Thursday Jun 26, 2014

Big Data Breakthrough; Watch the Webcast on July 15th

Thursday Jun 05, 2014

Globacom and mCentric Deploy BDA and NoSQL Database to analyze network traffic 40x faster

In a fast evolving market, speed is of the essence. mCentric and Globacom leveraged Big Data Appliance, Oracle NoSQL Database to save over 35,000 Call-Processing minutes daily and analyze network traffic 40x faster. 

Here are some highlights from the profile:

Why Oracle

“Oracle Big Data Appliance works well for very large amounts of structured and unstructured data. It is the most agile events-storage system for our collect-it-now and analyze-it-later set of business requirements. Moreover, choosing a prebuilt solution drastically reduced implementation time. We got the big data benefits without needing to assemble and tune a custom-built system, and without the hidden costs required to maintain a large number of servers in our data center. A single support license covers both the hardware and the integrated software, and we have one central point of contact for support,” said Sanjib Roy, CTO, Globacom.

Implementation Process

It took only five days for Oracle partner mCentric to deploy Oracle Big Data Appliance, perform the software install and configuration, certification, and resiliency testing. The entire process—from site planning to phase-I, go-live—was executed in just over ten weeks, well ahead of the four months allocated to complete the project.

Oracle partner mCentric leveraged Oracle Advanced Customer Support Services’ implementation methodology to ensure configurations are tailored for peak performance, all patches are applied, and software and communications are consistently tested using proven methodologies and best practices.

Read the entire profile here.

Tuesday May 13, 2014

Announcing: Big Data Lite VM 3.0

Last week we released Big Data Lite VM 3.0. It contains the latest update on the VM for the entire stack.

Oracle Enterprise Linux 6.4
Oracle Database 12c Release 1 Enterprise Edition ( with Oracle Advanced Analytics, Spatial & Graph, and more
Cloudera’s Distribution including Apache Hadoop (CDH 5.0)
Oracle Big Data Connectors 3.0
        Oracle SQL Connector for HDFS 3.0.0
        Oracle Loader for Hadoop 3.0.0
        Oracle Data Integrator 12c
        Oracle R Advanced Analytics for Hadoop 2.4.0
        Oracle XQuery for Hadoop 3.0.0
Oracle NoSQL Database Enterprise Edition 12cR1 (3.0.5)
Oracle JDeveloper 11g
Oracle SQL Developer 4.0
Oracle Data Integrator 12cR1
Oracle R Distribution 3.0.1

The download page is on OTN in its usual place.

Tuesday Apr 22, 2014

Announcing: Big Data Appliance 3.0 and Big Data Connectors 3.0

Today we are releasing Big Data Appliance 3.0 (which includes the just released Oracle NoSQL Database 3.0) and Big Data Connectors 3.0.These releases deliver a large number of interesting and cool features and enhance the overall Oracle Big Data Management System that we think is going to be the core of information management going forward.

This post highlights a few of the new enhancements across the BDA, NoSQL DB and BDC stack.

Big Data Appliance 3.0:
  • Pre-configured and pre-installed CDH 5.0 with default support for YARN and MR2
  • Upgrade from BDA 2.5 (CDH 4.6) to BDA 3.0 (CDH 5.0)
  • Full encryption (at rest and over the network) from a single vendor in an appliance
  • Kerberos and Apache Sentry pre-configured
  • Partition Pruning through Oracle SQL Connector for Hadoop
  • Apache Spark (incl. Spark Streaming) support
  • More
Oracle NoSQL Database 3.0:
  • Table data model support layered on top of distributed key-value model
  • Support for Secondary Indexing
  • Support for "Data Centers" => Metro zones for DR and secondary zones for read-only workloads
  • Authentication and network encryption
  • More

You can read about all of these features by going to links above and reading the OTN page, data sheets and other relevant information.

While BDA 3.0 immediately delivers upgrade from BDA 2.5, Oracle will also support the current version and we fully expect more BDA 2.x releases based on more CDH 4.x releases. As a customer you now have a choice how to deploy BDA and which version it is you want to run, while knowing you can upgrade to the latest and greatest in a safe manner.

Thursday Apr 03, 2014

Updated: Price Comparison for Big Data Appliance and Hadoop

Untitled Document

It was time to update this post a little. Big Data Appliance grew, got more features and prices as well as insights just changed all across the board. So, here is an update.

The post is still aimed at providing a simple apples-to-apples comparison and a clarification of what is, and what is not included in the pricing and packaging of Oracle Big Data Appliance when compared to "I'm doing this myself - DIY style".

