• IBM Consulting

    DBA Consulting can help you with IBM BI and Web related work. Also IBM Linux is our portfolio.

  • Oracle Consulting

    For Oracle related consulting and Database work and support and Migration call DBA Consulting.

  • Novell/RedHat Consulting

    For all Novell Suse Linux and SAP on Suse Linux questions releated to OS and BI solutions. And offcourse also for the great RedHat products like RedHat Enterprise Server and JBoss middelware and BI on RedHat.

  • Microsoft Consulting

    For Microsoft Server 2012 onwards, Microsoft Client Windows 7 and higher, Microsoft Cloud Services (Azure,Office 365, etc.) related consulting services.

  • Citrix Consulting

    Citrix VDI in a box, Desktop Vertualizations and Citrix Netscaler security.

  • Web Development

    Web Development (Static Websites, CMS Websites (Drupal 7/8, WordPress, Joomla, Responsive Websites and Adaptive Websites).

15 May 2016

Blockchain-Powered Internet of Things

IBM Reveals Concept for Blockchain-Powered Internet of Things

What is blockchain?

Blockchain is a technology for a new generation of transactional applications that establishes trust, accountability and transparency while streamlining business processes. A blockchain has two main concepts. A business network, where members exchange items of value through a ledger, which each member possesses and whose content is always in sync with the others.

Blockchain a New Disruption in Financial Servies


Blockchain is a technology for a new generation of transactional applications that establishes trust, accountability and transparency while streamlining business processes. It is a design pattern made famous by bitcoin, but its uses go far beyond. With it, we can re-imagine the world's most fundamental business interactions and open the door to invent new styles of digital interactions. It has the potential to vastly reduce the cost and complexity of cross-enterprise business processes. The distributed ledger makes it easier to create cost-efficient business networks where virtually anything of value can be tracked and traded—without requiring a central point of control.
The application of this emerging technology is showing great promise across a broad range of business applications.


For example, blockchain allows securities to be settled in minutes instead of days. It can also be used to help companies manage the flow of goods and related payments, or enable manufacturers to share production logs with OEMs and regulators to reduce product recalls.

“ Over the past two decades, the Internet has revolutionized many aspects of business and society–making individuals and organizations more productive. Yet the basic mechanics of how people and organizations execute transactions with one another have not been updated for the 21st century. Blockchain could bring to those processes the openness and efficiency we have come to expect in the Internet Era. ”
—Arvind Krishna, Senior VP, IBM Research

IBM joins Linux Foundation to advance Blockchain

Blockchain has huge potential to transform a wide range of industries. But work needs to be done to arrive at a blockchain fabric that is standards-based and ready for the enterprise. IBM joins the Linux Foundation to help accelerate this exciting technology in a new open source community.

Technical Introduction to IBM's Open Blockchain (OBC)

Watch this animation for a sneak peek at the possibilities and how a blockchain-enabled internet can make a radical difference in your industry. Read this post to see how IBM is contributing to this pursuit: ibm.biz/ThinkBlockchain

Bitcoin explained

The Problem and the solution


Asset ownership and transfer between businesses is currently inefficient, slow, costly and vulnerable to manipulation. Everyone has their own ledger where discrepancies between business parties can increase settlement times.
A new way is needed for Internet-age market enablement.


Blockchain technologies can be used to share a ledger across the business network. The network will be private to the parties concerned, permissioned so only authorized parties are allowed to join, and secure using cryptographic technology to ensure that participants only see what they are allowed to see.
The shared ledger will be more robust, since it is replicated and distributed. All transactions against the ledger will require consensus across the network, where provenance of information is clear and transparent. Transactions will be immutable (unchangeable) and final.
The business network participants will be the same - disintermediation is not a natural consequence of blockchain usage.
Goods and services are provided more efficiently, with the potential to lower costs on all levels.

Key concepts of blockchain

A blockchain has two main concepts. A business network, in which members exchange items of value through a ledger, which each member possesses and whose content is always in sync with the others.

Understanding Blockchains

A business network

  • A decentralized peer-to-peer architecture with nodes consisting of market participants (such as banks and securities firms).
  • Protocol peers validate and commit transactions in order to reach consensus.

Shared ledger

It can act as a source of truth for businesses doing transactions on a blockchain:

  • Records all transactions across the business network
  • Is shared among participants
  • Is replicated so each participant has their own copy
  • Is permissioned, so participants see only appropriate transactions

Often, companies have multiple ledgers for multiple business networks in which they participate. It can be used for recording and totaling financial transactions.

The benefits of blockchain

Blockchain can help radically improve industries, beginning with banking and insurance. However the opportunities for blockchain go far beyond this. We predict that this technology will be used to create smart(er), more efficient systems for supply chains, Internet of Things networks, gaming, multi-media rights management, car rental, Government proof of identity (or license) creation and insurance record management.

