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  • New Feature in ODI 11.1.1.6: ODI for Big Data

    - by Julien Testut
    Normal 0 false false false EN-US X-NONE X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin:0in; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:10.0pt; font-family:"Calibri","sans-serif"; mso-bidi-font-family:"Times New Roman";} By Ananth Tirupattur Starting with Oracle Data Integrator 11.1.1.6.0, ODI is offering a solution to process Big Data. This post provides an overview of this feature. With all the buzz around Big Data and before getting into the details of ODI for Big Data, I will provide a brief introduction to Big Data and Oracle Solution for Big Data. So, what is Big Data? Big data includes: structured data (this includes data from relation data stores, xml data stores), semi-structured data (this includes data from weblogs) unstructured data (this includes data from text blob, images) Traditionally, business decisions are based on the information gathered from transactional data. For example, transactional Data from CRM applications is fed to a decision system for analysis and decision making. Products such as ODI play a key role in enabling decision systems. However, with the emergence of massive amounts of semi-structured and unstructured data it is important for decision system to include them in the analysis to achieve better decision making capability. While there is an abundance of opportunities for business for gaining competitive advantages, process of Big Data has challenges. The challenges of processing Big Data include: Volume of data Velocity of data - The high Rate at which data is generated Variety of data In order to address these challenges and convert them into opportunities, we would need an appropriate framework, platform and the right set of tools. Hadoop is an open source framework which is highly scalable, fault tolerant system, for storage and processing large amounts of data. Hadoop provides 2 key services, distributed and reliable storage called Hadoop Distributed File System or HDFS and a framework for parallel data processing called Map-Reduce. Innovations in Hadoop and its related technology continue to rapidly evolve, hence therefore, it is highly recommended to follow information on the web to keep up with latest information. Oracle's vision is to provide a comprehensive solution to address the challenges faced by Big Data. Oracle is providing the necessary Hardware, software and tools for processing Big Data Oracle solution includes: Big Data Appliance Oracle NoSQL Database Cloudera distribution for Hadoop Oracle R Enterprise- R is a statistical package which is very popular among data scientists. ODI solution for Big Data Oracle Loader for Hadoop for loading data from Hadoop to Oracle. Further details can be found here: http://www.oracle.com/us/products/database/big-data-appliance/overview/index.html ODI Solution for Big Data: ODI’s goal is to minimize the need to understand the complexity of Hadoop framework and simplify the adoption of processing Big Data seamlessly in an enterprise. ODI is providing the capabilities for an integrated architecture for processing Big Data. This includes capability to load data in to Hadoop, process data in Hadoop and load data from Hadoop into Oracle. ODI is expanding its support for Big Data by providing the following out of the box Knowledge Modules (KMs). IKM File to Hive (LOAD DATA).Load unstructured data from File (Local file system or HDFS ) into Hive IKM Hive Control AppendTransform and validate structured data on Hive IKM Hive TransformTransform unstructured data on Hive IKM File/Hive to Oracle (OLH)Load processed data in Hive to Oracle RKM HiveReverse engineer Hive tables to generate models Using the Loading KM you can map files (local and HDFS files) to the corresponding Hive tables. For example, you can map weblog files categorized by date into a corresponding partitioned Hive table schema. Normal 0 false false false EN-US X-NONE X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin:0in; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:10.0pt; font-family:"Calibri","sans-serif"; mso-bidi-font-family:"Times New Roman";} Using the Hive control Append KM you can validate and transform data in Hive. In the below example, two source Hive tables are joined and mapped to a target Hive table. Normal 0 false false false EN-US X-NONE X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin:0in; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:10.0pt; font-family:"Calibri","sans-serif"; mso-bidi-font-family:"Times New Roman";} The Hive Transform KM facilitates processing of semi-structured data in Hive. In the below example, the data from weblog is processed using a Perl script and mapped to target Hive table. Normal 0 false false false EN-US X-NONE X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin:0in; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:10.0pt; font-family:"Calibri","sans-serif"; mso-bidi-font-family:"Times New Roman";} Using the Oracle Loader for Hadoop (OLH) KM you can load data from Hive table or HDFS to a corresponding table in Oracle. OLH is available as a standalone product. ODI greatly enhances OLH capability by generating the configuration and mapping files for OLH based on the configuration provided in the interface and KM options. ODI seamlessly invokes OLH when executing the scenario. In the below example, a HDFS file is mapped to a table in Oracle. Development and Deployment:The following diagram illustrates the development and deployment of ODI solution for Big Data. Using the ODI Studio on your development machine create and develop ODI solution for processing Big Data by connecting to a MySQL DB or Oracle database on a BDA machine or Hadoop cluster. Schedule the ODI scenarios to be executed on the ODI agent deployed on the BDA machine or Hadoop cluster. ODI Solution for Big Data provides several exciting new capabilities to facilitate the adoption of Big Data in an enterprise. You can find more information about the Oracle Big Data connectors on OTN. You can find an overview of all the new features introduced in ODI 11.1.1.6 in the following document: ODI 11.1.1.6 New Features Overview

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  • Big GRC: Turning Data into Actionable GRC Intelligence

