Oracle SPARC SuperCluster: Cloud and Consolidation Capabilities

Oracle has introduced new software enhancements to the Oracle SPARC SuperCluster engineered system that enable customers to consolidate any combination of mission-critical enterprise databases, middleware, and applications on a single system and rapidly deploy secure, self-service cloud services. Oracle SPARC SuperCluster can deliver 10 times application consolidation ratOracle SPARC SuperCluster: Cloud and Consolidation Capabilitiesios, using new layered, zero-overhead virtualization combined with the database offload functions of Oracle Exadata storage servers and efficient networking. Compared to customer-integrated systems, Oracle SPARC SuperCluster delivers 5 times faster time to value, cutting time for installation to production from months to weeks. Continue reading Oracle SPARC SuperCluster: Cloud and Consolidation Capabilities

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Oracle SuperCluster

Oracle SuperCluster Datasheet

Oracle SuperCluster T5-8 in Half Rack and Full Rack configurations
Oracle SuperCluster T5-8 in Half Rack and Full Rack configurations

The Oracle SuperCluster engineered system integrates the following Oracle technologies: SPARC T5-8 and SPARC M6-32 servers; Oracle Solaris 11 and Oracle Solaris 10 operating systems; Oracle VM Server for SPARC virtualization; Oracle Solaris Zones; Oracle Exadata Storage Servers and Exadata Storage Server Software; Oracle ZFS Storage Appliance; InfiniBand QDR networking; Oracle Enterprise Manager Ops Center 12c; Oracle Exalogic Elastic Cloud Software (Optional); Oracle Solaris Cluster (Optional).

This paper helps customers to benefit from accelerating the performance of Oracle Databases, middleware, and business applications on an integrated platform.

Oracle SuperCluster Price

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Big Data Database, Appliance and Big Data Connectors

Oracle’s Big Data Appliance includes Intel’s new processors and the latest release of CDH (Cloudera’s Distribution, including Apache Hadoop) and Cloudera Manager, as well as Oracle Enterprise Manager Plug-In for
Oracle Big Data Database Appliance. Oracle Big Data Connectors have been enhanced to enable greater database SQL access to Hadoop from Oracle Database.

Oracle Big Data Appliance and Connectors
Oracle Big Data Appliance

 

“An influx of raw data sets is flooding every enterprise. However, before businesses can take advantage of the potential opportunity, they face a significant challenge in organizing these diverse datasources,” says Cetin Ozbutun, vice president of data warehousing and big data technologies at Oracle. “The latest updates further improve the abilities of our customers to optimize big data workloads and integrate them with their data warehouses to easily analyze all data throughout the enterprise.”

Big Data Database: appliance and connectors

Oracle Big Data Appliance is an open, multi-purpose engineered system for Hadoop and NoSQL processing. It runs a diverse set of workloads – from Hadoop-only workloads (MapReduce 2, Spark, Hive etc.) to interactive, all-encompassing interactive SQL queries using Oracle Big Data database SQL. Big Data Appliance provides an open environment for innovation while maintaining tight integration and enterprise-level support. Organizations can deploy external software to support new functionality – such as graph analytics, natural language processing and fraud detection. Support for non-Oracle components is delivered by their respective support channels and not by Oracle.

In addition to providing Oracle Big Data database SQL and the full Cloudera software platform, Big Data Appliance utilizes Oracle Big Data Connectors to simplify data integration and analytics. Big Data Connectors provide high-speed access to data in Hadoop from Oracle Exadata and Oracle Database – with data transfer rates on the order of 15 TB/hour. Big Data Connectors also enable integrated, highly scalable analytics – providing native access to Hadoop data and parallel processing using Oracle R Distribution. Finally, Oracle XQuery for Hadoop facilitates standard XQuery operations to process and transform documents in various formats (JSON, XML, Avro and others), executing in parallel across the Hadoop cluster.

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How to Build Better Data Warehousing and Business Intelligence

The processing capacity of the Oracle Exadata Database Machine means fewer physical structures and better Data Warehousing and Business Intelligence.