  • More efficient
  • Less risky
  • More cost-effective
  • Legal contracts
  • Corporate treasury, accounts payable and receivable
  • Trade finance, letters of credit
  • Smart Property
  • International payments
  • Internal cash management

Next Generation IoT Technologies Using The Block Chain

This session will include findings from the recent research study conducted by IBM’s Institute for Business Value on a decentralized Internet of Things. We present a revolutionary approach to address the problems of cost, privacy and longevity of smart devices on the Internet Things. We make the case for rethinking the technology strategy at the foundation of the IoT to be secure, scalable and efficient. Our approach leverages the “block chain” – the technology platform underlying Bitcoin - to create a decentralized platform for the Internet of Things.

An overview of how Samsung and IBM are thinking about the next generation of IoT infrastructure, why we're using the Block Chain (derived from Bitcoin) and what to expect at our January proof of concept presentation in CES will be presented.

IBM Bluemix Garage for Blockchain

Making blockchain real for business

To help accelerate the design and development of blockchain applications, connect with us or try out the IBM Bluemix garages for blockchain. IBM Bluemix garage for blockchain combines industry expertise with blockchain technology along with proven methods Design Thinking and Agile Development, to deliver business solutions that work. We know it’s important to be close to our customers and we have IBM Bluemix for blockchain garage locations in New York, London, Singapore and Tokyo. Additionally, we have Bluemix garage offices in San Francisco, Toronto, Nice, Melbourne, and wherever you are.

Featured sample applications

Simple Data Pipe

Data movement in the cloud made easy
The Simple Data Pipe allows you to connect to data behind web APIs and land it all in one staging ground in its native form using IBM Cloudant NoSQL DB. Bluemix provides prebuilt connections to data sources as well as integrations with both IBM dashDB for data warehousing and IBM Analytics for Apache Spark™ for advanced analytics processing.

Twitter Sentiment Analysis

Decode social conversations using Spark and Watson
Tweets can tell you a lot about how your customers feel. With Twitter Sentiment Analysis, you can easily deploy Spark Streaming along with IBM Watson to analyze emotional, social, and language tones across the Twittersphere. This open source app uses Spark Streaming to capture tweets in real time, score them with the Watson Tone Analyzer service on Bluemix, and visualize the results with an iPython Notebook. It's simple social sentiment scoring, for free!

Getting started with Blockchain 

With the IBM® Blockchain service on Bluemix, you can quickly spin up a blockchain network and circumvent the complexities involved with manually creating a development environment. Rather than creating and managing a network, developers can spend their time generating applications and working with chaincode. The service is a peer-to-peer permissioned network built on top of the Linux Foundation's Hyperledger fabric code.

You can use a blockchain network to exchange financial records through a shared ledger. For more information about shared ledgers and business networks, see the About Blockchain topic.

You can get to the Console through this Link:

To get started, follow these steps to create and deploy an unbound service instance of a Blockchain network. Once complete, you will have your own development environment with validating nodes and a security service. From there, you can deploy chaincode, see results, and build your applications:

From the landing page for the Blockchain DevOps Service, complete the following fields in the Add Service window:

  1. Choose dev from the Space drop-down window.
  2. Leave the App field as Leave unbound.
  3. Change the Service name to myblockchain123, or some value unique to you.
  4. Leave the Credential name field as its default value.
  5. Leave the Selected Plan as its default value.
  6. Click CREATE.

You are now on the Service Dashboard screen for your new service. From here you can Manage your instance of the network:

  1. Click LAUNCH to see the blockchain monitor for your Blockchain network.

The blockchain monitor displays network details, live logs, current ledger state, APIs, and chaincode templates. Use the dashboard for any of the following functions:

  1. Access Discovery and API routes for the peers on your network.
  2. View any currently-running chaincode containers.
  3. View real-time logs and troubleshoot chaincode that fails to execute.
  4. View the world state for your ledger.
  5. Access the Swagger UI and interact with your network via the REST API.
  6. Deploy one of three available chaincode examples.

Jerry Cuomo says Blockchain is OPEN for business

Jerry Cuomo (IBM Fellow, VP, and CTO of Middleware) opens his remarks by saying, "We believe will fundamentally change the way we do business." He says that IBM's efforts in blockchain are "open by design."

Jerry Cuomo starts with some "blockchain 101." He discusses some common use cases that may be radically changed by blockchain technology:

Common blockchain use casesJerry Cuomo says the common element in these use cases is that they take several days to "settle" and that, "Blockchain can reimagine the world's most fundamental business interactions and open the door to invent new styles of digital interactions." Jerry says the game changing element of blockchain is it's ability to reduce the time it takes to settle multiparty transactions. Blockchain can also reduce the cost of these transactions and reduce risk of tampering with the transactions.

Jerry Cuomo explains the concept of a "ledger" in blockchain technology:

Jerry Cuomo introduces the "Hyperledger project," which is being launched through the Linux Foundation. It's goal is to make blockchain ready for business. They are looking at enhancements that will be needed for business applications such as a permission model, audit mechanisms. Jerry says that IBM has offered 45,000 lines of code to the Hyperledger project to help make blockchain technology ready for business.