    - by Jenna Danko
    While it’s no longer headline news that Governments have carried out large scale data-mining programmes aimed at terrorism detection and identifying other patterns of interest across a wide range of digital data sources, the debate over the ethics and justification over this action, will clearly continue for some time to come. What is becoming clear is that these programmes are a framework for the collation and aggregation of massive amounts of unstructured data and from this, the creation of actionable intelligence from analyses that allowed the analysts to explore and extract a variety of patterns and then direct resources. This data included audio and video chats, phone calls, photographs, e-mails, documents, internet searches, social media posts and mobile phone logs and connections. Although Governance, Risk and Compliance (GRC) professionals are not looking at the implementation of such programmes, there are many similar GRC “Big data” challenges to be faced and potential lessons to be learned from these high profile government programmes that can be applied a lot closer to home. For example, how can GRC professionals collect, manage and analyze an enormous and disparate volume of data to create and manage their own actionable intelligence covering hidden signs and patterns of criminal activity, the early or retrospective, violation of regulations/laws/corporate policies and procedures, emerging risks and weakening controls etc. Not exactly the stuff of James Bond to be sure, but it is certainly more applicable to most GRC professional’s day to day challenges. So what is Big Data and how can it benefit the GRC process? Although it often varies, the definition of Big Data largely 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 and trading systems data. Social Data – includes customer feedback streams, micro-blogging sites like Twitter, and 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, according to sources such as Forrester there are four key characteristics that define big data: Volume. Machine-generated data is produced in much larger quantities than non-traditional data. This is all the data generated by IT systems that power the enterprise. This includes live data from packaged and custom applications – for example, app servers, Web servers, databases, networks, virtual machines, telecom equipment, and much more. 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 as well as offering early insight into potential reputational risk issues. Even at 140 characters per tweet, the high velocity (or frequency) of Twitter data ensures large volumes (over 8 TB per day) need to be managed. 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. Without question, all GRC professionals work in a dynamic environment and as new services, new products, new business lines are added or new marketing campaigns executed for example, new data types are needed to capture the resultant information.  Value. The economic value of data varies significantly. Typically, there is good information hidden amongst a larger body of non-traditional data that GRC professionals can use to add real value to the organisation; the greater challenge is identifying what is valuable and then transforming and extracting that data for analysis and action. For example, customer service calls and emails have millions of useful data points and have long been a source of information to GRC professionals. Those calls and emails are critical in helping GRC professionals better identify hidden patterns and implement new policies that can reduce the amount of customer complaints.   Now on a scale and depth far beyond those in place today, all that unstructured call and email data can be captured, stored and analyzed to reveal the reasons for the contact, perhaps with the aggregated customer results cross referenced against what is being said about the organization or a similar peer organization on social media. The organization can then take positive actions, communicating to the market in advance of issues reaching the press, strengthening controls, adjusting risk profiles, changing policy and procedures and completely minimizing, if not eliminating, complaints and compensation for that specific reason in the future. In this one example of many similar ones, the GRC team(s) has demonstrated real and tangible business value. Big Challenges - Big Opportunities As pointed out by recent Forrester research, high performing companies (those that are growing 15% or more year-on-year compared to their peers) are taking a selective approach to investing in Big Data.  "Tomorrow's winners understand this, and they are making selective investments aimed at specific opportunities with tangible benefits where big data offers a more economical solution to meet a need." (Forrsights Strategy Spotlight: Business Intelligence and Big Data, Q4 2012) As pointed out earlier, with the ever increasing volume of regulatory demands and fines for getting it wrong, limited resource availability and out of date or inadequate GRC systems all contributing to a higher cost of compliance and/or higher risk profile than desired – a big data investment in GRC clearly falls into this category. However, to make the most of big data organizations must evolve both their business and IT procedures, processes, people and infrastructures to handle these new high-volume, high-velocity, high-variety sources of data and be able integrate them with the pre-existing company data to be analyzed. GRC big data clearly allows the organization access to and management over a huge amount of often very sensitive information that although can help create a more risk intelligent organization, also presents numerous data governance challenges, including regulatory compliance and information security. In addition to client and regulatory demands over better information security and data protection the sheer amount of information organizations deal with the need to quickly access, classify, protect and manage that information can quickly become a key issue  from a legal, as well as technical or operational standpoint. However, by making information governance processes a bigger part of everyday operations, organizations can make sure data remains readily available and protected. The Right GRC & Big Data Partnership Becomes Key  The "getting it right first time" mantra used in so many companies remains essential for any GRC team that is sponsoring, helping kick start, or even overseeing a big data project. To make a big data GRC initiative work and get the desired value, partnerships with companies, who have a long history of success in delivering successful GRC solutions as well as being at the very forefront of technology innovation, becomes key. Clearly solutions can be built in-house more cheaply than through vendor, but as has been proven time and time again, when it comes to self built solutions covering AML and Fraud for example, few have able to scale or adapt appropriately to meet the changing regulations or challenges that the GRC teams face on a daily basis. This has led to the creation of GRC silo’s that are causing so many headaches today. The solutions that stand out and should be explored are the ones that can seamlessly merge the traditional world of well-known data, analytics and visualization with the new world of seemingly innumerable data sources, utilizing Big Data technologies to generate new GRC insights right across the enterprise.Ultimately, Big Data is here to stay, and organizations that embrace its potential and outline a viable strategy, as well as understand and build a solid analytical foundation, will be the ones that are well positioned to make the most of it. A Blueprint and Roadmap Service for Big Data Big data adoption is first and foremost a business decision. As such it is essential that your partner can align your strategies, goals, and objectives with an architecture vision and roadmap to accelerate adoption of big data for your environment, as well as establish practical, effective governance that will maintain a well managed environment going forward. Key Activities: While your initiatives will clearly vary, there are some generic starting points the team and organization will need to complete: Clearly define your drivers, strategies, goals, objectives and requirements as it relates to big data Conduct a big data readiness and Information Architecture maturity assessment Develop future state big data architecture, including views across all relevant architecture domains; business, applications, information, and technology Provide initial guidance on big data candidate selection for migrations or implementation Develop a strategic roadmap and implementation plan that reflects a prioritization of initiatives based on business impact and technology dependency, and an incremental integration approach for evolving your current state to the target future state in a manner that represents the least amount of risk and impact of change on the business Provide recommendations for practical, effective Data Governance, Data Quality Management, and Information Lifecycle Management to maintain a well-managed environment Conduct an executive workshop with recommendations and next steps There is little debate that managing risk and data are the two biggest obstacles encountered by financial institutions.  Big data is here to stay and risk management certainly is not going anywhere, and ultimately financial services industry organizations that embrace its potential and outline a viable strategy, as well as understand and build a solid analytical foundation, will be best positioned to make the most of it. Matthew Long is a Financial Crime Specialist for Oracle Financial Services. He can be reached at matthew.long AT oracle.com.

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  • BIG IP - HTTPS Health Monitor setup

    - by djo
    I have a Web site that we have setup a health monitoring pages so we can take our servers in and out of the Big-IP as we see fit. Now we have just moved onto Big-IP and the issue I have hit is that you setup Health Monitors for port 80 and 443, now the 80 check works fine but when I to get the 443 check to look at our file it fails. Now I am aware as I am hitting the this page on the IP address over HTTPS is going to cause a cert error but I would have guessed that BIG-Ip would have been setup just to accept the cert and carry on with the check. Is what I am wanting to do possible? Also is there a way of just using a HTTP monitor for HTTPS? Because if port 80 has stopped sending traffic then if i use the same monitor for 443 it will stop traffic to that. Any help would be great! Thanks

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  • Big Data – Interacting with Hadoop – What is PIG? – What is PIG Latin? – Day 16 of 21

