are not keeping up.

Whether they are part of IT or business functions, which professionals responsible for designing, developing, and deploying BI, analytics and data warehousing (DW) systems are feeling the heat. Applications that merely give users one report after another are not adequate for agility. Professionals must shift development and deployment approaches so that systems are responsive to business needs and agile oriented toward providing self-service functionality to free users from dependence on IT. Self-service BI, however, must be part of a balance; data professionals must balance user freedom with data governance and the need for stable performance for all users.

This TDWI Best Practices Report focuses on how organizations can achieve greater agility with BI, DW, and analytics through adjustments in technology and development strategies. The report provides analysis of an in-depth research survey and user stories to reveal current strategies and future plans for achieving higher agility. The report offers recommendations for making flexibility, shorter time to value, and self-directed functionality higher priorities in BI, DW, and analytics.

Many leading organizations are implementing agile software development methods for BI, DW, and data integration systems. These methods form an alternative to traditional "waterfall" methods and cycles. Agile methods aim at closer collaboration between users and developers; they proposed iterative cycles to deliver value incrementally rather than only at the end of full waterfall cycles. This report discusses agile method adoption.