More Information:
















17 April 2016

SUSE Enterprise Linux 12 and Docker Containers

Propel your enterprise to the next level of productivity and competitiveness with SUSE Linux Enterprise 12 Service Pack 1.

Service Pack 1 further adds to SUSE Linux Enterprise making it the most interoperable platform for mission-critical computing across physical, virtual and cloud environments.

SUSE Linux Enterprise 12 Install and overview | The Advanced Foundation for Enterprise Computing  

Solutions based on SUSE Linux Enterprise 12 Service Pack 1 (SP1) feature unique Docker and hardware support along with new and updated capabilities so you can:
  • Achieve SLAs for application uptime
  • Run highly efficient data center development and operations
  • Bring innovative solutions to market faster
Docker in SUSE Linux Enterprise Server

Increase Uptime

SUSE Enterprise Linux DownTime isn’t and Option

Minimize planned and unplanned downtime and maximize service availability. Take advantage of our rugged reliability, high availability and live kernel patching to meet service-level agreements and keep your business running. Learn more about how SUSE helps you achieve 99.999% availability and move towards zero downtime.
  • SUSE Linux Enterprise works perfectly on a variety of hardware platforms that can prevent hardware downtime
  • Maximize service availability with high availability clustering, geo clustering and live kernel patching
  • Minimize human mistakes with a wide range of tools and services including system rollback of SUSE Linux Enterprise service packs

SUSE Linux Enterprise Server 12 Zero Downtime

Improve Operational Efficiency

Boost your efficiency by simplifying systems management and by ensuring high levels of resource utilization.
  • Stay ahead on implementations with container technologies. Take advantage of SUSE’s enterprise ready solution of Docker. See how the ecosystem of SUSE applications creates additional value for your business, so you can just focus on building the apps.
  • Save time and resources with JeOS (Just enough Operating System), a lightweight Linux OS that needs fewer resources than the full OS but provides the same enterprise-grade performance and availability.
  • Reduce IT maintenance workload with easy-to-use management tools such as YaST/AutoYaST (single system management), Wicked (network management), and HAWK (cluster resource management).
  • Maximize your efficiency with virtualization technologies of Xen and KVM.

The Evolution of Linux Containers and Integration of Docker with SLES 12 

Accelerate Innovation

Harness the power of the newest CPUs on the market. Get fast, timely access to abundant open source and partner innovations. Reduce time to value through SUSE-certified quality and ease of integration.
  • Get the benefits of the latest open source innovation sooner by updating with modules
  • Get partner innovation quickly through SUSE SolidDriver Program
  • Reduce time to value through SUSE certifications for hardware and applications

Welcome Docker to SUSE Linux Enterprise Server

Lightweight virtualization is a hot topic these days. Also called “operating system-level virtualization,” it allows you to run multiple applications or systems on one host without a hypervisor. The advantages are obvious: not having a hypervisor, the layer between the host hardware and the operating system and its applications, is eliminated, allowing a much more efficient use of resources. That, in turn, reduces the virtualization overhead while still allowing for separation and isolation of multiple tasks on one host. As a result, lightweight virtualization is very appealing in environments where resource use is critical, like server hosting or outsourcing business.

One specific example of operating system-level virtualization is Linux Containers, also sometimes called “LXC” for short. We already introduced Linux Containers to SUSE customers and users in February 2012 as a part of SUSE Linux Enterprise Server 11 SP2. Linux Containers employ techniques like Control Groups (cgroups) to perform resource isolation to control CPU, memory, network, block I/O and namespaces to isolate the process view of the operating system, including users, processes or file systems. That provides advantages similar to those of “regular” virtualization technologies – such as KVM or Xen –, but with much smaller I/O overhead, storage savings and the ability to apply dynamic parameter changes without the need to reboot the system. The Linux Containers infrastructure is supported in SUSE Linux Enterprise 11 and will remain supported in SUSE Linux Enterprise 12.

Full system roll-back and systemd in SUSE 

Now, we are taking a next step to further enhance our virtualization strategy and introduce you to Docker. Docker is built on top of Linux Containers with the aim of providing an easy way to deploy and manage applications. It packages the application including its dependencies in a container, which then runs like  a virtual machine. Such packaging allows for application portability between various hosts, not only across one data center, but also to the cloud. And starting with SUSE Linux Enterprise Server 12 we plan to make Docker available to our customers so they can start using it to build and run their containers. 