Oracle Big Data Appliance Details

A few of the most overlooked items in pricing out a Hadoop cluster are the cost of software, the cost of actual production-ready hardware and the required networking equipment. A Hadoop cluster needs more than just CPUs and disks... For Oracle Big Data Appliance we assume that you would want to run this system as a production system (with hot-pluggable components and redundant components in your system). We also assume you want the leading Hadoop distribution plus support for that software. You'd want to look at securing the cluster and possibly encrypting data at rest and over the network. Speaking of network, InfiniBand will eliminate network saturation issues - which is important for your Hadoop cluster.

With that in mind, Oracle Big Data Appliance is an engineered system built for production clusters.  It is pre-installed and pre-configured with Cloudera CDH and all (I emphasize all!) options included and we (with the help of Cloudera of course) have done the tuning of the system for you. On top of that, the price of the hardware (US$ 525,000 for a full rack system - more configs and smaller sizes => read more) includes the cost of Cloudera CDH, its options and Cloudera Manager (for the life of the machine - so not a subscription).

So, for US$ 525,000 you get the following:

  • Big Data Appliance Hardware (comes with Automatic Service Request upon component failures)
  • Cloudera CDH and Cloudera Manager
  • All Cloudera options as well as Accumulo and Spark (CDH 5.0)
  • Oracle Linux and the Oracle JDK
  • Oracle Distribution of R
  • Oracle NoSQL Database Community Edition
  • Oracle Big Data Appliance Enterprise Manager Plug-In

The support cost for the above is a single line item.. The list price for Premier Support for Systems per the Oracle Price list (see source below) is US$ 63,000 per year.

To do a simple 3 year comparison with other systems, the following table shows the details and the totals for Oracle Big Data Appliance. Note that the only additional item is the install and configuration cost which are done by Oracle personnel or partners, on-site:

Year 1 Year 2 Year 3 3 Year
BDA Cost

Annual Support Cost

On-site Install (approximately)


For this you will get a full rack BDA (18 Sun X4-2L servers, 288 cores (Two Intel Xeon E5-2650V2 CPUs per node), 864TB disk (twelve 4TB disks per node), plus software, plus support, plus on-site setup and configuration. Or in terms of cost per raw TB at purchase and at list pricing: $697.

HP DL-380 Comparative System (this is changed from the original post to the more common DL-380's)

To build a comparative hardware solution to the Big Data Appliance we picked an HP-DL180 configuration and built up the servers using the website for pricing. The following is the price for a single server.

Model Number Description Quantity Total Price
653200-B21 ProLiant DL380p Gen8 Rackmount Factory Integrated 8 SFF CTO Model (2U) with no processor, 24 DIMM with no memory, open bay (diskless) with 8 SFF drive cage, Smart Array P420i controller with Zero Memory, 3 x PCIe 3.0 slots, 1 FlexibleLOM connector, no power supply, 4 x redundant fans, Integrated HP iLO Management Engine
2.6GHz Xeon E5-2650 v2 processor (1 chip, 8 cores) with 20MB L3 cache - Factory Integrated Only
HP 1GbE 4-port 331FLR Adapter - Factory Integrated Only
460W Common Slot Gold Hot Plug Power Supply
HP Rack 10000 G2 Series - 10842 (42U) 800mm Wide Cabinet - Pallet Universal Rack
8GB (1 x 8GB) Single Rank x8 PC3L-12800R (DDR3-1600) Registered CAS-11 Low Voltage Memory Kit
HP Smart Array P222/512MB FBWC 6Gb 1-port Int/1-port Ext SAS controller 1
4TB 6Gb SAS 7.2K LFF hot-plug SmartDrive SC Midline disk drive (3.5") with 1-year warranty

Grand Total for a single server (list prices)


On top of this we need InfiniBand switches. Oracle Big Data Appliance comes with 3 IB switches, allowing us to expand the cluster without suddenly requiring extra switches. And, we do expect these machines to be a part of a much larger clusters. The IB switches are somewhere in the neighborhood of US$ 6,000 per switch, so add $18,000 per rack and add a management switch (BDA uses a Cisco switch) which seems to be around $15,000 list. The total switching comes to roughly $33,000.

We will also need Cloudera Enterprise subscription - and to compare apples to apples, we will do it for all software. Some sources (see this document) peg CDH Core at $3,382 list per node and per year (24*7 support). Since BDA has more software (all options) and that pricing is not public I am going to make an educated calculation and rounding and double the price with a rounding to the nearest nice and round number. That gets me to $7,000 per node, per year for 24*7 support. 

BDA also comes with on-disk encryption, which is even harder to price out. My somewhat educated guess is around $1,500 list or so per node and per year. Oh, and lets not forget the Linux subscription, which lists at $1,299 per node per year. We also run a MySQL database (enterprise edition with replication), which costs list subscription $5,000. We run it replicated over 2 nodes.

This all gets us to roughly $10,000 list price per node per year for all applicable software subscriptions and support and an additional $10,000 for the two MySQL nodes.