    - by Pinal Dave
    In yesterday’s blog post we learned the importance of the HIVE in Big Data Story. In this article we will understand what is PIG and PIG Latin in Big Data Story. Yahoo started working on Pig for their application deployment on Hadoop. The goal of Yahoo to manage their unstructured data. What is Pig and What is Pig Latin? Pig is a high level platform for creating MapReduce programs used with Hadoop and the language we use for this platform is called PIG Latin. The pig was designed to make Hadoop more user-friendly and approachable by power-users and nondevelopers. PIG is an interactive execution environment supporting Pig Latin language. The language Pig Latin has supported loading and processing of input data with series of transforming to produce desired results. PIG has two different execution environments 1) Local Mode – In this case all the scripts run on a single machine. 2) Hadoop – In this case all the scripts run on Hadoop Cluster. Pig Latin vs SQL Pig essentially creates set of map and reduce jobs under the hoods. Due to same users does not have to now write, compile and build solution for Big Data. The pig is very similar to SQL in many ways. The Ping Latin language provide an abstraction layer over the data. It focuses on the data and not the structure under the hood. Pig Latin is a very powerful language and it can do various operations like loading and storing data, streaming data, filtering data as well various data operations related to strings. The major difference between SQL and Pig Latin is that PIG is procedural and SQL is declarative. In simpler words, Pig Latin is very similar to SQ Lexecution plan and that makes it much easier for programmers to build various processes. Whereas SQL handles trees naturally, Pig Latin follows directed acyclic graph (DAG). DAGs is used to model several different kinds of structures in mathematics and computer science. DAG Tomorrow In tomorrow’s blog post we will discuss about very important components of the Big Data Ecosystem – Zookeeper. Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: Big Data, PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL

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  • How a .NET Programmer learn Big Data/Hadoop? [on hold]

    - by Smith Pascal Jr.
    I have been ASP.NET developer for sometime now and I have been reading a lot about Big Data- Hadoop and its future as to how it is the next technology in IT and how it would be useful to create million of jobs in US and elsewhere in the world. Now since Hadoop is an open source big data tool which is managed by Apache Server Foundation Group, I'm assuming I have to be well aware of JAVA - Correct me if I'm wrong. Moreover, How a .NET programmer can learn Big Data and its related technologies and can work professionally full time into this technology? What challenges and opportunities does a .NET professional face while changing the technology platform? Please advice. Thanks

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  • Choices in Architecture, Design, Algorithms, Data Structures for effective RDF Reasoning and Querying in a Big Data Environment [on hold]

    - by user2891213
    As part of my academic project I would like to know what choices in Architecture, Design, Algorithms, Data Structures do we need in order to provide effective and efficient RDF Reasoning and Querying in a Big Data Environment. Basically I want to get info regarding below points: What are the Systems and Software to get appropriate Architecture? What kind of API layer(s) would we need on top of the Big Data stores, to make this possible? The Indexing structures we will need. The appropriate Algorithms, and appropriate Algorithms for Query Planning across Big Data stores. The Performance Analysis and Cost Models we will need to justify the design decisions we have made along the way. Can anyone please provide pointers.. Thanks, David

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  • Big Data – How to become a Data Scientist and Learn Data Science? – Day 19 of 21

    - by Pinal Dave
    In yesterday’s blog post we learned the importance of the analytics in Big Data Story. In this article we will understand how to become a Data Scientist for Big Data Story. Data Scientist is a new buzz word, everyone seems to be wanting to become Data Scientist. Let us go over a few key topics related to Data Scientist in this blog post. First of all we will understand what is a Data Scientist. In the new world of Big Data, I see pretty much everyone wants to become Data Scientist and there are lots of people I have already met who claims that they are Data Scientist. When I ask what is their role, I have got a wide variety of answers. What is Data Scientist? Data scientists are the experts who understand various aspects of the business and know how to strategies data to achieve the business goals. They should have a solid foundation of various data algorithms, modeling and statistics methodology. What do Data Scientists do? Data scientists understand the data very well. They just go beyond the regular data algorithms and builds interesting trends from available data. They innovate and resurrect the entire new meaning from the existing data. They are artists in disguise of computer analyst. They look at the data traditionally as well as explore various new ways to look at the data. Data Scientists do not wait to build their solutions from existing data. They think creatively, they think before the data has entered into the system. Data Scientists are visionary experts who understands the business needs and plan ahead of the time, this tremendously help to build solutions at rapid speed. Besides being data expert, the major quality of Data Scientists is “curiosity”. They always wonder about what more they can get from their existing data and how to get maximum out of future incoming data. Data Scientists do wonders with the data, which goes beyond the job descriptions of Data Analysist or Business Analysist. Skills Required for Data Scientists Here are few of the skills a Data Scientist must have. Expert level skills with statistical tools like SAS, Excel, R etc. Understanding Mathematical Models Hands-on with Visualization Tools like Tableau, PowerPivots, D3. j’s etc. Analytical skills to understand business needs Communication skills On the technology front any Data Scientists should know underlying technologies like (Hadoop, Cloudera) as well as their entire ecosystem (programming language, analysis and visualization tools etc.) . Remember that for becoming a successful Data Scientist one require have par excellent skills, just having a degree in a relevant education field will not suffice. Final Note Data Scientists is indeed very exciting job profile. As per research there are not enough Data Scientists in the world to handle the current data explosion. In near future Data is going to expand exponentially, and the need of the Data Scientists will increase along with it. It is indeed the job one should focus if you like data and science of statistics. Courtesy: emc Tomorrow In tomorrow’s blog post we will discuss about various Big Data Learning resources. Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: Big Data, PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL

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  • Big Data – Interacting with Hadoop – What is Sqoop? – What is Zookeeper? – Day 17 of 21

