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AppId is over the quota

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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.

Big Data and the Democratisation of Decisions: A Report from the Economist Intelligence Unit

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Realize the potential In-Memory Computing for BI and Analytics

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Responding When It Matters: Creating Unique Real-time Responses Using Analytics

AppId is over the quota AppId is over the quota To make real-time unique response a reality, you must focus on three core competencies.

By Venkat Rajan, Vice president, analytic services, Symphony Teleca Corp.

You're at a home improvement store when you get an offer for a patio furniture set on your cell phone. You think to yourself, "Interesting. This is exactly what I need! I should take a look and see if it's what I've been looking for."

How was an offer presented to you that seemed to practically read your mind? If you study the scenario shown in Figure 1 (below), you will see that no single activity indicates a need for a patio set. These activities by themselves don't provide enough insight to what kind of offer you should get, but there it is. How did it happen?

Figure 1: Patio furniture real-time promotion

Today, we have all the technology components and most of the data to make this happen. First, the home improvement retailer has data about your prior shopping activities. Through analysis of this structured, internally available, transaction data, the retailer can categorize you into the right behavior segment by evaluating characteristics such as basket size, frequency, price sensitivity, and style. It is then a matter of targeting and deciding what kind of offer to make. The retailer can stop at that point and make an offer that fits your behavior and how it wants to influence you. For example, if you are a frequent shopper who buys products in certain categories, the retailer may want you to buy more or buy a product in a related category.

That is not enough. You may not be in the market for buying products in those other categories. If the retailer can integrate unstructured data from Web logs, Facebook, Twitter, and other sources and find the relevant insight into your current behavior, the targeting can be more, well, "on target."

Additionally, by integrating location-based real-time data, the retailer can make that relevant offer to you now, while you have an expressed intent to buy, because you're currently in the home improvement store.

Data analysis faster and better Business Intelligence

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Four pillars for a solution to handling large amounts of data

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