HP + Cloudera Do-it-Yourself System

Let's go build our own system. The specs are like a BDA, so we will have 18 servers and all other components included. 

Year 1 Year 2 Year 3 Total




SW Subscriptions and Support

Installation and Configuration


Some will argue that the installation and configuration is free (you already pay your data center team), but I would argue that something that takes a short amount of time when done by Oracle, is worth the equivalent if it takes you a lot longer to get all this installed, optimized, and running. Nevertheless, here is some math on how to get to that cost anyways: approximately 150 hours of labor per rack for the pure install work. That adds up to US $15,000 if we assume a cost per hour of $100. 

Note: those $15,000 do NOT include optimizations and tuning to Hadoop, to the OS, to Java and other interesting things like networking settings across all these areas. You will now need to spend time to figure out the number of slots you allocate per node, the file system block size (do you use Apache defaults, or Cloudera's or something else) and many more things at system level. On top of that, we pre-configure for example Kerberos and Apache Sentry giving you a secure authorization and authentication method, as well as have a one-click on-disk and network encryption setting. Of course you can contact various other companies to do this for you.

You can also argue that "you want the cheapest hardware possible", because Hadoop is built to deal with failures, so it is OK for things to regularly fail. Yes, Hadoop does deal well with hardware failures, but your data center is probably much less keen about this idea, because someone is going to replace the disks (all the time). So make sure the disks are hot-swappable. An oh, that someone swapping the disks does cost money... The other consideration is failures in important components like power... redundant power in a rack is a good thing to have. All of this is included (and thought about) in Oracle Big Data Appliance.

In other words, do you really want spend weeks installing, configuring and learning or would you rather start to build applications on top of the Hadoop cluster and thus providing value to your organization.

The Differences

The main differences between Oracle Big Data Appliance and a DIY approach are:

  1. A DIY system - at list price with basic installation but no optimization - is a staggering $220 cheaper as an initial purchase
  2. A DIY system - at list price with basic installation but no optimization - is almost $250,000 more expensive over 3 years.
    Note to purchasing, you can spend this on building or buying applications on your cluster (or buy some real intriguing Oracle software)
  3. The support for the DIY system includes five (5) vendors. Your hardware support vendor, the OS vendor, your Hadoop vendor, your encryption vendor as well as your database vendor. Oracle Big Data Appliance is supported end-to-end by a single vendor: Oracle
  4. Time to value. While we trust that your IT staff will get the DIY system up and running, the Oracle system allows for a much faster "loading dock to loading data" time. Typically a few days instead of a few weeks (or even months)
  5. Oracle Big Data Appliance is tuned and configured to take advantage of the software stack, the CPUs and InfiniBand network it runs on
  6. Any issue we, you or any other BDA customer finds in the system is fixed for all customers. You do not have a unique configuration, with unique issues on top of the generic issues.


In an apples-to-apples comparison of a production Hadoop cluster, Oracle Big Data Appliance starts of with the same acquisition prices and comes out ahead in terms of TCO over 3 years. It allows an organization to enter the Hadoop world with a production-grade system in a very short time reducing both risk as well as reducing time to market.

As always, when in doubt, simply contact your friendly Oracle representative for questions, support and detailed quotes.


HP and related pricing: or (the latter is a paid service - sorry!)
Oracle Pricing:
MySQL Pricing:

Wednesday Mar 26, 2014

Oracle Big Data Lite Virtual Machine - Version 2.5 Now Available

Oracle Big Data Appliance Version 2.5 was released last week.  Some great new features in this release- including a continued security focus (on-disk encryption and automated configuration of Sentry for data authorization) and updates to Cloudera Distribution of Apache Hadoop and Cloudera Manager.

With each BDA release, we have a new release of Oracle Big Data Lite Virtual Machine.  Oracle Big Data Lite provides an integrated environment to help you get started with the Oracle Big Data platform. Many Oracle Big Data platform components have been installed and configured - allowing you to begin using the system right away. The following components are included on Oracle Big Data Lite Virtual Machine v 2.5:

  • Oracle Enterprise Linux 6.4
  • Oracle Database 12c Release 1 Enterprise Edition (
  • Cloudera’s Distribution including Apache Hadoop (CDH4.6)
  • Cloudera Manager 4.8.2
  • Cloudera Enterprise Technology, including:
    • Cloudera RTQ (Impala 1.2.3)
    • Cloudera RTS (Search 1.2)
  • Oracle Big Data Connectors 2.5
    • Oracle SQL Connector for HDFS 2.3.0
    • Oracle Loader for Hadoop 2.3.1
    • Oracle Data Integrator 11g
    • Oracle R Advanced Analytics for Hadoop 2.3.1
    • Oracle XQuery for Hadoop 2.4.0
  • Oracle NoSQL Database Enterprise Edition 12cR1 (2.1.54)
  • Oracle JDeveloper 11g
  • Oracle SQL Developer 4.0
  • Oracle Data Integrator 12cR1
  • Oracle R Distribution 3.0.1

Go to the Oracle Big Data Lite Virtual Machine landing page on OTN to download the latest release.

Monday Mar 24, 2014

Demonstration: Auditing Data Access Across the Enterprise

Security has been an important theme across recent Big Data Appliance releases. Our most recent release includes encryption of data at rest and automatic configuration of Sentry for data authorization. This is in addition to the security features previously added to the BDA, including Kerberos-based authentication, network encryption and auditing.

Auditing data access across the enterprise - including databases, operating systems and Hadoop - is critically important and oftentimes required for SOX, PCI and other regulations. Let's take a look at a demonstration of how Oracle Audit Vault and Database Firewall delivers comprehensive audit collection, alerting and reporting of activity on an Oracle Big Data Appliance and Oracle Database 12c. 


In this scenario, we've set up auditing for both the BDA and Oracle Database 12c.


The Audit Vault Server is deployed to its own secure server and serves as mission control for auditing. It is used to administer audit policies, configure activities that are tracked on the secured targets and provide robust audit reporting and alerting. In many ways, Audit Vault is a specialized auditing data warehouse. It automates ETL from a variety of sources into an audit schema and then delivers both pre-built and ad hoc reporting capabilities.

For our demonstration, Audit Vault agents are deployed to the BDA and Oracle Database 12c monitored targets; these agents are responsible for managing collectors that gather activity data. This is a secure agent deployment; the Audit Vault Server has a trusted relationship with each agent. To set up the trusted relationship, the agent makes an activation request to the Audit Vault Server; this request is then activated (or "approved") by the AV Administrator. The monitored target then applies an AV Server generated Agent Activation Key to complete the activation.


On the BDA, these installation and configuration steps have all been automated for you. Using the BDA's Configuration Generation Utility, you simply specify that you would like to audit activity in Hadoop. Then, you identify the Audit Vault Server that will receive the audit data. Mammoth - the BDA's installation tool - uses this information to configure the audit processing. Specifically, it sets up audit trails across the following services:

  • HDFS: collects all file access activity
  • MapReduce:  identifies who ran what jobs on the cluster
  • Oozie:  audits who ran what as part of a workflow
  • Hive:  captures changes that were made to the Hive metadata

There is much more flexibility when monitoring the Oracle Database. You can create audit policies for SQL statements, schema objects, privileges and more. Check out the auditor's guide for more details. In our demonstration, we kept it simple: we are capturing all select statements on the sensitive HR.EMPLOYEES table, all statements made by the HR user and any unsuccessful attempts at selecting from any table in any schema.

Now that we are capturing activity across the BDA and Oracle Database 12c, we'll set up an alert to fire whenever there is suspicious activity attempted over sensitive HR data in Hadoop:


In the alert definition found above, a critical alert is defined as three unsuccessful attempts from a given IP address to access data in the HR directory. Alert definitions are extremely flexible - using any audited field as input into a conditional expression. And, they are automatically delivered to the Audit Vault Server's monitoring dashboard - as well as via email to appropriate security administrators.

Now that auditing is configured, we'll generate activity by two different users: oracle and DrEvil. We'll then see how the audit data is consolidated in the Audit Vault Server and how auditors can interrogate that data.

Capturing Activity

The demonstration is driven by a few scripts that generate different types of activity by both the oracle and DrEvil users. These activities include:

  • an oozie workflow that removes salary data from HDFS
  • numerous HDFS commands that upload files, change file access privileges, copy files and list the contents of directories and files
  • hive commands that query, create, alter and drop tables
  • Oracle Database commands that connect as different users, create and drop users, select from tables and delete records from a table

After running the scripts, we log into the Audit Vault Server as an auditor. Immediately, we see our alert has been triggered by the users' activity.


Drilling down on the alert reveals DrEvil's three failed attempts to access the sensitive data in HDFS:

alert details

Now that we see the alert triggered in the dashboard, let's see what other activity is taking place on the BDA and in the Oracle Database.

Ad Hoc Reporting

Audit Vault Server delivers rich reporting capabilities that enables you to better understand the activity that has taken place across the enterprise. In addition to the numerous reports that are delivered out of box with Audit Vault, you can create your own custom reports that meet your own personal needs. Here, we are looking at a BDA monitoring report that focuses on Hadoop activities that occurred in the last 24 hours:

monitor events

As you can see, the report tells you all of the key elements required to understand: 1) when the activity took place, 2) the source service for the event, 3) what object was referenced, 4) whether or not the event was successful, 5) who executed the event, 6) the ip address (or host) that initiated the event, and 7) how the object was modified or accessed. Stoplight reporting is used to highlight critical activity - including DrEvils failed attempts to open the sensitive salaries.txt file.

Notice, events may be related to one another. The Hive command "ALTER TABLE my_salarys RENAME TO my_salaries" will generate two events. The first event is sourced from the Metastore; the alter table command is captured and the metadata definition is updated. The Hive command also impacts HDFS; the table name is represented by an HDFS folder. Therefore, an HDFS event is logged that renames the "my_salarys" folder to "my_salaries".

Next, consider an Oozie workflow that performs a simple task: delete a file "salaries2.txt" in HDFS. This Oozie worflow generates the following events:


  1. First, an Oozie workflow event is generated indicating the start of the workflow.
  2. The workflow definition is read from the "workflow.xml" file found in HDFS.
  3. An Oozie working directory is created
  4. The salaries2.txt file is deleted from HDFS
  5. Oozie runs its clean-up process

The Audit Vault reports are able to reveal all of the underlying activity that is executed by the Oozie workflow. It's flexible reporting allows you to sequence these independent events into a logical series of related activities.

The reporting focus so far has been on Hadoop - but one of the core strengths of Oracle Audit Vault is its ability to consolidate all audit data. We know that DrEvil had a few unsuccessful attempts to access sensitive salary data in HDFS. But, what other unsuccessful events have occured recently across our data platform? We'll use Audit Vault's ad hoc reporting capabilities to answer that question. Report filters enable users to search audit data based on a range of conditions. Here, we'll keep it pretty simple; let's find all failed access attempts across both the BDA and the Oracle Database within the last two hours:


Again, DrEvil's activity stands out. As you can see, DrEvil is attempting to access sensitive salary data not only in HDFS - but also in the Oracle Database.


Security and integration with the rest of the Oracle ecosystem are two tablestakes that are critical to Oracle Big Data Appliance releases. Oracle Audit Vault and Database Firewall's auditing of data across the BDA, databases and operating systems epitomizes this goal - providing a single repository and reporting environment for all your audit data.

Wednesday Mar 19, 2014

Announcing Encryption of Data-at-Rest on Big Data Appliance

With the release of Big Data Appliance software bundle 2.5, BDA completes the encryption story underneath Cloudera CDH. BDA already came with network encryption, ensuring no network sniffing can be applied in between the nodes, it now adds encryption of data-at-rest.

A Brief Overview

Encryption of data-at-rest can be done in 2 modes. One mode leverages the Trusted Platform Module (TPM) on the motherboard to provide a key to encrypt the data on disk. This mode does not require a password or pass phrase but relies on the motherboard. The second mode leverages a passphrase, which in turn will be used to generate a private-public key pair generated with OpenSSL. The key pair is encrypted as well.

The passphrase encryption has a few more interesting aspects. For one, it does require the passphrase to be entered upon re-booting the system. Leveraging the TPM option does not require any manual intervention at reboot. On Big Data Appliance it is possible to regularly change the passphrase without impacting the encryption, or required re-encryption of the data.

Neither one of the encryption methods affect user access to user data. In other words, on an unprotected cluster a user that can read data before encryption will be able to read data after encryption. The goal is to ensure data is protected on physical media - like theft or incorrect disposal of a disk. Both forms protect from that, but only passphrase based encryption protects from disposal or theft of a server.

On BDA, it is possible to switch between these two methods. This does have impact on running the cluster as data needs to be re-encrypted. For this step the cluster will be down, however data is not duplicated, so there is no need to reserve double the space to do the re-encryption.

How to Encrypt Data

As with all installation or changes on Big Data Appliance you will leverage Mammoth to do the install with encryption or to make changes to the system if you are already in production. Before you set up either of the two modes of data-at-rest encryption, you should consider your requirements. Changing the mode - as described - is possible, but will require the cluster to be down for re-encryption.

Full Set of Security Features

Encryption - out-of-the-box is yet another feature that is specific to Oracle Big Data Appliance. On top of pre-configured Kerberos, Apache Sentry, Oracle Audit Vault Encryption now adds another security dimension. To read more about the full set of features start here.

Thursday Mar 13, 2014

Video: Big Data Connectors and IDH (Strata)

The certification of Oracle Big Data Connectors on Intel Distribution for Hadoop now complete (see our previous post). This video from Strata gives you a nice overview of IDH and BDC.

Friday Mar 07, 2014

Intel® Distribution for Apache Hadoop* certified with Oracle Big Data Connectors

Intel partnered with Oracle to certify compatibility between Intel® Distribution for Apache Hadoop* (IDH) and Oracle Big Data Connectors*.  Users can now connect IDH to Oracle Database with Oracle Big Data Connectors, taking advantage of the high performance feature-rich components of that product suite. Applications on IDH can leverage the connectors for fast load into Oracle Database, in-place query of data in HDFS with Oracle SQL, analytics in Hadoop with R, XQuery processing on Hadoop, and native Hadoop integration within Oracle Data Integrator.

Read the whole post here.

Monday Jan 27, 2014

Announcing: Oracle Big Data Lite Virtual Machine

You've been hearing alot about Oracle's big data platform. Today, we're pleased to announce Oracle Big Data Lite Virtual Machine - an environment to help you get started with the platform. And, we have a great OTN Virtual Developer Day event scheduled where you can start using our big data products as part of a series of workshops.

BigDataLite Picture

Oracle Big Data Lite Virtual Machine is an Oracle VM VirtualBox that contains many key components of Oracle's big data platform, including: Oracle Database 12c Enterprise Edition, Oracle Advanced Analytics, Oracle NoSQL Database, Cloudera Distribution including Apache Hadoop, Oracle Data Integrator 12c, Oracle Big Data Connectors, and more. It's been configured to run on a "developer class" computer; all Big Data Lite needs is a couple of cores and about 5GB memory to run (this means that your computer should have at least 8GB total memory). With Big Data Lite, you can develop your big data applications and then deploy them to the Oracle Big Data Appliance. Or, you can use Big Data Lite as a client to the BDA during application development.

How do you get started? Why not start by registering for the Virtual Developer Day scheduled for Tuesday, February 4, 2014 - 9am  to 1pm PT / 12pm to 4pm ET / 3pm to 7pm BRT:


There will be 45 minute sessions delivered by product experts (from both Oracle and Oracle Aces) - highlighted by Tom Kyte and Jonathan Lewis' keynote "Landscape of Oracle Database Technology Evolution". Some of the big data technical sessions include:

  • Oracle NoSQL Database Installation and Cluster Topology Deployment
  • Application Development & Schema Design with Oracle NoSQL Database
  • Processing Twitter Data with Hadoop
  • Use Data from a Hadoop Cluster with Oracle Database
  • Make the Right Offers to Customers Using Oracle Advanced Analytics
  • In-DB Map Reduce with SQL/Hadoop
  • Pattern Matching in SQL

Keep an eye on this space - we'll be publishing how-to's that leverage the new Oracle Big Data Lite VM. And, of course, we'd love to hear about the clever applications you build as well!

Monday Jan 06, 2014

CaixaBank deploys new big data infrastructure on Oracle

CaixaBank is Spain’s largest domestic bank by market share with a customer base of 13.7 It is also Spain’s leading bank in terms of innovation and technology, and one of the most prominent innovators worldwide. CaixaBank has been recently awarded the title of the World’s Most Innovative Bank at the 2013 Global Banking Innovation Awards (November 2013).

Like most financial services companies CaixaBank wants to get closer to its customers by collecting data about their activities across all the different channels (offices, internet, phone banking, ATMs, etc.). In the old days we used to call this CRM and then this morphed into "360-degree view" etc etc. While many companies have delivered these types of projects and customers feel much more connected and in control of their relationship with their bank the capture of streams of big data has the potential to create another revolution in the way we interact with our bank. What banks like CaixaBank want to do is to capture data in one part of the business and make it available to all the other lines of business as quickly as possible.

Big data is allowing businesses like  CaixaBank to significantly enhance the business value of their existing customer data by integrating it with all sorts of other internal and external data sets. This is probably the most exciting part of big data because the potential business benefits are really only constrained by imagination of the team working on these type of projects. However, that in itself does create problems in terms of securing funding and ownership of projects because the benefits can be difficult to estimate which is where all the industry use cases, conference papers and blog posts can help in terms of providing insight into what is going on in across the market in broad general terms.

To help them implement a strategic Big Data project, CaixaBank has selected Oracle for the deployment of its new Big Data infrastructure. This project, which includes an array of Oracle solutions, positions CaixaBank at the forefront of innovation in the banking industry. The new infrastructure will allow CaixaBank to maximize the business value from any kind of data and embark on new business innovation projects based on valuable information gathered from large data sets. Projects currently under review include:

  • Development of predictive models to improve customer value
  • Identifying cross-selling and up-selling  opportunities
  • Development of personalized offers to customers
  • Reinforcement of risk management and brand protection services
  • More powerful fraud analysis 
  • General streamlining of current processes to reduce time-to-market
  • Support new regulatory requirements

The Oracle solution (including Oracle Engineered Systems, Oracle Software and Oracle Consulting Services) consists in the implementation of a new Information Management Architecture that provides a unified corporate data model and new advanced analytic capabilities (for more information about how Oracle's Reference Architecture can help you integrate structured, semi-structured and unstructured information into a single logical information resource that can be exploited for commercial gain click here to view our whitepaper)

The importance of the project is best explained by Juan Maria Nin, CEO of CaixaBank:

“Business innovation is the key for success in today’s highly competitive banking environment. The implementation of this Big Data solution will help CaixaBank remain at the forefront of innovation in the financial sector, delivering the best and most competitive services to our customers”.