SUSE Linux Enterprise Live Patching Roadmap: Live Kernel Patching using kGraft   

This is the another step in enhancing the SUSE virtualization story, building on top of what we have already done with Linux Containers. Leveraging the SUSE ecosystem, Docker and Linux Containers are not only a great way to build, deploy and manage applications; the idea nicely plugs into tools like Open Build Service and Kiwi for easy and powerful image building or SUSE Studio, which offers a similar concept already for virtual machines. Docker easily supports rapid prototyping and a fast deployment process; thus when combined with Open Build Service, it’s a great tool for developers aiming to support various platforms with a unified tool chain. This is critical for the future because those platforms easily apply also to clouds, public, private and hybrid. Combining Linux Containers, Docker, SUSE’s development and deployment infrastructures and SUSE Cloud, our OpenStack-based cloud infrastructure offering, brings flexibility in application deployment to a completely new level.

SUSE Linux Enterprise High Availability Roadmap: Secure your Data and Service from Local to Geo 

Introducing Docker follows the SUSE philosophy by offering choice in the virtualization space, allowing for flexibility, performance and simplicity for Linux in data centers and the cloud.

Securing Your System Hardening and Tweaking SUSE Linux Enterprise Server 12

More Information:

SUSE Embedded Offers a Medical Device Operating System 

15 March 2016

Oracle Big Data in the Enterprise

Oracle Enterprise Big Data

Oracle is the first vendor to offer a complete and integrated solution to address the full spectrum of enterprise big data requirements. Oracle’s big data strategy is centered on the idea that you can evolve your current enterprise data architecture to incorporate big data and deliver business value. By evolving your current enterprise architecture, you can leverage the proven reliability, flexibility and performance of your Oracle systems to address your big data requirements.
Defining Big Data

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, and 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

The McKinsey Global Institute estimates that data volume is growing 40% per year, and will grow 44x between 2009 and 2020. But while it’s often the most visible parameter, volume of data is not the only characteristic that matters. In fact, there are four key characteristics that define big data:

  • Volume. Machine-generated data is produced in much larger quantities than non-traditional data. For instance, a single jet engine can generate 10TB of data in 30 minutes. With more than 25,000 airline flights per day, the daily volume of just this single data source runs into the Petabytes. Smart meters and heavy industrial equipment like oil refineries and drilling rigs generate similar data volumes, compounding the problem.
  • Velocity. Social media data streams – while not as massive as machine-generated data – produce a large influx of opinions and relationships valuable to customer relationship management. Even at 140 characters per tweet, the high velocity (or frequency) of Twitter data ensures large volumes (over 8 TB per day).
  • Variety. Traditional data formats tend to be relatively well defined by a data schema and change slowly. In contrast, non-traditional data formats exhibit a dizzying rate of change. As new services are added, new sensors deployed, or new marketing campaigns executed, new data types are needed to capture the resultant information.
  • Value. The economic value of different data varies significantly. Typically there is good information hidden amongst a larger body of non-traditional data; the challenge is identifying what is valuable and then transforming and extracting that data for analysis.

Big Data gets Real time with Oracle Fast Data

To make the most of big data, enterprises must evolve their IT infrastructures to handle these new high-volume, high-velocity, high-variety sources of data and integrate them with the pre-existing enterprise data to be analyzed.

Building a Big Data Platform

As with data warehousing, web stores or any IT platform, an infrastructure for big data has unique requirements. In considering all the components of a big data platform, it is important to remember that the end goal is to easily integrate your big data with your enterprise data to allow you to conduct deep analytics on the combined data set.

Infrastructure Requirements

The requirements in a big data infrastructure span data acquisition, data organization and data analysis.

Acquire Big Data

The acquisition phase is one of the major changes in infrastructure from the days before big data. Because big data refers to data streams of higher velocity and higher variety, the infrastructure required to support the acquisition of big data must deliver low, predictable latency in both capturing data and in executing short, simple queries; be able to handle very high transaction volumes, often in a distributed environment; and support flexible, dynamic data structures.

NoSQL databases are frequently used to acquire and store big data. They are well suited for dynamic data structures and are highly scalable. The data stored in a NoSQL database is typically of a high variety because the systems are intended to simply capture all data without categorizing and parsing the data into a fixed schema.

For example, NoSQL databases are often used to collect and store social media data. While customer facing applications frequently change, underlying storage structures are kept simple. Instead of designing a schema with relationships between entities, these simple structures often just contain a major key to identify the data point, and then a content container holding the relevant data (such as a customer id and a customer profile). This simple and dynamic structure allows changes to take place without costly reorganizations at the storage layer (such as adding new fields to the customer profile).

Organize Big Data

In classical data warehousing terms, organizing data is called data integration. Because there is such a high volume of big data, there is a tendency to organize data at its initial destination location, thus saving both time and money by not moving around large volumes of data. The infrastructure required for organizing big data must be able to process and manipulate data in the original storage location; support very high throughput (often in batch) to deal with large data processing steps; and handle a large variety of data formats, from unstructured to structured.

Hadoop is a new technology that allows large data volumes to be organized and processed while keeping the data on the original data storage cluster. Hadoop Distributed File System (HDFS) is the long-term storage system for web logs for example. These web logs are turned into browsing behavior (sessions) by running MapReduce programs on the cluster and generating aggregated results on the same cluster. These aggregated results are then loaded into a Relational DBMS system.