    - by Pinal Dave
    In yesterday’s blog post we learned the importance of the Pig and Pig Latin in Big Data Story. In this article we will understand what is Sqoop and Zookeeper in Big Data Story. There are two most important components one should learn when learning about interacting with Hadoop – Sqoop and Zookper. What is Sqoop? Most of the business stores their data in RDBMS as well as other data warehouse solutions. They need a way to move data to the Hadoop system to do various processing and return it back to RDBMS from Hadoop system. The data movement can happen in real time or at various intervals in bulk. We need a tool which can help us move this data from SQL to Hadoop and from Hadoop to SQL. Sqoop (SQL to Hadoop) is such a tool which extract data from non-Hadoop data sources and transform them into the format which Hadoop can use it and later it loads them into HDFS. Essentially it is ETL tool where it Extracts, Transform and Load from SQL to Hadoop. The best part is that it also does extract data from Hadoop and loads them to Non-SQL (or RDBMS) data stores. Essentially, Sqoop is a command line tool which does SQL to Hadoop and Hadoop to SQL. It is a command line interpreter. It creates MapReduce job behinds the scene to import data from an external database to HDFS. It is very effective and easy to learn tool for nonprogrammers. What is Zookeeper? ZooKeeper is a centralized service for maintaining configuration information, naming, providing distributed synchronization, and providing group services. In other words Zookeeper is a replicated synchronization service with eventual consistency. In simpler words – in Hadoop cluster there are many different nodes and one node is master. Let us assume that master node fails due to any reason. In this case, the role of the master node has to be transferred to a different node. The main role of the master node is managing the writers as that task requires persistence in order of writing. In this kind of scenario Zookeeper will assign new master node and make sure that Hadoop cluster performs without any glitch. Zookeeper is the Hadoop’s method of coordinating all the elements of these distributed systems. Here are few of the tasks which Zookeepr is responsible for. Zookeeper manages the entire workflow of starting and stopping various nodes in the Hadoop’s cluster. In Hadoop cluster when any processes need certain configuration to complete the task. Zookeeper makes sure that certain node gets necessary configuration consistently. In case of the master node fails, Zookeepr can assign new master node and make sure cluster works as expected. There many other tasks Zookeeper performance when it is about Hadoop cluster and communication. Basically without the help of Zookeeper it is not possible to design any new fault tolerant distributed application. Tomorrow In tomorrow’s blog post we will discuss about very important components of the Big Data Ecosystem – Big Data Analytics. Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: Big Data, PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL

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  • Big IP F5 Basics (show run/show conf/term len 0)

    - by PP
    I've tried to find the basics in a Big IP manual but it seems to me the device is marketed towards GUI users only. Meanwhile I want to write a few scripts to automate tasks on the load balancer. Namely: how do I turn off more - when I issue a command I want the output to stream out without waiting for me to press a key for the next page how do I show the running configuration (I think list all is the way to do it but cannot find it documented anywhere) Thanks!

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  • Replicate a big, dense Windows volume over a WAN -- too big for DFS-R

    - by Jesse
    I've got a server with a LOT of small files -- many millions files, and over 1.5 TB of data. I need a decent backup strategy. Any filesystem-based backup takes too long -- just enumerating which files need to be copied takes a day. Acronis can do a disk image in 24 hours, but fails when it tries to do a differential backup the next day. DFS-R won't replicate a volume with this many files. I'm starting to look at Double Take, which seems to be able to do continuous replication. Are there other solutions that can do continuous replication at a block or sector level -- not file-by-file over a WAN?

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  • How to calculate order (big O) for more complex algorithms (ie quicksort)

    - by bangoker
    I know there are quite a bunch of questions about big O notation, I have already checked Plain english explanation of Big O , Big O, how do you calculate/approximate it?, and Big O Notation Homework--Code Fragment Algorithm Analysis?, to name a few. I know by "intuition" how to calculate it for n, n^2, n! and so, however I am completely lost on how to calculate it for algorithms that are log n , n log n, n log log n and so. What I mean is, I know that Quick Sort is n log n (on average).. but, why? Same thing for merge/comb, etc. Could anybody explain me in a not to math-y way how do you calculate this? The main reason is that Im about to have a big interview and I'm pretty sure they'll ask for this kind of stuff. I have researched for a few days now, and everybody seem to have either an explanation of why bubble sort is n^2 or the (for me) unreadable explanation a la wikipedia Thanks!

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  • F5 Big-IP iRule - HTTP Redirect

    - by djo
    I have just started to work with F5's Big-IP and I have a question about iRules and HTTP redirects. We are moving to offload our SSL from our web servers and onto the F5, our application as it stands enforces a number of pages on our site to only run in HTTPS. I want to move this from the APP and onto the F5 but I have not been able to figure our how, so as an example I would want anyone trying to login in to be forced to use HTTPS e.g. http://"mysite"/login.aspx = https://"mysite"/login.aspx. I have done some google searches that have come up with some good info on this but I have yet to find what I am looking for, if anyone has done this and wishes to share this with me that would be great

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  • Big IP F5 outbound HTTP issues

    - by mbuk2k
    We've tried upgrading from 9.x to 10.2 on our F5 Big IP 3400 and everything went over fine apart from one thing. We're unable to establish any outbound HTTP (80) connections from any servers that are assigned to a virtual server. This is something that worked before and is required for certain calls our servers need to make. Interestingly HTTPS (443) connections work fine, it's literally just anything outbound over port 80 seems to fail. Does anyone know if anything has changed between 9.4 and 10.2 that would mean additional config would need to be made to allow for external HTTP connections? Any advice would be appreciated, thank you

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  • Big Data Matters with ODI12c