The Oracle press release is here:

Tuesday Dec 03, 2013

Big Data - Real and Practical Use Cases: Expand the Data Warehouse

As a follow-on to the previous post (here) on use cases, follow the link below to a recording that explains how to go about expanding the data warehouse into a big data platform.

The idea behind it all is to cover a best practice on adding to the existing data warehouse and expanding the system (shown in the figure below) to deal with:

  • Real-Time ingest and reporting on large volumes of data
  • A cost effective strategy to analyze all data across types and at large volumes
  • Deliver SQL access and analytics to all data 
  • Deliver the best query performance to match the business requirements

Roles of the Components

To access the webcast:

  • Register on this page
  • Scroll down and find the session labeled "Best practices for expanding the data warehouse with new big data streams"
  • Have fun
you want the slides, go to slideshare.

Wednesday Nov 27, 2013

Big Data - Real and Practical Use Cases

The goal of this post is to explain in a few succinct patterns how organizations can start to work with big data and identify credible and doable big data projects. This goal is achieved by describing a set of general patterns that can be seen in the market today.
Big Data Usage Patterns
The following usage patterns are derived from actual customer projects across a large number of industries and cross boundaries between commercial enterprises and public sector. These patterns are also geographically applicable and technically feasible with today’s technologies.
This paper will address the following four usage patterns:
  • Data Factory – a pattern that enable an organization to integrate and transform – in a batch method – large diverse data sets before moving this data into an upstream system like an RDBMS or a NoSQL system. Data in the data factory is possibly transient and the focus is on data processing.
  • Data Warehouse Expansion with a Data Reservoir – a pattern that expands the data warehouse with a large scale Hadoop system to capture data at lower grain and higher diversity, which is then fed into upstream systems. Data in the data reservoir is persistent and the focus is on data processing as well as data storage as well as the reuse of data.
  • Information Discovery with a Data Reservoir – a pattern that creates a data reservoir for discovery data marts or discovery systems like Oracle Endeca to tap into a wide range of data elements. The goal is to simplify data acquisition into discovery tools and to initiate discovery on raw data.
  • Closed Loop Recommendation and Analytics system – a pattern that is often considered the holy grail of data systems. This pattern combines both analytics on historical data, event processing or real time actions on current events and closes the loop between the two to continuously improve real time actions based on current and historical event correlation.

Pattern 1: Data Factory

The core business reason to build a Data Factory as it is presented here is to implement a cost savings strategy by placing long-running batch jobs on a cheaper system. The project is often funded by not spending money on the more expensive system – for example by switching Mainframe MIPS off  - and instead leveraging that cost savings to fund the Data Factory. The first figure shows a simplified implementation of the Data Factory.
As the image below shows, the data factory must be scalable, flexible and (more) cost effective for processing the data. The typical system used to build a data factory is Apache Hadoop or in the case of Oracle’s Big Data Appliance – Cloudera’s Distribution including Apache Hadoop (CDH).

data factory

Hadoop (and therefore Big Data Appliance and CDH) offers an extremely scalable environment to process large data volumes (or a large number of small data sets) and jobs. Most typical is the offload of large batch updates, matching and de-duplication jobs etc. Hadoop also offers a very flexible model, where data is interpreted on read, rather than on write. This idea enables a data factory to quickly accommodate all types of data, which can then be processed in programs written in Hive, Pig or MapReduce.
As shown in above the data factory is an integration platform, much like an ETL tool. Data sets land in the data factory, batch jobs process data and this processed data moves into the upstream systems. These upstream systems include RDBMS’s which are then used for various information needs. In the case of a Data Warehouse, this is very close to pattern 2 described below, with the difference that in the data factory data is often transient and removed after the processing is done.
This transient nature of data is not a required feature, but it is often implemented to keep the Hadoop cluster relatively small. The aim is generally to just transform data in a more cost effective manner.
In the case of an upstream system in NoSQL systems, data is often prepared in a specific key-value format to be served up to end applications like a website. NoSQL databases work really well for that purpose, but the batch processing is better left to Hadoop cluster.
It is very common for data to flow in the reverse order or for data from RDBMS or NoSQL databases to flow into the data factory. In most cases this is reference data, like customer master data. In order to process new customer data, this master data is required in the Data Factory.
Because of its low risk profile – the logic of these batch processes is well known and understood – and funding from savings in other systems, the Data Factory is typically an IT department’s first attempt at a big data project. The down side of a Data Factory project is that business users see very little benefits in that they do not get new insights out of big data.