ORACLE EXADATA DATABASE MACHINE
No, the Oracle Exadata Database Machine is not the infinite machine that will make integration, computation, and aggregation all on-demand functions. But the Oracle Exadata Database Machine helps us to start thinking about simplifying some of the processing and gyrations we have to do to build and deliver an efficient data warehouse solution. With Oracle Exadata, we can more quickly read the data we need to satisfy our queries. Without Oracle Exadata, we need to build separate structures—such as materialized views—in our data warehouses to boost performance. With Oracle Exadata, we don’t need to build as many physical structures, freeing up more time for using the data instead of processing it.

In general terms, there are two reasons for building additional data structures, or even databases, above an integrated data warehouse. The first is to make the data easier to consume by our end users and their reporting and analytic applications. To get better usability and to focus our analytic applications, we create a new subset of our data in snapshots or data marts to support a specific analytic or reporting application, and that subset is just another physical projection, or movement, of the same data. With Oracle Exadata, however, we create fewer physical projections and can create more logical projections of the data in our BI tool of choice in our integrated data warehouse for the same purpose.

Data Warehousing and Business Intelligence

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Oracle Solaris 11 Express

Oracle Solaris 11 Express delivers advanced Oracle Solaris features that have been in development over the past five years. Oracle Solaris 11 Express provides availability features that greatly reduce planned downtime by eliminating traditional patching- and maintenance- related reboots and vastly improving system boot time. It also adds network virtualization and resource management to the complete, built-in virtualization capabilities of Oracle Solaris, providing high-performance virtualization with low overhead.

Oracle Solaris 11 Express also powers Oracle’s Exadata Database Machine X2-2 and Exadata Database Machine X2-8 and Oracle Exalogic Elastic Cloud.

“We are excited to announce the release of Oracle Solaris 11 Express to enable our customers to deploy the new advanced features of Oracle Solaris 11 across a broad set of platforms and our engineered systems: Oracle Exadata and Oracle Exalogic Elastic Cloud,” says John F., executive vice president, Oracle Hardware. “Through the same engineering disciplines that achieved a legendary mission-critical reputation for Oracle Solaris, we are expecting Oracle Solaris 11 to further reduce any downtime by being quicker and easier to deploy, maintain, and update and to deliver a highly efficient virtualized operating system to meet the scale and performance requirements of immediate and future virtualization and cloud-based deployments.”

Read more www.oracle.com/solaris

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Big Data for the Enterprise (White paper)

Today the term big data draws a lot of attention, but behind the hype there’s a simple story. For decades, companies have been making business decisions based on transactional data stored in relational databases. Beyond that critical data, however, is a potential treasure trove of non-traditional, less structured data: weblogs, social media, email, sensors, and photographs that can be mined for useful information. Decreases in the cost of both storage and compute power have made it feasible to collect this data – which would have been thrown away only a few years ago. As a result, more and more companies are looking to include non-traditional yet potentially very valuable data with their traditional enterprise data in their business intelligence analysis.

To derive real business value from big data, you need the right tools to capture and organize a wide variety of data types from different sources, and to be able to easily analyze it within the context of all your enterprise data. Oracle offers the broadest and most integrated portfolio of products to help you acquire and organize these diverse data types and analyze them alongside your existing data to find new insights and capitalize
on hidden relationships.

Big data typically refers to the following types of data:

  • Traditional enterprise data – includes customer information from CRM systems, transactional ERP data, web store transactions, general ledger data.
  • Machine-generated /sensor data – includes Call Detail Records (“CDR”), weblogs, smart meters, manufacturing sensors, equipment logs (often referred to as digital exhaust), trading systems data.
  • Social data – includes customer feedback streams, micro-blogging sites like Twitter, social media platforms like Facebook

Big data Growth
The McKinsey Global Institute estimates that data volume is growing 40% per year, and will grow 44x between 2009 and 2020.