Analyze Big Data

Since data is not always moved during the organization phase, the analysis may also be done in a distributed environment, where some data will stay where it was originally stored and be transparently accessed from a data warehouse. The infrastructure required for analyzing big data must be able to support deeper analytics such as statistical analysis and data mining, on a wider variety of data types stored in diverse systems; scale to extreme data volumes; deliver faster response times driven by changes in behavior; and automate decisions based on analytical models.

Big Data Discovery: Unlock the Potential in a Big Data Reservoir

Most importantly, the infrastructure must be able to integrate analysis on the combination of big data and traditional enterprise data. New insight comes not just from analyzing new data, but from analyzing it within the context of the old to provide new perspectives on old problems.

For example, analyzing inventory data from a smart vending machine in combination with the events calendar for the venue in which the vending machine is located, will dictate the optimal product mix and replenishment schedule for the vending machine.

Statistics and Predictive Analytics in Oracle Database and Hadoop

Solution Spectrum

Many new technologies have emerged to address the IT infrastructure requirements outlined above. At last count, there were over 120 open source key-value databases for acquiring and storing big data, while Hadoop has emerged as the primary system for organizing big data and relational databases maintain their footprint as a data warehouse and expand their reach into less structured data sets to analyze big data. These new systems have created a divided solutions spectrum comprised of:

  • Not Only SQL (NoSQL) solutions: developer-centric specialized systems
  • SQL solutions: the world typically equated with the manageability, security and trusted nature of relational database management systems (RDBMS)

NoSQL systems are designed to capture all data without categorizing and parsing it upon entry into the system, and therefore the data is highly varied. SQL systems, on the other hand, typically place data in well-defined structures and impose metadata on the data captured to ensure consistency and validate data types.

Distributed file systems and transaction (key-value) stores are primarily used to capture data and are generally in line with the requirements discussed earlier in this paper. To interpret and distill information from the data in these solutions, a programming paradigm called MapReduce is used. MapReduce programs are custom written programs that run in parallel on the distributed data nodes.

The key-value stores or NoSQL databases are the OLTP databases of the big data world; they are optimized for very fast data capture and simple query patterns. NoSQL databases are able to provide very fast performance because the data that is captured is quickly stored with a single indentifying key rather than being interpreted and cast into a schema. By doing so, NoSQL database can rapidly store large numbers of transactions.

However, due to the changing nature of the data in the NoSQL database, any data organization effort requires programming to interpret the storage logic used. This, combined with the lack of support for complex query patterns, makes it difficult for end users to distill value out of data in a NoSQL database.

To get the most from NoSQL solutions and turn them from specialized, developer-centric solutions into solutions for the enterprise, they must be combined with SQL solutions into a single proven infrastructure that meets the manageability and security requirements of today’s enterprises.

Oracle’s Big Data Solution

Oracle is the first vendor to offer a complete and integrated solution to address the full spectrum of enterprise big data requirements. Oracle’s big data strategy is centered on the idea that you can extend your current enterprise information architecture to incorporate big data.

Oracle big data appliance and solutions

New big data technologies, such as Hadoop and Oracle NoSQL database, run alongside your Oracle data warehouse to deliver business value and address your big data requirements.

Ask the Oracle Experts Big Data Analytics with Oracle Advanced Analytics

Oracle Big Data Appliance

Oracle Big Data Appliance is an engineered system that combines optimized hardware with a comprehensive big data software stack to deliver a complete, easy-to-deploy solution for acquiring and organizing big data.

Oracle Big Data Appliance comes in a full rack configuration with 18 Sun servers for a total storage capacity of 648TB. Every server in the rack has 2 CPUs, each with 8 cores for a total of 288 cores per full rack. Each server has 64GB1 memory for a total of 1152GB of memory per full rack.

Oracle big data appliance and solutions

Oracle Big Data Appliance includes a combination of open source software and specialized software developed by Oracle to address enterprise big data requirements.

The Oracle Big Data Appliance software includes:

  • Full distribution of Cloudera’s Distribution including Apache Hadoop (CDH4)
  • Oracle Big Data Appliance Plug-In for Enterprise Manager
  • Cloudera Manager to administer all aspects of Cloudera CDH
  • Oracle distribution of the statistical package R
  • Oracle NoSQL Database Community Edition2
  • And Oracle Enterprise Linux operating system and Oracle Java VM

Big Data: Myths and Realities

Oracle NoSQL Database

Oracle NoSQL Database is a distributed, highly scalable, key-value database based on Oracle Berkeley DB. It delivers a general purpose, enterprise class key value store adding an intelligent driver on top of distributed Berkeley DB. This intelligent driver keeps track of the underlying storage topology, shards the data and knows where data can be placed with the lowest latency. Unlike competitive solutions, Oracle NoSQL Database is easy to install, configure and manage, supports a broad set of workloads, and delivers enterprise-class reliability backed by enterprise-class Oracle support.