Pattern 2: Data Warehouse Expansion

The common way to drive new insights out of big data is pattern two. Expanding the data warehouse with a data reservoir enables an organization to expand the raw data captured in a system that is able to add agility to the organization. The graphical pattern is shown in below.

DW Expansion

A Data Reservoir – like the Data Factory from Pattern 1 – is based on Hadoop and Oracle Big Data Appliance, but rather then have transient data and just process data and then hand the data off, a Data Reservoir aims to store data at a lower than previously stored grain for a period much longer than previous periods.
The Data Reservoir is initially used to capture data, aggregate new metrics and augment (not replace) the data warehouse with new and expansive KPIs or context information. A very typical addition is the sentiment of a customer towards a product or brand which is added to a customer table in the data warehouse.
The addition of new KPIs or new context information is a continuous process. That is, new analytics on raw and correlated data should find their way into the upstream Data Warehouse on a very, very regular basis.
As the Data Reservoir grows and starts to become known to exist because of the new KPIs or context, users should start to look at the Data Reservoir as an environment to “experiment” and “play” with data. With some rudimentary programming skills power users can start to combine various data elements in the Data Reservoir, using for example Hive. This enables the users to verify a hypotheses without the need to build a new data mart. Hadoop and the Data Reservoir now becomes an economically viable sandbox for power users driving innovation, agility and possibly revenue from hitherto unused data.

Pattern 3: Information Discovery

Agility for power users and expert programmers is one thing, but eventually the goal is to enable business users to discover new and exciting things in the data. Pattern 3 combines the data reservoir with a special information discovery system to provide a Graphical User Interface specifically for data discovery. This GUI emulates in many ways how an end user today searches for information on the internet.
To empower a set of business users to truly discover information, they first and foremost require a Discovery tool. A project should therefore always start with that asset.

Once the Discovery tool (like Oracle Endeca) is in place, it pays to start to leverage the Data Reservoir to feed the Discovery tool. As is shown above, the Data Reservoir is continuously fed with new data. The Discovery tool is a business user’s tool to create ad-hoc data marts in the discovery tool. Having the Data Reservoir simplifies the acquisition by end users because they only need to look in one place for data.
In essence, the Data Reservoir now is used to drive two different systems; the Data Warehouse and the Information Discovery environment and in practice users will very quickly gravitate to the appropriate system. But no matter which system they use, they now have the ability to drive value from data into the organization.

Pattern 4: Closed Loop Recommendation and Analytics System

So far, most of what was discussed was analytics and batch based. But a lot of organizations want to come to some real time interaction model with their end customers (or in the world of the Internet of Things – with other machines and sensors).

Closed Loop System

Hadoop is very good at providing the Data Factory and the Data Reservoir, at providing a sandbox, at providing massive storage and processing capabilities, but it is less good at doing things in real time. Therefore, to build a closed loop recommendation system – which should react in real time – Hadoop is only one of the components .
Typically the bottom half of the last figure is akin to pattern 2 and is used to catch all data, analyze the correlations between recorded events (detected fraud for example) and generate a set of predictive models describing something like “if a, b and c during a transaction – mark as suspect and hand off to an agent”. This model would for example block a credit card transaction.
To make such a system work it is important to use the right technology at both levels. Real time technologies like Oracle NoSQL Database, Oracle Real Time Decisions and Oracle Event Processing work on the data stream in flight. Oracle Big Data Appliance, Oracle Exadata/Database and Oracle Advanced Analytics provide the infrastructure to create, refine and expose the models.


Today’s big data technologies offer a wide variety of capabilities. Leveraging these capabilities with the existing environment and skills already in place according to the four patterns described does enable an organization to benefit from big data today. It is a matter of identifying the applicable pattern for your organization and then to start on the implementation.

The technology is ready. Are you?

Thursday Nov 14, 2013

Data Scientist Boot camp (Skills and Training)

As almost everyone is interested in data science, take the boot camp to get ahead of the curve. Leverage this free Data Science Boot camp from Oracle Academy to learn some of the following things:

  • Introduction: Providing Data-Driven Answers to Business Questions
  • Lesson 1: Acquiring and Transforming Big Data
  • Lesson 2: Finding Value in Shopping Baskets
  • Lesson 3: Unsupervised Learning for Clustering
  • Lesson 4: Supervised Learning for Classification and Prediction
  • Lesson 5: Classical Statistics in a Big Data World
  • Lesson 6: Building and Exploring Graphs

You will also find the code samples that go with the training and you can get of to a running start.


The data warehouse insider is written by the Oracle product management team and sheds lights on all thing data warehousing and big data.


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