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Report: Cost/Benefit of Enterprise Warehouse Solutions

In-depth Comparison of IBM Smart Analytics System 7700,
Teradata Active Enterprise Data Warehouse and
Oracle Exadata Database Machine.

Data warehousing has emerged as one of the IT world’s fastest growth areas. New deployments continue to accelerate, and numbers of applications and users within organizations continue to expand. Demand for high-quality, current information and for tools to interpret and exploit it shows no signs of abating. High
double-digit growth in data volumes has become the norm.

The business benefits of data warehouse applications are clearly recognized. But, increasingly, users are faced with escalating expenditure not only on data warehouse solutions, but also on underlying platforms. At a time of budgetary pressures, questions are raised about the most cost-effective means of realizing
information value.

This is particularly the case for special-purpose platforms offered by IBM (Smart Analytics System, Netezza TwinFin), Oracle (Exadata Database Machine), Teradata (Active Enterprise Data Warehouse) and smaller players. Architectures and technologies of these systems are often unfamiliar to organizations that deploy them. Techniques for measuring comparative performance and cost are rudimentary.

Challenges are compounded by several factors. One is that the performance of different architectures depends on the workloads they execute. Another is that data warehouse usage tends to evolve rapidly – organizations that deploy platforms for specific applications may soon find that they must deal with significantly different environments. A third is that vendor pricing may vary widely between customers.

This report sets some parameters for comparisons. To do this, it takes into account types of workload – in particular, a key distinction is drawn between complex mixed workloads and queries involving large sequential table scans – compares overall three-year as well as acquisition costs, and bases platform calculation on “street” pricing (i.e., discounted prices paid by users).

The report focuses on three platforms: IBM Smart Analytics System 7700, Oracle Exadata Database Machine and Teradata’s flagship Active Enterprise Data Warehouse (Active EDW) 6650. Results are based on input from 46 users of these systems and their recent predecessors, on other industry sources, as well as on research and analysis conducted by the International Technology Group (ITG).

Two sets of cost comparisons, based on performance and user data, are presented.

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Oracle Big Data Appliance

The creation of data has always been part of the impact of information and communications technology. As the amount of data available for analysis continues to grow, the challenge is for organizations to find the technology that would give them the ability to disseminate, understand and ultimately benefit from the increasing volumes of data. Oracle has recently announced the release of what they consider a viable solution to handle the challenge of Big data. The Oracle Big Data Appliance consists of optimized hardware and several software products from Oracle Corporation .The Oracle Big Data Appliance in union with Oracle Exadata Database Machine and the Oracle Exalytics Business Intelligence Machine (Oracle Exalogic) is used for obtaining, consolidating and loading unstructured data into the Oracle Database 11g. The solution consists of an open source distribution of Apache Hadoop, Oracle NoSQL Database, Oracle Data Integrator with Application Adapter for Hadoop, Oracle Loader for Hadoop, an open source distribution of R, Oracle Linux, and Oracle Java Hotspot Virtual Machine.

Oracle announced the Oracle Big Data Appliance Mon, October 3 at Oracle OpenWorld 2011.

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Private Cloud Appliance

Things are heating up in the private cloud appliance world. Meet Exalogic Elastic Compute Cloud (EECC), which contains both a full server and storage hardware. It is not an appliance, it is much more!

Exalogic is an Engineered System: an assemblage of best-of-breed storage, compute, network, operating system and software products that are integrated, tested, tuned, optimized, delivered and supported by Oracle as a single factory-assembled unit. Exalogic is not an appliance (customers can disassemble the system and use the components for whatever they like, whenever they like, with full support from Oracle) and it is not a blue-print.

Exalogic is designed to provide extreme high performance, reliability, ease-of-use and versatility without being a proprietary, closed system with high total cost of ownership and vendor lock-in. Exalogic is everything enterprises love about both mainframes and open systems with none of the stuff they don’t. Exalogic is the realization of a new way of looking at the role of IT in the modern enterprise.

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