    - by Madhu Nair
    contributed by Mike Eisterer On October 17th, 2013, Oracle announced the release of Oracle Data Integrator 12c (ODI12c).  This release signifies improvements to Oracle’s Data Integration portfolio of solutions, particularly Big Data integration. Why Big Data = Big Business Organizations are gaining greater insights and actionability through increased storage, processing and analytical benefits offered by Big Data solutions.  New technologies and frameworks like HDFS, NoSQL, Hive and MapReduce support these benefits now. As further data is collected, analytical requirements increase and the complexity of managing transformations and aggregations of data compounds and organizations are in need for scalable Data Integration solutions. ODI12c provides enterprise solutions for the movement, translation and transformation of information and data heterogeneously and in Big Data Environments through: The ability for existing ODI and SQL developers to leverage new Big Data technologies. A metadata focused approach for cataloging, defining and reusing Big Data technologies, mappings and process executions. Integration between many heterogeneous environments and technologies such as HDFS and Hive. Generation of Hive Query Language. Working with Big Data using Knowledge Modules  ODI12c provides developers with the ability to define sources and targets and visually develop mappings to effect the movement and transformation of data.  As the mappings are created, ODI12c leverages a rich library of prebuilt integrations, known as Knowledge Modules (KMs).  These KMs are contextual to the technologies and platforms to be integrated.  Steps and actions needed to manage the data integration are pre-built and configured within the KMs.  The Oracle Data Integrator Application Adapter for Hadoop provides a series of KMs, specifically designed to integrate with Big Data Technologies.  The Big Data KMs include: Check Knowledge Module Reverse Engineer Knowledge Module Hive Transform Knowledge Module Hive Control Append Knowledge Module File to Hive (LOAD DATA) Knowledge Module File-Hive to Oracle (OLH-OSCH) Knowledge Module  Nothing to beat an Example: To demonstrate the use of the KMs which are part of the ODI Application Adapter for Hadoop, a mapping may be defined to move data between files and Hive targets.  The mapping is defined by dragging the source and target into the mapping, performing the attribute (column) mapping (see Figure 1) and then selecting the KM which will govern the process.  In this mapping example, movie data is being moved from an HDFS source into a Hive table.  Some of the attributes, such as “CUSTID to custid”, have been mapped over. Figure 1  Defining the Mapping Before the proper KM can be assigned to define the technology for the mapping, it needs to be added to the ODI project.  The Big Data KMs have been made available to the project through the KM import process.   Generally, this is done prior to defining the mapping. Figure 2  Importing the Big Data Knowledge Modules Following the import, the KMs are available in the Designer Navigator. v\:* {behavior:url(#default#VML);} o\:* {behavior:url(#default#VML);} w\:* {behavior:url(#default#VML);} .shape {behavior:url(#default#VML);} Normal 0 false false false EN-US ZH-TW X-NONE MicrosoftInternetExplorer4 /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin:0in; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:10.0pt; font-family:"Calibri","sans-serif"; mso-bidi-font-family:"Times New Roman";} Figure 3  The Project View in Designer, Showing Installed IKMs Once the KM is imported, it may be assigned to the mapping target.  This is done by selecting the Physical View of the mapping and examining the Properties of the Target.  In this case MOVIAPP_LOG_STAGE is the target of our mapping. Figure 4  Physical View of the Mapping and Assigning the Big Data Knowledge Module to the Target Alternative KMs may have been selected as well, providing flexibility and abstracting the logical mapping from the physical implementation.  Our mapping may be applied to other technologies as well. The mapping is now complete and is ready to run.  We will see more in a future blog about running a mapping to load Hive. To complete the quick ODI for Big Data Overview, let us take a closer look at what the IKM File to Hive is doing for us.  ODI provides differentiated capabilities by defining the process and steps which normally would have to be manually developed, tested and implemented into the KM.  As shown in figure 5, the KM is preparing the Hive session, managing the Hive tables, performing the initial load from HDFS and then performing the insert into Hive.  HDFS and Hive options are selected graphically, as shown in the properties in Figure 4. Figure 5  Process and Steps Managed by the KM What’s Next Big Data being the shape shifting business challenge it is is fast evolving into the deciding factor between market leaders and others. Now that an introduction to ODI and Big Data has been provided, look for additional blogs coming soon using the Knowledge Modules which make up the Oracle Data Integrator Application Adapter for Hadoop: Importing Big Data Metadata into ODI, Testing Data Stores and Loading Hive Targets Generating Transformations using Hive Query language Loading Oracle from Hadoop Sources For more information now, please visit the Oracle Data Integrator Application Adapter for Hadoop web site, http://www.oracle.com/us/products/middleware/data-integration/hadoop/overview/index.html Do not forget to tune in to the ODI12c Executive Launch webcast on the 12th to hear more about ODI12c and GG12c. Normal 0 false false false EN-US ZH-TW X-NONE MicrosoftInternetExplorer4 /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin:0in; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:10.0pt; font-family:"Calibri","sans-serif"; mso-bidi-font-family:"Times New Roman";}

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  • Bridging Two Worlds: Big Data and Enterprise Data

    - by Dain C. Hansen
    Normal 0 false false false EN-US X-NONE X-NONE MicrosoftInternetExplorer4 /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin:0in; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri","sans-serif"; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-fareast-font-family:"Times New Roman"; mso-fareast-theme-font:minor-fareast; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:minor-bidi;} The big data world is all the vogue in today’s IT conversations. It’s a world of volume, velocity, variety – tantalizing us with its untapped potential. It’s a world of transformational game-changing technologies that have already begun to alter the information management landscape. One of the reasons that big data is so compelling is that it’s a universal challenge that impacts every one of us. Whether it is healthcare, financial, manufacturing, government, retail - big data presents a pressing problem for many industries: how can so much information be processed so quickly to deliver the ‘bigger’ picture? With big data we’re tapping into new information that didn’t exist before: social data, weblogs, sensor data, complex content, and more. What also makes big data revolutionary is that it turns traditional information architecture on its head, putting into question commonly accepted notions of where and how data should be aggregated processed, analyzed, and stored. This is where Hadoop and NoSQL come in – new technologies which solve new problems for managing unstructured data. And now for some worst practices that I'd recommend that you please not follow: Worst Practice Lesson 1: Throw away everything that you already know about data management, data integration tools, and start completely over. One shouldn’t forget what’s already running in today’s IT. Today’s Business Analytics, Data Warehouses, Business Applications (ERP, CRM, SCM, HCM), and even many social, mobile, cloud applications still rely almost exclusively on structured data – or what we’d like to call enterprise data. This dilemma is what today’s IT leaders are up against: what are the best ways to bridge enterprise data with big data? And what are the best strategies for dealing with the complexities of these two unique worlds? Worst Practice Lesson 2: Throw away all of your existing business applications … because they don’t run on big data yet. Bridging the two worlds of big data and enterprise data means considering solutions that are complete, based on emerging Hadoop technologies (as well as traditional), and are poised for success through integrated design tools, integrated platforms that connect to your existing business applications, as well as and support real-time analytics. Leveraging these types of best practices translates to improved productivity, lowered TCO, IT optimization, and better business insights. Worst Practice Lesson 3: Separate out [and keep separate] your big data sandboxes from all the current enterprise IT systems. Don’t mix sand among playgrounds. We didn't tell you that you wouldn't get dirty doing this. Correlation between the two worlds is key. The real advantage to analyzing big data comes when you can correlate it with the existing data in your data warehouse or your current applications to make sense of the larger patterns. If you have not followed these worst practices 1-3 then you qualify for the first step of our journey: bridging the two worlds of enterprise data and big data. Over the next several weeks we’ll be discussing this topic along with several others around big data as it relates to data integration. We welcome you to join us in the conversation by following us on twitter on #BridgingBigData or download our latest white paper and resource kit: Big Data and Enterprise Data: Bridging Two Worlds.

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  • Programmaticaly finding the Landau notation (Big O or Theta notation) of an algorithm?

    - by Julien L
    I'm used to search for the Landau (Big O, Theta...) notation of my algorithms by hand to make sure they are as optimized as they can be, but when the functions are getting really big and complex, it's taking way too much time to do it by hand. it's also prone to human errors. I spent some time on Codility (coding/algo exercises), and noticed they will give you the Landau notation for your submitted solution (both in Time and Memory usage). I was wondering how they do that... How would you do it? Is there another way besides Lexical Analysis or parsing of the code? PS: This question concerns mainly PHP and or JavaScript, but I'm opened to any language and theory.

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  • Big-O complexity of c^n + n*(logn)^2 + (10*n)^c

    - by zebraman
    I need to derive the Big-O complexity of this expression: c^n + n*(log(n))^2 + (10*n)^c where c is a constant and n is a variable. I'm pretty sure I understand how to derive the Big-O complexity of each term individually, I just don't know how the Big-O complexity changes when the terms are combined like this. Ideas? Any help would be great, thanks.

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  • TDWI World Conference Features Oracle and Big Data

    - by Mandy Ho
    Oracle is a Gold Sponsor at this year's TDWI World Conference Series, held at the Manchester Grand Hyatt in San Diego, California - July 31 to Aug 1. The theme of this event is Big Data Tipping Point: BI Strategies in the Era of Big Data. The conference features an educational look at how data is now being generated so quickly that organizations across all industries need new technologies to stay ahead - to understand customer behavior, detect fraud, improve processes and accelerate performance. Attendees will hear how the internet, social media and streaming data are fundamentally changing business intelligence and data warehousing. Big data is reaching critical mass - the tipping point. Oracle will be conducting the following Evening Workshop. To reserve your space, call 1.800.820.5592 ext 10775. Title...:    Integrating Big Data into Your Data Center (or A Big Data Reference Architecture) Date.:    Wed., August 1, 2012, at 7:00 p.m Venue:: Manchester Grand Hyatt, San Diego, Room Weblogs, Social Media, smart meters, senors and other devices generate high volumes of low density information that isn't readily accessible in enterprise data warehouses and business intelligence applications today. But, this data can have relevant business value, especially when analyzed alongside traditional information sources. In this session, we will outline a reference architecture for big data that will help you maximize the value of your big data implementation. You will learn: The key technologies in a big architecture, and their specific use case The integration point of the various technologies and how they fit into your existing IT environment How effectively leverage analytical sandboxes for data discovery and agile development of data driven solutions   At the end of this session you will understand the reference architecture and have the tools to implement this architecture at your company. Presenter: Jean-Pierre Dijcks, Senior Principal Product Manager Don't miss our booth and the chance to meet with our Big data experts on the exhibition floor at booth #306. 

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  • Big Data: Size isn’t everything

    - by Simon Elliston Ball
    Big Data has a big problem; it’s the word “Big”. These days, a quick Google search will uncover terabytes of negative opinion about the futility of relying on huge volumes of data to produce magical, meaningful insight. There are also many clichéd but correct assertions about the difficulties of correlation versus causation, in massive data sets. In reading some of these pieces, I begin to understand how climatologists must feel when people complain ironically about “global warming” during snowfall. Big Data has a name problem. There is a lot more to it than size. Shape, Speed, and…err…Veracity are also key elements (now I understand why Gartner and the gang went with V’s instead of S’s). The need to handle data of different shapes (Variety) is not new. Data developers have always had to mold strange-shaped data into our reporting systems, integrating with semi-structured sources, and even straying into full-text searching. However, what we lacked was an easy way to add semi-structured and unstructured data to our arsenal. New “Big Data” tools such as MongoDB, and other NoSQL (Not Only SQL) databases, or a graph database like Neo4J, fill this gap. Still, to many, they simply introduce noise to the clean signal that is their sensibly normalized data structures. What about speed (Velocity)? It’s not just high frequency trading that generates data faster than a single system can handle. Many other applications need to make trade-offs that traditional databases won’t, in order to cope with high data insert speeds, or to extract quickly the required information from data streams. Unfortunately, many people equate Big Data with the Hadoop platform, whose batch driven queries and job processing queues have little to do with “velocity”. StreamInsight, Esper and Tibco BusinessEvents are examples of Big Data tools designed to handle high-velocity data streams. Again, the name doesn’t do the discipline of Big Data any favors. Ultimately, though, does analyzing fast moving data produce insights as useful as the ones we get through a more considered approach, enabled by traditional BI? Finally, we have Veracity and Value. In many ways, these additions to the classic Volume, Velocity and Variety trio acknowledge the criticism that without high-quality data and genuinely valuable outputs then data, big or otherwise, is worthless. As a discipline, Big Data has recognized this, and data quality and cleaning tools are starting to appear to support it. Rather than simply decrying the irrelevance of Volume, we need as a profession to focus how to improve Veracity and Value. Perhaps we should just declare the ‘Big’ silent, embrace these new data tools and help develop better practices for their use, just as we did the good old RDBMS? What does Big Data mean to you? Which V gives your business the most pain, or the most value? Do you see these new tools as a useful addition to the BI toolbox, or are they just enabling a dangerous trend to find ghosts in the noise?

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  • When is BIG, big enough for a database?

    - by David ???
    I'm developing a Java application that has performance at its core. I have a list of some 40,000 "final" objects, i.e., I have an initialization input data of 40,000 vectors. This data is unchanged throughout the program's run. I am always preforming lookups against a single ID property to retrieve the proper vectors. Currently I am using a HashMap over a sub-sample of a 1,000 vectors, but I'm not sure it will scale to production. When is BIG, actually big enough for a use of DB? One more thing, an SQLite DB is a viable option as no concurrency is involved, so I guess the "threshold" for db use, is perhaps lower.

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  • Proving that a function f(n) belongs to a Big-Theta(g(n))

    - by PLS
    Its a exercise that ask to indicate the class Big-Theta(g(n)) the functions belongs to and to prove the assertion. In this case f(n) = (n^2+1)^10 By definition f(n) E Big-Theta(g(n)) <= c1*g(n) < f(n) < c2*g(n), where c1 and c2 are two constants. I know that for this specific f(n) the Big-Theta is g(n^20) but I don't know who to prove it properly. I guess I need to manipulate this inequality but I don't know how

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  • Running a simple integration scenario using the Oracle Big Data Connectors on Hadoop/HDFS cluster

