Www.informatica.com/downloads/02-Unleash Power Big Data Hadoop_Wei Zheng.pdf. Oracle Exadata and Exalogic Sales. When Oracle announced the Sun technologies-based Exadata in 2010, it claimed to have a backlog of $1 billion. Since then we have seen a steady retreat in optimism about Sun sales in general (including Exadata and Exalogic products) illustrated by a 14% drop in Sun sales in the last quarter. Recent Oracle statements have combined Exadata and Exalogic sales together. Oracle stated that it sold 200 Exadata/Exalogic systems in 2Q 2012 and approximately 1,000 since these systems were introduced.
Based on the assumptions that Oracle sold twice as many half-rack system as full racks, and twice as many quarter rack systems as half-rack systems, and taking the prices from Oracle as of March 2012, Wikibon projects that Exadata/Exalogic hardware revenues in 2Q 2012 were about $84m, or about 1% of Oracle revenue. Including services and software, this means that Exadata/Exalogic sales account for approximately 2% of total Oracle revenue. Footnotes:
Survey results. Www.databaserevolution.com/wp-content/uploads/downloads/2012/05/DbRevolutionReportFinal.pdf. IDC's Big Data Predictions For 2012 | Ali Rıza BABAOGLAN. The Vendor Landscape of BI and Analytics. By Ravi Kalakota “In God we trust, all others bring data” The “Raw Data -> Aggregated Data -> Intelligence -> Insights -> Decisions” is a differentiating causal chain in business today. To service this “data->decision” chain a very large industry is emerging. The Business Intelligence, Performance Management and Data Analytics is a large confusing software category with multiple sub-categories — mega-vendors (full stack, niche vendors, data discovery, visualization, data appliances, Open Source, Cloud – SaaS, Data Integration, Data Quality, Mobile BI, Services and Custom Analytics).
But the interest in BI and analytics is surging. Here is a list of vendors who participate in this marketspace: Big Data Startup and Existing Companies to Watch The BI and Analytics Stack is getting incredibly complex. Given the complexity and the non-linear innovation taking place we expect the market fragmentation to continue for a while before we see a wave of consolidation. Like this: Like Loading... Big Data Manifesto | Hadoop, Business Analytics and Beyond. A Big Data Manifesto from the Wikibon Community Providing effective business analytics tools and technologies to the enterprise is a top priority of CIOs and for good reason. Effective business analytics – from basic reporting to advanced data mining and predictive analytics — allows data analysts and business users alike to extract insights from corporate data that, when translated into action, deliver higher levels of efficiency and profitability to the enterprise.
Underlying every business analytics practice is data. Traditionally, this meant structured data created and stored by enterprises themselves, such as customer data housed in CRM applications, operational data stored in ERP systems or financial data tallied in accounting databases. Traditional data management and business analytics tools and technologies are straining under the added weight of Big Data and new approaches are emerging to help enterprises gain actionable insights from Big Data.
The Changing Nature of Big Data. Think BIG, Act SMALL: Use Case Series – Part 4 of 5 (Using Big Data in Telecommunications and Media & Entertainment) « Big Data. Big Data use-cases in Telecommunications In recent decade, telecom industry has seen data explosion due to increase in subscription, voice data record, wireless information, geo-location details, social media and data usages. Telecom companies who used legacy systems to gain insights from internally generated data often face issues of high storage costs, long data loading time, long administration process, complex queries, outdated compression techniques, and high support costs.
Many organizations are beginning to wake to the reality of big data. Here are some of the use cases for Big Data in Telco business. 1. 2. 3. 4. 5. 6. Big Data use-cases in Media & Entertainment The media/entertainment industry moved to digital recording, production, and delivery in the past few years and is now collecting large amounts of rich content and user viewing behaviors on real-time basis. 1. 2. 3. 4. 5. 6. 7. 8. 9. Next time: Part 5 of 5 (Using Big Data in Utilities, Hi-Tech and ECommerce) About Author: Cdn.idc.com/research/Predictions12/Main/downloads/IDCTOP10Predictions2012.pdf. Hadoop and Netezza: Differences & Similarities. Most of the time vendor videos are emphasizing the superiority of their own commercial platform. But this short video gives a fair overview of the similarities and differences between Hadoop and Netezza.
The video is 5 minutes long and well worth watching. Krishnan Parasuraman (IBM Netezza Chief Architect) also mentiones a couple of scenarios where using both solutions would deliver an optimal solution: Hadoop used as a data ingestion layer for large volumes of dataHadoop is a system of archive In both these scenarios, Netezza would would be the tool for performing deep data analysis, while Hadoop would be used as both a cost-effective storage solution and ETL processing system. Original title and link: Hadoop and Netezza: Differences & Similarities (NoSQL database©myNoSQL) Hadoop and NoSQL - Part IV - Architecting for Analytics - Blog: Wayne Eckerson. Architecture The term "analytical architecture" is an oxymoron. In most organizations, business analysts are left to their own devices to access, integrate, and analyze data.
By necessity, they create their own data sets and reports outside the purview and approval of corporate IT. By definition, there is no analytical architecture in most organizations--just a hodge-podge of analytical silos and spreadmarts, each with conflicting business rules and data definitions. Analytical sandboxes. There are four types of analytical sandboxes: Staging Sandbox. Next-Generation BI Architecture. Figure 1. The next-generation BI architecture is more analytical, giving power users greater options to access and mix corporate data with their own data via various types of analytical sandboxes.
Analytical Platforms The analytical platform movement. Table 1. Moreover, many of these analytical platforms contain built-in analytical functions that make life easier for business analysts. Analytical Tools Data.