The primary use cases for Oracle NoSQL Database are low latency data capture and fast querying of that data, typically by key lookup. Oracle NoSQL Database comes with an easy to use Java API and a management framework. The product is available in both an open source community edition and in a priced enterprise edition for large distributed data centers. The former version is installed as part of the Big Data Appliance integrated software.

Oracle Big Data Connectors

Where Oracle Big Data Appliance makes it easy for organizations to acquire and organize new types of data, Oracle Big Data Connectors tightly integrates the big data environment with Oracle Exadata and Oracle Database, so that you can analyze all of your data together with extreme performance.

The Oracle Big Data Connectors consist of four components:

1. Oracle Loader for Hadoop

Oracle Loader for Hadoop (OLH) enables users to use Hadoop MapReduce processing to create optimized data sets for efficient loading and analysis in Oracle Database 11g. Unlike other Hadoop loaders, it generates Oracle internal formats to load data faster and use less database system resources. OLH is added as the last step in the MapReduce transformations as a separate map – partition – reduce step. This last step uses the CPUs in the Hadoop cluster to format the data into Oracle’s internal database formats, allowing for a lower CPU utilization and higher data ingest rates on the Oracle Database platform. Once loaded, the data is permanently available in the database providing very fast access to this data for general database users leveraging SQL or business intelligence tools.

2. Oracle SQL Connector for Hadoop Distributed File System

Oracle SQL Connector for Hadoop Distributed File System (HDFS) is a high speed connector for accessing data on HDFS directly from Oracle Database. Oracle SQL Connector for HDFS gives users the flexibility of querying data from HDFS at any time, as needed by their application.

Oracle Big Data SQL - Create Value with Data

It allows the creation of an external table in Oracle Database, enabling direct SQL access on data stored in HDFS. The data stored in HDFS can then be queried via SQL, joined with data stored in Oracle Database, or loaded into the Oracle Database. Access to the data on HDFS is optimized for fast data movement and parallelized, with automatic load balancing. Data on HDFS can be in delimited files or in Oracle data pump files created by Oracle Loader for Hadoop.

3. Oracle Data Integrator Application Adapter for Hadoop

Hortonworks Oracle Big Data Integration

Oracle Data Integrator Application Adapter for Hadoop simplifies data integration from Hadoop and an Oracle Database through Oracle Data Integrator’s easy to use interface. Once the data is accessible in the database, end users can use SQL and Oracle BI Enterprise Edition to access data.

Hortonworks and Oracle Data Transformation and Acquisition Techniques to Handle Petabytes of Data

Enterprises that are already using a Hadoop solution, and don’t need an integrated offering like Oracle Big Data Appliance, can integrate data from HDFS using Big Data Connectors as a stand-alone software solution.

4. Oracle R Connector for Hadoop

Oracle R Connector for Hadoop is an R package that provides transparent access to Hadoop and to data stored in HDFS.

R Connector for Hadoop provides users of the open-source statistical environment R with the ability to analyze data stored in HDFS, and to scalably run R models against large volumes of data leveraging MapReduce processing – without requiring R users to learn yet another API or language. End users can leverage over 3500 open source R packages to analyze data stored in HDFS, while administrators do not need to learn R to schedule R MapReduce models in production environments.

Big Data Discovery: Unlock the Potential in a Big Data Reservoir

R Connector for Hadoop can optionally be used together with the Oracle Advanced Analytics Option for Oracle Database. The Oracle Advanced Analytics Option enables R users to transparently work with database resident data without having to learn SQL or database concepts but with R computations executing directly in-database.

Oracle Data Integration 

In-Database Analytics

Once data has been loaded from Oracle Big Data Appliance into Oracle Database or Oracle Exadata, end users can use one of the following easy-to-use tools for in-database, advanced analytics:

  • Oracle R Enterprise – Oracle’s version of the widely used Project R statistical environment enables statisticians to use R on very large data sets without any modifications to the end user experience. Examples of R usage include predicting airline delays at a particular airports and the submission of clinical trial analysis and results.
  • In-Database Data Mining – the ability to create complex models and deploy these on very large data volumes to drive predictive analytics. End-users can leverage the results of these predictive models in their BI tools without the need to know how to build the models. For example, regression models can be used to predict customer age based on purchasing behavior and demographic data.
  • In-Database Text Mining – the ability to mine text from micro blogs, CRM system comment fields and review sites combining Oracle Text and Oracle Data Mining. An example of text mining is sentiment analysis based on comments. Sentiment analysis tries to show how customers feel about certain companies, products or activities.
  • In-Database Graph Analysis – the ability to create graphs and connections between various data points and data sets. Graph analysis creates, for example, networks of relationships determining the value of a customer’s circle of friends. When looking at customer churn customer value is based on the value of his network, rather than on just the value of the customer.
  • In-Database Spatial – the ability to add a spatial dimension to data and show data plotted on a map. This ability enables end users to understand geospatial relationships and trends much more efficiently. For example, spatial data can visualize a network of people and their geographical proximity. Customers who are in close proximity can readily influence each other’s purchasing behavior, an opportunity which can be easily missed if spatial visualization is left out.
  • In-Database MapReduce – the ability to write procedural logic and seamlessly leverage Oracle Database parallel execution. In-database MapReduce allows data scientists to create high-performance routines with complex logic. In-database MapReduce can be exposed via SQL. Examples of leveraging in-database MapReduce are sessionization of weblogs or organization of Call Details Records (CDRs).