    - by hamsun
    Between the elephant ( the tradional image of the Hadoop framework) and the Oracle Iron Man (Big Data..) an english setter could be seen as the link to the right data Data, Data, Data, we are living in a world where data technology based on popular applications , search engines, Webservers, rich sms messages, email clients, weather forecasts and so on, have a predominant role in our life. More and more technologies are used to analyze/track our behavior, try to detect patterns, to propose us "the best/right user experience" from the Google Ad services, to Telco companies or large consumer sites (like Amazon:) ). The more we use all these technologies, the more we generate data, and thus there is a need of huge data marts and specific hardware/software servers (as the Exadata servers) in order to treat/analyze/understand the trends and offer new services to the users. Some of these "data feeds" are raw, unstructured data, and cannot be processed effectively by normal SQL queries. Large scale distributed processing was an emerging infrastructure need and the solution seemed to be the "collocation of compute nodes with the data", which in turn leaded to MapReduce parallel patterns and the development of the Hadoop framework, which is based on MapReduce and a distributed file system (HDFS) that runs on larger clusters of rather inexpensive servers. Several Oracle products are using the distributed / aggregation pattern for data calculation ( Coherence, NoSql, times ten ) so once that you are familiar with one of these technologies, lets says with coherence aggregators, you will find the whole Hadoop, MapReduce concept very similar. Oracle Big Data Appliance is based on the Cloudera Distribution (CDH), and the Oracle Big Data Connectors can be plugged on a Hadoop cluster running the CDH distribution or equivalent Hadoop clusters. In this paper, a "lab like" implementation of this concept is done on a single Linux X64 server, running an Oracle Database 11g Enterprise Edition Release 11.2.0.4.0, and a single node Apache hadoop-1.2.1 HDFS cluster, using the SQL connector for HDFS. The whole setup is fairly simple: Install on a Linux x64 server ( or virtual box appliance) an Oracle Database 11g Enterprise Edition Release 11.2.0.4.0 server Get the Apache Hadoop distribution from: http://mir2.ovh.net/ftp.apache.org/dist/hadoop/common/hadoop-1.2.1. Get the Oracle Big Data Connectors from: http://www.oracle.com/technetwork/bdc/big-data-connectors/downloads/index.html?ssSourceSiteId=ocomen. Check the java version of your Linux server with the command: java -version java version "1.7.0_40" Java(TM) SE Runtime Environment (build 1.7.0_40-b43) Java HotSpot(TM) 64-Bit Server VM (build 24.0-b56, mixed mode) Decompress the hadoop hadoop-1.2.1.tar.gz file to /u01/hadoop-1.2.1 Modify your .bash_profile export HADOOP_HOME=/u01/hadoop-1.2.1 export PATH=$PATH:$HADOOP_HOME/bin export HIVE_HOME=/u01/hive-0.11.0 export PATH=$PATH:$HADOOP_HOME/bin:$HIVE_HOME/bin (also see my sample .bash_profile) Set up ssh trust for Hadoop process, this is a mandatory step, in our case we have to establish a "local trust" as will are using a single node configuration copy the new public keys to the list of authorized keys connect and test the ssh setup to your localhost: We will run a "pseudo-Hadoop cluster", in what is called "local standalone mode", all the Hadoop java components are running in one Java process, this is enough for our demo purposes. We need to "fine tune" some Hadoop configuration files, we have to go at our $HADOOP_HOME/conf, and modify the files: core-site.xml hdfs-site.xml mapred-site.xml check that the hadoop binaries are referenced correctly from the command line by executing: hadoop -version As Hadoop is managing our "clustered HDFS" file system we have to create "the mount point" and format it , the mount point will be declared to core-site.xml as: The layout under the /u01/hadoop-1.2.1/data will be created and used by other hadoop components (MapReduce = /mapred/...) HDFS is using the /dfs/... layout structure format the HDFS hadoop file system: Start the java components for the HDFS system As an additional check, you can use the GUI Hadoop browsers to check the content of your HDFS configurations: Once our HDFS Hadoop setup is done you can use the HDFS file system to store data ( big data : )), and plug them back and forth to Oracle Databases by the means of the Big Data Connectors ( which is the next configuration step). You can create / use a Hive db, but in our case we will make a simple integration of "raw data" , through the creation of an External Table to a local Oracle instance ( on the same Linux box, we run the Hadoop HDFS one node cluster and one Oracle DB). Download some public "big data", I use the site: http://france.meteofrance.com/france/observations, from where I can get *.csv files for my big data simulations :). Here is the data layout of my example file: Download the Big Data Connector from the OTN (oraosch-2.2.0.zip), unzip it to your local file system (see picture below) Modify your environment in order to access the connector libraries , and make the following test: [oracle@dg1 bin]$./hdfs_stream Usage: hdfs_stream locationFile [oracle@dg1 bin]$ Load the data to the Hadoop hdfs file system: hadoop fs -mkdir bgtest_data hadoop fs -put obsFrance.txt bgtest_data/obsFrance.txt hadoop fs -ls /user/oracle/bgtest_data/obsFrance.txt [oracle@dg1 bg-data-raw]$ hadoop fs -ls /user/oracle/bgtest_data/obsFrance.txt Found 1 items -rw-r--r-- 1 oracle supergroup 54103 2013-10-22 06:10 /user/oracle/bgtest_data/obsFrance.txt [oracle@dg1 bg-data-raw]$hadoop fs -ls hdfs:///user/oracle/bgtest_data/obsFrance.txt Found 1 items -rw-r--r-- 1 oracle supergroup 54103 2013-10-22 06:10 /user/oracle/bgtest_data/obsFrance.txt Check the content of the HDFS with the browser UI: Start the Oracle database, and run the following script in order to create the Oracle database user, the Oracle directories for the Oracle Big Data Connector (dg1 it’s my own db id replace accordingly yours): #!/bin/bash export ORAENV_ASK=NO export ORACLE_SID=dg1 . oraenv sqlplus /nolog <<EOF CONNECT / AS sysdba; CREATE OR REPLACE DIRECTORY osch_bin_path AS '/u01/orahdfs-2.2.0/bin'; CREATE USER BGUSER IDENTIFIED BY oracle; GRANT CREATE SESSION, CREATE TABLE TO BGUSER; GRANT EXECUTE ON sys.utl_file TO BGUSER; GRANT READ, EXECUTE ON DIRECTORY osch_bin_path TO BGUSER; CREATE OR REPLACE DIRECTORY BGT_LOG_DIR as '/u01/BG_TEST/logs'; GRANT READ, WRITE ON DIRECTORY BGT_LOG_DIR to BGUSER; CREATE OR REPLACE DIRECTORY BGT_DATA_DIR as '/u01/BG_TEST/data'; GRANT READ, WRITE ON DIRECTORY BGT_DATA_DIR to BGUSER; EOF Put the following in a file named t3.sh and make it executable, hadoop jar $OSCH_HOME/jlib/orahdfs.jar \ oracle.hadoop.exttab.ExternalTable \ -D oracle.hadoop.exttab.tableName=BGTEST_DP_XTAB \ -D oracle.hadoop.exttab.defaultDirectory=BGT_DATA_DIR \ -D oracle.hadoop.exttab.dataPaths="hdfs:///user/oracle/bgtest_data/obsFrance.txt" \ -D oracle.hadoop.exttab.columnCount=7 \ -D oracle.hadoop.connection.url=jdbc:oracle:thin:@//localhost:1521/dg1 \ -D oracle.hadoop.connection.user=BGUSER \ -D oracle.hadoop.exttab.printStackTrace=true \ -createTable --noexecute then test the creation fo the external table with it: [oracle@dg1 samples]$ ./t3.sh ./t3.sh: line 2: /u01/orahdfs-2.2.0: Is a directory Oracle SQL Connector for HDFS Release 2.2.0 - Production Copyright (c) 2011, 2013, Oracle and/or its affiliates. All rights reserved. Enter Database Password:] The create table command was not executed. The following table would be created. CREATE TABLE "BGUSER"."BGTEST_DP_XTAB" ( "C1" VARCHAR2(4000), "C2" VARCHAR2(4000), "C3" VARCHAR2(4000), "C4" VARCHAR2(4000), "C5" VARCHAR2(4000), "C6" VARCHAR2(4000), "C7" VARCHAR2(4000) ) ORGANIZATION EXTERNAL ( TYPE ORACLE_LOADER DEFAULT DIRECTORY "BGT_DATA_DIR" ACCESS PARAMETERS ( RECORDS DELIMITED BY 0X'0A' CHARACTERSET AL32UTF8 STRING SIZES ARE IN CHARACTERS PREPROCESSOR "OSCH_BIN_PATH":'hdfs_stream' FIELDS TERMINATED BY 0X'2C' MISSING FIELD VALUES ARE NULL ( "C1" CHAR(4000), "C2" CHAR(4000), "C3" CHAR(4000), "C4" CHAR(4000), "C5" CHAR(4000), "C6" CHAR(4000), "C7" CHAR(4000) ) ) LOCATION ( 'osch-20131022081035-74-1' ) ) PARALLEL REJECT LIMIT UNLIMITED; The following location files would be created. osch-20131022081035-74-1 contains 1 URI, 54103 bytes 54103 hdfs://localhost:19000/user/oracle/bgtest_data/obsFrance.txt Then remove the --noexecute flag and create the external Oracle table for the Hadoop data. Check the results: The create table command succeeded. CREATE TABLE "BGUSER"."BGTEST_DP_XTAB" ( "C1" VARCHAR2(4000), "C2" VARCHAR2(4000), "C3" VARCHAR2(4000), "C4" VARCHAR2(4000), "C5" VARCHAR2(4000), "C6" VARCHAR2(4000), "C7" VARCHAR2(4000) ) ORGANIZATION EXTERNAL ( TYPE ORACLE_LOADER DEFAULT DIRECTORY "BGT_DATA_DIR" ACCESS PARAMETERS ( RECORDS DELIMITED BY 0X'0A' CHARACTERSET AL32UTF8 STRING SIZES ARE IN CHARACTERS PREPROCESSOR "OSCH_BIN_PATH":'hdfs_stream' FIELDS TERMINATED BY 0X'2C' MISSING FIELD VALUES ARE NULL ( "C1" CHAR(4000), "C2" CHAR(4000), "C3" CHAR(4000), "C4" CHAR(4000), "C5" CHAR(4000), "C6" CHAR(4000), "C7" CHAR(4000) ) ) LOCATION ( 'osch-20131022081719-3239-1' ) ) PARALLEL REJECT LIMIT UNLIMITED; The following location files were created. osch-20131022081719-3239-1 contains 1 URI, 54103 bytes 54103 hdfs://localhost:19000/user/oracle/bgtest_data/obsFrance.txt This is the view from the SQL Developer: and finally the number of lines in the oracle table, imported from our Hadoop HDFS cluster SQL select count(*) from "BGUSER"."BGTEST_DP_XTAB"; COUNT(*) ---------- 1151 In a next post we will integrate data from a Hive database, and try some ODI integrations with the ODI Big Data connector. Our simplistic approach is just a step to show you how these unstructured data world can be integrated to Oracle infrastructure. Hadoop, BigData, NoSql are great technologies, they are widely used and Oracle is offering a large integration infrastructure based on these services. Oracle University presents a complete curriculum on all the Oracle related technologies: NoSQL: Introduction to Oracle NoSQL Database Using Oracle NoSQL Database Big Data: Introduction to Big Data Oracle Big Data Essentials Oracle Big Data Overview Oracle Data Integrator: Oracle Data Integrator 12c: New Features Oracle Data Integrator 11g: Integration and Administration Oracle Data Integrator: Administration and Development Oracle Data Integrator 11g: Advanced Integration and Development Oracle Coherence 12c: Oracle Coherence 12c: New Features Oracle Coherence 12c: Share and Manage Data in Clusters Oracle Coherence 12c: Oracle GoldenGate 11g: Fundamentals for Oracle Oracle GoldenGate 11g: Fundamentals for SQL Server Oracle GoldenGate 11g Fundamentals for Oracle Oracle GoldenGate 11g Fundamentals for DB2 Oracle GoldenGate 11g Fundamentals for Teradata Oracle GoldenGate 11g Fundamentals for HP NonStop Oracle GoldenGate 11g Management Pack: Overview Oracle GoldenGate 11g Troubleshooting and Tuning Oracle GoldenGate 11g: Advanced Configuration for Oracle Other Resources: Apache Hadoop : http://hadoop.apache.org/ is the homepage for these technologies. "Hadoop Definitive Guide 3rdEdition" by Tom White is a classical lecture for people who want to know more about Hadoop , and some active "googling " will also give you some more references. About the author: Eugene Simos is based in France and joined Oracle through the BEA-Weblogic Acquisition, where he worked for the Professional Service, Support, end Education for major accounts across the EMEA Region. He worked in the banking sector, ATT, Telco companies giving him extensive experience on production environments. Eugen currently specializes in Oracle Fusion Middleware teaching an array of courses on Weblogic/Webcenter, Content,BPM /SOA/Identity-Security/GoldenGate/Virtualisation/Unified Comm Suite) throughout the EMEA region.

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  • How meaningful is the Big-O time complexity of an algorithm?

    - by james creasy
    Programmers often talk about the time complexity of an algorithm, e.g. O(log n) or O(n^2). Time complexity classifications are made as the input size goes to infinity, but ironically infinite input size in computation is not used. Put another way, the classification of an algorithm is based on a situation that algorithm will never be in: where n = infinity. Also, consider that a polynomial time algorithm where the exponent is huge is just as useless as an exponential time algorithm with tiny base (e.g., 1.00000001^n) is useful. Given this, how much can I rely on the Big-O time complexity to advise choice of an algorithm?

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