Getting Started with Oracle Big Data Discovery v1.0

Every one of the analytical components in Oracle Database is valuable. Combining these components creates even more value to the business. Leveraging SQL or a BI Tool to expose the results of these analytics to end users gives an organization an edge over others who do not leverage the full potential of analytics in Oracle Database.

Big Data Analyics using Oracle Advanced Analytics12c and BigDataSQL

Connections between Oracle Big Data Appliance and Oracle Exadata are via InfiniBand, enabling high-speed data transfer for batch or query workloads. Oracle Exadata provides outstanding performance in hosting data warehouses and transaction processing databases.

Oracle Active Data Guard with Axxana Phoenix for Disaster Recovery Zero Transactions Loss

Now that the data is in mass-consumption format, Oracle Exalytics can be used to deliver the wealth of information to the business analyst. Oracle Exalytics is an engineered system providing speed-of-thought data access for the business community. It is optimized to run Oracle Business Intelligence Enterprise Edition with in-memory aggregation capabilities built into the system.

Oracle Big Data Appliance, in conjunction with Oracle Exadata Database Machine and the new Oracle Exalytics Business Intelligence Machine, delivers everything customers need to acquire, organize, analyze and maximize the value of Big Data within their enterprise.


Analyzing new and diverse digital data streams can reveal new sources of economic value, provide fresh insights into customer behavior and identify market trends early on. But this influx of new data creates challenges for IT departments. 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. By using the Oracle Big Data Appliance and Oracle Big Data Connectors in conjunction with Oracle Exadata, enterprises can acquire, organize and analyze all their enterprise data – including structured and unstructured – to make the most informed decisions.

More Information:

Oracle Enterprise Big Data  https://www.oracle.com/big-data/index.html

Oracle Big Data SQL    http://www.oracle.com/us/products/database/big-data-sql/overview/index.html

Oracle NoSQL Database

Big Data for Data Scientists  https://www.oracle.com/big-data/roles/data-scientist.html

Oracle: Big Data for the Enterprise   http://www.oracle.com/us/products/database/big-data-for-enterprise-519135.pdf

Oracle Big Data Appliance  https://www.oracle.com/engineered-systems/big-data-appliance/index.html

Oracle Big Data Solutions  https://www.oracle.com/big-data/solutions/index.html

Big Data in the Cloud   https://cloud.oracle.com/bigdata

Oracle Big Data Lite Virtual Machine  http://www.oracle.com/technetwork/database/bigdata-appliance/oracle-bigdatalite-2104726.html

Oracle Big Data Discovery  https://cloud.oracle.com/bigdatadiscovery

Oracle Data Integrator https://blogs.oracle.com/dataintegration/entry/announcing_oracle_data_integrator_for

Oracle GoldenGate for Big Data https://blogs.oracle.com/dataintegration/entry/oracle_goldengate_for_big_data

Demystifying Big Data for Oracle Professionals  http://arup.blogspot.nl/2013/06/demystifying-big-data-for-oracle.html

How to Implement a Big Data System http://www.oracle.com/technetwork/articles/bigdata/implementing-bigdata-1502704.html

Oracle Active Data Guard with Axxana Phoenix for Disaster Recovery Zero Transactions Loss http://www.axxana.com/resources/videos/

09 February 2016

Red Hat Enterprise Linux 7.2 deployment of container-based applications

Red Hat Drives Networking, Linux Container Innovation in Latest Version of Red Hat Enterprise Linux 7

Red Hat Enterprise Linux 7.2 boosts network performance and delivers additional enhancements to support the development and deployment of container-based applications

Red Hat, Inc. (NYSE: RHT), the world's leading provider of open source solutions, today announced the general availability of Red Hat Enterprise Linux 7.2, the latest release of Red Hat Enterprise Linux 7. Red Hat Enterprise Linux 7.2 continues Red Hat's goal of redefining the enterprise operating system by providing a trusted path towards the future of information technology without compromising the needs of the modern enterprise.

New features and capabilities focus on security, networking, and system administration, along with a continued emphasis on enterprise-ready tooling for the development and deployment of Linux container-based applications. In addition, Red Hat Enterprise Linux 7.2 includes compatibility with the new Red Hat Insights, an add-on operational analytics offering designed to increase IT efficiency and reduce downtime through the proactive identification of known risks and technical issues.

Red Hat Enterprise Linux 7 Atomic Host & Containers

Retaining Red Hat's commitment to security, including meeting the needs of financial, government, and military customers, Red Hat Enterprise Linux 7.2 continues to provide new security capabilities and features. True security requires both a secure foundation and secure configuration of systems. OpenSCAP is an implementation of the Security Content Automation Protocol that analyzes a system for security compliance. The new OpenSCAP Anaconda plug-in allows use of SCAP based security and configuration analysis during the installation process, ensuring a secure starting point for system deployment.

Container security: Do containers actually contain? Should you care? - 2015 Red Hat Summit

A critical part of secure distributed systems is being able to trust the address resolution performed by DNS servers. DNSSEC extends DNS to provide a secure chain of trust for address resolution. The Red Hat Identity Management system (IdM) now supports DNSSEC for DNS zones.

Beyond Containers: Agility and Security in Docker Delivery

Networking performance in Red Hat Enterprise Linux 7.2 has been significantly improved -- with throughput doubled in many network function virtualization (NFV) and software defined networking (SDN) use cases. Other enhancements to the kernel networking subsystem, include:

Tuning the network kernel stack to dramatically improve packet processing time, enable Red Hat Enterprise Linux 7.2 to perform at physical line rates in advanced (virtual and containerized) workloads.
Inclusion of the Data Plane Development Kit (DPDK), which makes it possible to rapidly develop low-latency and high throughput custom applications capable of direct packet processing in user space for NFV and other use cases. Prior to this enhancement, systems were limited to running only one type of application (DPDK-enabled or traditional-network enabled.) Enhancements in Red Hat Enterprise LInux 7.2, specifically the introduction of a new bifurcated driver, now allow for both types of applications to be hosted on the same system thus consolidating physical hardware.
The addition of TCP (DCTCP), a feature for solving TCP congestion problems in data centers that works smoothly across Windows- and Red Hat Enterprise Linux-based hosts to maximize throughput and efficiency.

Linux Containers
Red Hat Enterprise Linux 7.2 features many improvements to the underlying container support infrastructure. Updates are included for the docker engine, Kubernetes, Cockpit and the Atomic command. In addition, Red Hat Enterprise Linux Atomic Host 7.2, the latest version of Red Hat's container workload-optimized host platform, is available with most Red Hat Enterprise Linux 7.2 subscriptions.

Super privileged containers - 2015 Red Hat Summit

Also available today is the beta of the Red Hat Container Development Kit 2, a collection of images, tools, and documentation to help application developers simplify the creation of container-based applications that are certified for deployment on Red Hat container hosts, including Red Hat Enterprise Linux 7.2, Red Hat Enterprise Linux Atomic Host 7.2 and OpenShift Enterprise 3.

System Administration
As managing the modern datacenter at scale becomes increasingly complex, Red Hat Enterprise Linux 7.2 includes new and improved tools to deliver a more streamlined system administration experience. Highlighting these updates is the inclusion of Relax-and-Recover, a system archiving tool that enables administrators to create local backups in ISO format that can be centrally archived and replicated remotely for simplified disaster recovery operations.

Red Hat Insights
Red Hat Enterprise Linux 7.2 is compatible with Red Hat Insights, an operational analytics service designed for the proactive management of Red Hat Enterprise Linux environments. Available for up to 10 Red Hat Enterprise Linux 7 systems at no additional cost, the offering is designed to help customers detect technical issues before they impact business operations by analyzing infrastructure assets and identifying key risks and vulnerabilities through continuous monitoring and analysis. Red Hat Insights provides resolution steps to help IT managers and administrators respond to these issues and potentially prevent future problems.

Red Hat Enterprise Linux Server for ARM 7.2 Development Preview
Red Hat is also making available Red Hat Enterprise Linux Server for ARM 7.2 Development Preview, which was first made available to partners and their customers in June 2015. This Development Preview enables new partner hardware and additional features for the ARM architecture.

Process-driven application development using Red Hat JBoss BPM Suite - 2015 Red Hat Summit

Supporting Quote
Jim Totton, vice president and general manager, Platforms Business Unit, Red Hat
“With the launch of Red Hat Enterprise Linux 7 in June 2014, Red Hat redefined the enterprise open source operating system. Red Hat Enterprise Linux 7.2 continues this effort, delivering new capabilities for containerized application deployments and significant networking enhancements while retaining our focus on delivering a stable, reliable and more secure platform for the most critical of business applications.”
With the launch of Red Hat Enterprise Linux 7 in June 2014, Red Hat redefined the enterprise open source operating system. Red Hat Enterprise Linux 7.2 continues this effort, delivering new capabilities for containerized application deployments and significant networking enhancements while retaining our focus on delivering a stable, reliable and more secure platform for the most critical of business applications.

More Information: