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• Big Data Videos

  - by Jean-Pierre Dijcks

  You can view them all on YouTube using the following links: Overview for the Boss: http://youtu.be/ikJyrmKdJWc Hadoop: http://youtu.be/acWtid-OOWM Acquiring Big Data: http://youtu.be/TfuhuA_uaho Organizing Big Data: http://youtu.be/IC6jVRO2Hq4 Analyzing Big Data: http://youtu.be/2yf_jrBhz5w These videos are a great place to start learning about big data, the value it can bring to your organisation and how Oracle can help you start working with big data today.

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• Winner of the 2012 Government Big Data Solutions Award

  - by Jean-Pierre Dijcks

  Hot off the press: The winner of the 2012 Government Big Data Solutions Aware is the National Cancer Institute!! Read all the details on CTOLabs.com. A short excerpt to wet your appetite: "... This solution, based on the Oracle Big Data Appliance with the Cloudera Distribution of Apache Hadoop (CDH), leverages capabilities available from the Big Data community today in pioneering ways that can serve a broad range of researchers. The promising approach of this solution is repeatable across many other Big Data challenges for bioinfomatics, making this approach worthy of its selection as the 2012 Government Big Data Solution Award." Read the entire post. Congrats to the entire team!!

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• The Oldest Big Data Problem: Parsing Human Language

  - by dan.mcclary

  There's a new whitepaper up on Oracle Technology Network which details the use of Digital Reasoning Systems' Synthesys software on Oracle Big Data Appliance.  Digital Reasoning's approach is inherently "big data friendly," as it leverages multiple components of the Hadoop ecosystem.  Moreover, the paper addresses the oldest big data problem of them all: extracting knowledge from human text.   You can find the paper here.   From the Executive Summary: There is a wealth of information to be extracted from natural language, but that extraction is challenging. The volume of human language we generate constitutes a natural Big Data problem, while its complexity and nuance requires a particular expertise to model and mine. In this paper we illustrate the impressive combination of Oracle Big Data Appliance and Digital Reasoning Synthesys software. The combination of Synthesys and Big Data Appliance makes it possible to analyze tens of millions of documents in a matter of hours. Moreover, this powerful combination achieves four times greater throughput than conducting the equivalent analysis on a much larger cloud-deployed Hadoop cluster.

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• Big Data Accelerator

  - by Jean-Pierre Dijcks

  For everyone who does not regularly listen to earnings calls, Oracle's Q4 call was interesting (as it mostly is). One of the announcements in the call was the Big Data Accelerator from Oracle (Seeking Alpha link here - slightly tweaked for correctness shown below):  "The big data accelerator includes some of the standard open source software, HDFS, the file system and a number of other pieces, but also some Oracle components that we think can dramatically speed up the entire map-reduce process. And will be particularly attractive to Java programmers [...]. There are some interesting applications they do, ETL is one. Log processing is another. We're going to have a lot of those features, functions and pre-built applications in our big data accelerator."  Not much else we can say right now, more on this (and Big Data in general) at Openworld!

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• Big Data – Buzz Words: What is HDFS – Day 8 of 21

  - by Pinal Dave

  In yesterday’s blog post we learned what is MapReduce. In this article we will take a quick look at one of the four most important buzz words which goes around Big Data – HDFS. What is HDFS ? HDFS stands for Hadoop Distributed File System and it is a primary storage system used by Hadoop. It provides high performance access to data across Hadoop clusters. It is usually deployed on low-cost commodity hardware. In commodity hardware deployment server failures are very common. Due to the same reason HDFS is built to have high fault tolerance. The data transfer rate between compute nodes in HDFS is very high, which leads to reduced risk of failure. HDFS creates smaller pieces of the big data and distributes it on different nodes. It also copies each smaller piece to multiple times on different nodes. Hence when any node with the data crashes the system is automatically able to use the data from a different node and continue the process. This is the key feature of the HDFS system. Architecture of HDFS The architecture of the HDFS is master/slave architecture. An HDFS cluster always consists of single NameNode. This single NameNode is a master server and it manages the file system as well regulates access to various files. In additional to NameNode there are multiple DataNodes. There is always one DataNode for each data server. In HDFS a big file is split into one or more blocks and those blocks are stored in a set of DataNodes. The primary task of the NameNode is to open, close or rename files and directory and regulate access to the file system, whereas the primary task of the DataNode is read and write to the file systems. DataNode is also responsible for the creation, deletion or replication of the data based on the instruction from NameNode. In reality, NameNode and DataNode are software designed to run on commodity machine build in Java language. Visual Representation of HDFS Architecture Let us understand how HDFS works with the help of the diagram. Client APP or HDFS Client connects to NameSpace as well as DataNode. Client App access to the DataNode is regulated by NameSpace Node. NameSpace Node allows Client App to connect to the DataNode based by allowing the connection to the DataNode directly. A big data file is divided into multiple data blocks (let us assume that those data chunks are A,B,C and D. Client App will later on write data blocks directly to the DataNode. Client App does not have to directly write to all the node. It just has to write to any one of the node and NameNode will decide on which other DataNode it will have to replicate the data. In our example Client App directly writes to DataNode 1 and detained 3. However, data chunks are automatically replicated to other nodes. All the information like in which DataNode which data block is placed is written back to NameNode. High Availability During Disaster Now as multiple DataNode have same data blocks in the case of any DataNode which faces the disaster, the entire process will continue as other DataNode will assume the role to serve the specific data block which was on the failed node. This system provides very high tolerance to disaster and provides high availability. If you notice there is only single NameNode in our architecture. If that node fails our entire Hadoop Application will stop performing as it is a single node where we store all the metadata. As this node is very critical, it is usually replicated on another clustered as well as on another data rack. Though, that replicated node is not operational in architecture, it has all the necessary data to perform the task of the NameNode in the case of the NameNode fails. The entire Hadoop architecture is built to function smoothly even there are node failures or hardware malfunction. It is built on the simple concept that data is so big it is impossible to have come up with a single piece of the hardware which can manage it properly. We need lots of commodity (cheap) hardware to manage our big data and hardware failure is part of the commodity servers. To reduce the impact of hardware failure Hadoop architecture is built to overcome the limitation of the non-functioning hardware. Tomorrow In tomorrow’s blog post we will discuss the importance of the relational database in Big Data. 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 Learning Resources

  - by Lara Rubbelke

  I have recently had several requests from people asking for resources to learn about Big Data and Hadoop. Below is a list of resources that I typically recommend. I'll update this list as I find more resources. Let's crowdsource this... Tell me your favorite resources and I'll get them on the list! Books and Whitepapers Planning for Big Data Free e-book Great primer on the general Big Data space. This is always my recommendation for people who are new to Big Data and are trying to understand it....(read more)

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• E-Book on big data (featuring Analysts, Customers and more)

  - by Jean-Pierre Dijcks

  As we are gearing up for Openworld, here is a nice E-book on big data to start paging through. It contains Gartner's take on big data, customer and partner interviews and a lot more good info. Enjoy the read so you come prepared for Openworld!! Read the E-Book here. For those coming to Oracle Openworld (or the Americas Cup races around the same time), you can find big data sessions via this URL. Enjoy!!

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• Big Oh notation does not mention constant value

  - by user883561

  I am a programmer and have just started reading Algorithms. I am not completely convinced with the notations namely Bog Oh, Big Omega and Big Theta. The reason is by definition of Big Oh, it states that there should be a function g(x) such that it is always greater than or equal to f(x). Or f(x) <= c.n for all values of n n0. My doubt is the why dont we mention the constant value in the definition? For example. lets say a function 6n+4, we denote it as O(n). but its not true that the definition holds good for all constant value. this holds good only when c = 10 and n = 1. For lesser values of c than 6, the value of n0 increases. So why we do not mention the constant value as a part of the definition.

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• Big data: An evening in the life of an actual buyer

  - by Jean-Pierre Dijcks

  Here I am, and this is an actual story of one of my evenings, trying to spend money with a company and ultimately failing. I just gave up and bought a service from another vendor, not the incumbent. Here is that story and how I think big data could actually fix this (and potentially prevent some of this from happening). In the end this story should illustrate how big data can benefit me (get me what I want without causing grief) and the company I am trying to buy something from. Note: Lots of details left out, I have no intention of being the annoyed blogger moaning about a specific company. What did I want to get? We watch TV, we have internet and we do have a land line. The land line is from a different vendor then the TV and the internet. I have decided that this makes no sense and I was going to get a bundle (no need to infer who this is, I just picked the generic bundle word as this is what I want to get) of all three services as this seems to save me money. I also want to not talk to people, I just want to click on a website when I feel like it and get it all sorted. I do think that is reality. I want to just do my shopping at 9.30pm while watching silly reruns on TV. Problem 1 - Bad links So, I'm an existing customer of the company I want to buy my bundle from. I go to the website, I click on offers. Turns out they are offers for new customers. After grumbling about how good they are, I click on offers for existing customers. Bummer, it goes to offers for new customers, so I click again on the link for offers for existing customers. No cigar... it just does not work. Big data solutions: 1) Do not show an existing customer the offers for new customers unless they are the same => This is only partially doable without login, but if a customer logs in the application should always know that this is an existing customer. But in general, imagine I do this from my home going through the internet service of this vendor to their domain... an instant filter should move me into the "existing customer route". 2) Flag dead or incorrect links => I've clicked the link for "existing customer offers" at least 3 times in under 5 seconds... Identifying patterns like this is easy in Hadoop and can very quickly make a list of potentially incorrect links. No need for realtime fixing, just the fact that this link can be pro-actively fixed across my entire web domain is a good thing. Preventative maintenance! Problem 2 - Purchase cannot be completed Apart from the fact that the browsing pattern to actually get to what I want is poorly designed, my purchase never gets past a specific point. In other words, I put something into my shopping cart and when I want to move on the application either crashes (with me going to an error page) or hangs or goes into something like chat. So I try again, and again and again. I think I tried this entire path (while being logged in!!) at least 10 times over the course of 20 minutes. I also clicked on the feedback button and, frustrated as I was, tried to explain this did not work... Big Data Solutions: 1) This web site does shopping cart analysis. I got an email next day stating I have things in my shopping cart, just click here to complete my purchase. After the above experience, this just added insult to my pain... 2) What should have happened, is a Hadoop job going over all logged in customers that are on the buy flow. It should flag anyone who is trying (multiple attempts from the same user to do the same thing), analyze the shopping card, the clicks to identify what the customers wants, his feedback provided (note: always own your own website feedback, never just farm this out!!) and in a short turn around time (30 minutes to 2 hours or so) email me with a link to complete my purchase. Not with a link to my shopping cart 12 hours later, but a link to actually achieve what I wanted... Why should this company go through the big data effort? I do believe this is relatively easy to do using our Oracle Event Processing and Big Data Appliance solutions combined. It is almost so simple (to my mind) that it makes no sense that this is not in place? But, now I am ranting... Why is this interesting? It is because of $$$$. After trying really hard, I mean I did this all in the evening, and again in the morning before going to work. I kept on failing, But I really wanted this to work... so an email that said, sorry, we noticed you tried to get a bundle (the log knows what I wanted, where I failed, so easy to generate), here is the link to click and complete your purchase. And here is 2 movies on us as an apology would have kept me as a customer, and got the additional $$$$ per month for the next couple of years. It would also lead to upsell on my phone package etc. Instead, I went to a completely different company, bought service from them. Lost money for company A, negative sentiment for company A and me telling this story at the water cooler so I'm influencing more people to think negatively about company A. All in all, a loss of easy money, a ding in sentiment and image where a relatively simple solution exists and can be in place on the software I describe routinely in this blog... For those who are coming to Openworld and maybe see value in solving the above, or are thinking of how to solve this, come visit us in Moscone North - Oracle Red Lounge or in the Engineered Systems Showcase.

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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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• Big Data – Buzz Words: What is MapReduce – Day 7 of 21

  - by Pinal Dave

  In yesterday’s blog post we learned what is Hadoop. In this article we will take a quick look at one of the four most important buzz words which goes around Big Data – MapReduce. What is MapReduce? MapReduce was designed by Google as a programming model for processing large data sets with a parallel, distributed algorithm on a cluster. Though, MapReduce was originally Google proprietary technology, it has been quite a generalized term in the recent time. MapReduce comprises a Map() and Reduce() procedures. Procedure Map() performance filtering and sorting operation on data where as procedure Reduce() performs a summary operation of the data. This model is based on modified concepts of the map and reduce functions commonly available in functional programing. The library where procedure Map() and Reduce() belongs is written in many different languages. The most popular free implementation of MapReduce is Apache Hadoop which we will explore tomorrow. Advantages of MapReduce Procedures The MapReduce Framework usually contains distributed servers and it runs various tasks in parallel to each other. There are various components which manages the communications between various nodes of the data and provides the high availability and fault tolerance. Programs written in MapReduce functional styles are automatically parallelized and executed on commodity machines. The MapReduce Framework takes care of the details of partitioning the data and executing the processes on distributed server on run time. During this process if there is any disaster the framework provides high availability and other available modes take care of the responsibility of the failed node. As you can clearly see more this entire MapReduce Frameworks provides much more than just Map() and Reduce() procedures; it provides scalability and fault tolerance as well. A typical implementation of the MapReduce Framework processes many petabytes of data and thousands of the processing machines. How do MapReduce Framework Works? A typical MapReduce Framework contains petabytes of the data and thousands of the nodes. Here is the basic explanation of the MapReduce Procedures which uses this massive commodity of the servers. Map() Procedure There is always a master node in this infrastructure which takes an input. Right after taking input master node divides it into smaller sub-inputs or sub-problems. These sub-problems are distributed to worker nodes. A worker node later processes them and does necessary analysis. Once the worker node completes the process with this sub-problem it returns it back to master node. Reduce() Procedure All the worker nodes return the answer to the sub-problem assigned to them to master node. The master node collects the answer and once again aggregate that in the form of the answer to the original big problem which was assigned master node. The MapReduce Framework does the above Map () and Reduce () procedure in the parallel and independent to each other. All the Map() procedures can run parallel to each other and once each worker node had completed their task they can send it back to master code to compile it with a single answer. This particular procedure can be very effective when it is implemented on a very large amount of data (Big Data). The MapReduce Framework has five different steps: Preparing Map() Input Executing User Provided Map() Code Shuffle Map Output to Reduce Processor Executing User Provided Reduce Code Producing the Final Output Here is the Dataflow of MapReduce Framework: Input Reader Map Function Partition Function Compare Function Reduce Function Output Writer In a future blog post of this 31 day series we will explore various components of MapReduce in Detail. MapReduce in a Single Statement MapReduce is equivalent to SELECT and GROUP BY of a relational database for a very large database. Tomorrow In tomorrow’s blog post we will discuss Buzz Word – HDFS. 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 – Operational Databases Supporting Big Data – Key-Value Pair Databases and Document Databases – Day 13 of 21

  - by Pinal Dave

  In yesterday’s blog post we learned the importance of the Relational Database and NoSQL database in the Big Data Story. In this article we will understand the role of Key-Value Pair Databases and Document Databases Supporting Big Data Story. Now we will see a few of the examples of the operational databases. Relational Databases (Yesterday’s post) NoSQL Databases (Yesterday’s post) Key-Value Pair Databases (This post) Document Databases (This post) Columnar Databases (Tomorrow’s post) Graph Databases (Tomorrow’s post) Spatial Databases (Tomorrow’s post) Key Value Pair Databases Key Value Pair Databases are also known as KVP databases. A key is a field name and attribute, an identifier. The content of that field is its value, the data that is being identified and stored. They have a very simple implementation of NoSQL database concepts. They do not have schema hence they are very flexible as well as scalable. The disadvantages of Key Value Pair (KVP) database are that they do not follow ACID (Atomicity, Consistency, Isolation, Durability) properties. Additionally, it will require data architects to plan for data placement, replication as well as high availability. In KVP databases the data is stored as strings. Here is a simple example of how Key Value Database will look like: Key Value Name Pinal Dave Color Blue Twitter @pinaldave Name Nupur Dave Movie The Hero As the number of users grow in Key Value Pair databases it starts getting difficult to manage the entire database. As there is no specific schema or rules associated with the database, there are chances that database grows exponentially as well. It is very crucial to select the right Key Value Pair Database which offers an additional set of tools to manage the data and provides finer control over various business aspects of the same. Riak Rick is one of the most popular Key Value Database. It is known for its scalability and performance in high volume and velocity database. Additionally, it implements a mechanism for collection key and values which further helps to build manageable system. We will further discuss Riak in future blog posts. Key Value Databases are a good choice for social media, communities, caching layers for connecting other databases. In simpler words, whenever we required flexibility of the data storage keeping scalability in mind – KVP databases are good options to consider. Document Database There are two different kinds of document databases. 1) Full document Content (web pages, word docs etc) and 2) Storing Document Components for storage. The second types of the document database we are talking about over here. They use Javascript Object Notation (JSON) and Binary JSON for the structure of the documents. JSON is very easy to understand language and it is very easy to write for applications. There are two major structures of JSON used for Document Database – 1) Name Value Pairs and 2) Ordered List. MongoDB and CouchDB are two of the most popular Open Source NonRelational Document Database. MongoDB MongoDB databases are called collections. Each collection is build of documents and each document is composed of fields. MongoDB collections can be indexed for optimal performance. MongoDB ecosystem is highly available, supports query services as well as MapReduce. It is often used in high volume content management system. CouchDB CouchDB databases are composed of documents which consists fields and attachments (known as description). It supports ACID properties. The main attraction points of CouchDB are that it will continue to operate even though network connectivity is sketchy. Due to this nature CouchDB prefers local data storage. Document Database is a good choice of the database when users have to generate dynamic reports from elements which are changing very frequently. A good example of document usages is in real time analytics in social networking or content management system. Tomorrow In tomorrow’s blog post we will discuss about various other Operational Databases supporting Big Data. 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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• Welcome to the Oracle Retail International Blog

  - by sarah.taylor(at)oracle.com

  Welcome to the first post of the new Oracle Retail International Blog. Retail is an international business and today's successful retailers view themselves in the context of a global market. A niche fashion business in Tokyo will learn marketing strategies from the luxury brands of Milan, an independent grocer in Oslo will source the same global brands as a supermarket in Oklahoma, and every retailer in the world will measure their multi-channel operation against the international e-commerce giant Amazon.  Why? Because today's customer is a global customer with unparalleled expectations on choice, price and service. Today's consumers have access to more information on retail than ever before. Technology allows people to shop from their home, their office or from the phone in their pocket, wherever they are and at whatever time suits them. Customers are using the web to search for products and promotions. They are also using the web to develop their voice in commenting on products and services that have delighted or disappointed. In an information rich industry, this customer element creates a new world of data. The best retailers are developing eagle eyes for reading customer activity and turning it into profitable decisions. Ultimately, whether you choose to compete or shop on price, service, product innovation, excellent operations or all of the above - the international world of retail has become an inspiration for all - retailer and consumer alike.  Retail as an industry is growing and diversifying at a faster rate than ever before. Yet it is still the customer who picks the winners and the losers on the retail field. Economic circumstances transform the rules, but it is still the customer who dictates the game, the pace, the price, and the perception of the brand. Wise retailers never rest on their laurels. They are always shopping for ideas on how to improve and differentiate the offer at every touch point to meet the customer's needs better than anyone else and to gain each customer's loyalty at a time when loyalty can be cheap. With this blog, I hope that we might provide a hub for discussion around what unifies retail and how technology supports both the retailer and customer experience. Despite the competitive nature of this market, we hope that this will provide an opportunity to share experiences and lessons learnt with a view that knowledge can only help this industry to grow and develop. At Oracle we've been supporting retailers for many years. Many of us have worked within retail organisations all over the world, myself included. With this in mind, I don't feel it is too bold a statement to say that Oracle understands retail. We wouldn't be so heavily integrated in some of the biggest and most well-known names in retail if we didn't. With this blog, we intend to create a community of international retailers that can exchange ideas and experiences, debate collective challenges and drive a better understanding of this continually evolving industry. Events such as the World Retail Congress and NRF's Big Show bring enormous value to the retail industry providing platforms for discussion and learning but they happen once a year. We wanted to create a platform for discussion on a different level and that like retail, is always on. We hope not only to bring commitment to being not only the infrastructure that brings all of their systems together within a retail business, but an infrastructure that supports the industry internationally to grow and flourish through creating a platform for networking, discussion, creativity, vision and strategy. Please feel free to ask questions or comment using the comments functionality.  You might also want to visit our other Oracle Retail social media sites: Facebook - http://www.facebook.com/oracleretail YouTube - http://www.youtube.com/user/oracleretail Twitter - http://twitter.com/#!/oracleretailInsight-Driven Retailing Blog - http://blogs.oracle.com/retail/

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• Big Data – Buzz Words: What is NoSQL – Day 5 of 21

  - by Pinal Dave

  In yesterday’s blog post we explored the basic architecture of Big Data . In this article we will take a quick look at one of the four most important buzz words which goes around Big Data – NoSQL. What is NoSQL? NoSQL stands for Not Relational SQL or Not Only SQL. Lots of people think that NoSQL means there is No SQL, which is not true – they both sound same but the meaning is totally different. NoSQL does use SQL but it uses more than SQL to achieve its goal. As per Wikipedia’s NoSQL Database Definition – “A NoSQL database provides a mechanism for storage and retrieval of data that uses looser consistency models than traditional relational databases.“ Why use NoSQL? A traditional relation database usually deals with predictable structured data. Whereas as the world has moved forward with unstructured data we often see the limitations of the traditional relational database in dealing with them. For example, nowadays we have data in format of SMS, wave files, photos and video format. It is a bit difficult to manage them by using a traditional relational database. I often see people using BLOB filed to store such a data. BLOB can store the data but when we have to retrieve them or even process them the same BLOB is extremely slow in processing the unstructured data. A NoSQL database is the type of database that can handle unstructured, unorganized and unpredictable data that our business needs it. Along with the support to unstructured data, the other advantage of NoSQL Database is high performance and high availability. Eventual Consistency Additionally to note that NoSQL Database may not provided 100% ACID (Atomicity, Consistency, Isolation, Durability) compliance.  Though, NoSQL Database does not support ACID they provide eventual consistency. That means over the long period of time all updates can be expected to propagate eventually through the system and data will be consistent. Taxonomy Taxonomy is the practice of classification of things or concepts and the principles. The NoSQL taxonomy supports column store, document store, key-value stores, and graph databases. We will discuss the taxonomy in detail in later blog posts. Here are few of the examples of the each of the No SQL Category. Column: Hbase, Cassandra, Accumulo Document: MongoDB, Couchbase, Raven Key-value : Dynamo, Riak, Azure, Redis, Cache, GT.m Graph: Neo4J, Allegro, Virtuoso, Bigdata As of now there are over 150 NoSQL Database and you can read everything about them in this single link. Tomorrow In tomorrow’s blog post we will discuss Buzz Word – Hadoop. 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 – Buzz Words: What is Hadoop – Day 6 of 21

  - by Pinal Dave

  In yesterday’s blog post we learned what is NoSQL. In this article we will take a quick look at one of the four most important buzz words which goes around Big Data – Hadoop. What is Hadoop? Apache Hadoop is an open-source, free and Java based software framework offers a powerful distributed platform to store and manage Big Data. It is licensed under an Apache V2 license. It runs applications on large clusters of commodity hardware and it processes thousands of terabytes of data on thousands of the nodes. Hadoop is inspired from Google’s MapReduce and Google File System (GFS) papers. The major advantage of Hadoop framework is that it provides reliability and high availability. What are the core components of Hadoop? There are two major components of the Hadoop framework and both fo them does two of the important task for it. Hadoop MapReduce is the method to split a larger data problem into smaller chunk and distribute it to many different commodity servers. Each server have their own set of resources and they have processed them locally. Once the commodity server has processed the data they send it back collectively to main server. This is effectively a process where we process large data effectively and efficiently. (We will understand this in tomorrow’s blog post). Hadoop Distributed File System (HDFS) is a virtual file system. There is a big difference between any other file system and Hadoop. When we move a file on HDFS, it is automatically split into many small pieces. These small chunks of the file are replicated and stored on other servers (usually 3) for the fault tolerance or high availability. (We will understand this in the day after tomorrow’s blog post). Besides above two core components Hadoop project also contains following modules as well. Hadoop Common: Common utilities for the other Hadoop modules Hadoop Yarn: A framework for job scheduling and cluster resource management There are a few other projects (like Pig, Hive) related to above Hadoop as well which we will gradually explore in later blog posts. A Multi-node Hadoop Cluster Architecture Now let us quickly see the architecture of the a multi-node Hadoop cluster. A small Hadoop cluster includes a single master node and multiple worker or slave node. As discussed earlier, the entire cluster contains two layers. One of the layer of MapReduce Layer and another is of HDFC Layer. Each of these layer have its own relevant component. The master node consists of a JobTracker, TaskTracker, NameNode and DataNode. A slave or worker node consists of a DataNode and TaskTracker. It is also possible that slave node or worker node is only data or compute node. The matter of the fact that is the key feature of the Hadoop. In this introductory blog post we will stop here while describing the architecture of Hadoop. In a future blog post of this 31 day series we will explore various components of Hadoop Architecture in Detail. Why Use Hadoop? There are many advantages of using Hadoop. Let me quickly list them over here: Robust and Scalable – We can add new nodes as needed as well modify them. Affordable and Cost Effective – We do not need any special hardware for running Hadoop. We can just use commodity server. Adaptive and Flexible – Hadoop is built keeping in mind that it will handle structured and unstructured data. Highly Available and Fault Tolerant – When a node fails, the Hadoop framework automatically fails over to another node. Why Hadoop is named as Hadoop? In year 2005 Hadoop was created by Doug Cutting and Mike Cafarella while working at Yahoo. Doug Cutting named Hadoop after his son’s toy elephant. Tomorrow In tomorrow’s blog post we will discuss Buzz Word – MapReduce. 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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• New Big Data Appliance Security Features

  - by mgubar

  The Oracle Big Data Appliance (BDA) is an engineered system for big data processing.  It greatly simplifies the deployment of an optimized Hadoop Cluster – whether that cluster is used for batch or real-time processing.  The vast majority of BDA customers are integrating the appliance with their Oracle Databases and they have certain expectations – especially around security.  Oracle Database customers have benefited from a rich set of security features:  encryption, redaction, data masking, database firewall, label based access control – and much, much more.  They want similar capabilities with their Hadoop cluster.    Unfortunately, Hadoop wasn’t developed with security in mind.  By default, a Hadoop cluster is insecure – the antithesis of an Oracle Database.  Some critical security features have been implemented – but even those capabilities are arduous to setup and configure.  Oracle believes that a key element of an optimized appliance is that its data should be secure.  Therefore, by default the BDA delivers the “AAA of security”: authentication, authorization and auditing. Security Starts at Authentication A successful security strategy is predicated on strong authentication – for both users and software services.  Consider the default configuration for a newly installed Oracle Database; it’s been a long time since you had a legitimate chance at accessing the database using the credentials “system/manager” or “scott/tiger”.  The default Oracle Database policy is to lock accounts thereby restricting access; administrators must consciously grant access to users. Default Authentication in Hadoop By default, a Hadoop cluster fails the authentication test. For example, it is easy for a malicious user to masquerade as any other user on the system.  Consider the following scenario that illustrates how a user can access any data on a Hadoop cluster by masquerading as a more privileged user.  In our scenario, the Hadoop cluster contains sensitive salary information in the file /user/hrdata/salaries.txt.  When logged in as the hr user, you can see the following files.  Notice, we’re using the Hadoop command line utilities for accessing the data: $ hadoop fs -ls /user/hrdataFound 1 items-rw-r--r--   1 oracle supergroup         70 2013-10-31 10:38 /user/hrdata/salaries.txt$ hadoop fs -cat /user/hrdata/salaries.txtTom Brady,11000000Tom Hanks,5000000Bob Smith,250000Oprah,300000000 User DrEvil has access to the cluster – and can see that there is an interesting folder called “hrdata”.  $ hadoop fs -ls /user Found 1 items drwx------   - hr supergroup          0 2013-10-31 10:38 /user/hrdata However, DrEvil cannot view the contents of the folder due to lack of access privileges: $ hadoop fs -ls /user/hrdata ls: Permission denied: user=drevil, access=READ_EXECUTE, inode="/user/hrdata":oracle:supergroup:drwx------ Accessing this data will not be a problem for DrEvil. He knows that the hr user owns the data by looking at the folder’s ACLs. To overcome this challenge, he will simply masquerade as the hr user. On his local machine, he adds the hr user, assigns that user a password, and then accesses the data on the Hadoop cluster: $ sudo useradd hr $ sudo passwd $ su hr $ hadoop fs -cat /user/hrdata/salaries.txt Tom Brady,11000000 Tom Hanks,5000000 Bob Smith,250000 Oprah,300000000 Hadoop has not authenticated the user; it trusts that the identity that has been presented is indeed the hr user. Therefore, sensitive data has been easily compromised. Clearly, the default security policy is inappropriate and dangerous to many organizations storing critical data in HDFS. Big Data Appliance Provides Secure Authentication The BDA provides secure authentication to the Hadoop cluster by default – preventing the type of masquerading described above. It accomplishes this thru Kerberos integration. Figure 1: Kerberos Integration The Key Distribution Center (KDC) is a server that has two components: an authentication server and a ticket granting service. The authentication server validates the identity of the user and service. Once authenticated, a client must request a ticket from the ticket granting service – allowing it to access the BDA’s NameNode, JobTracker, etc. At installation, you simply point the BDA to an external KDC or automatically install a highly available KDC on the BDA itself. Kerberos will then provide strong authentication for not just the end user – but also for important Hadoop services running on the appliance. You can now guarantee that users are who they claim to be – and rogue services (like fake data nodes) are not added to the system. It is common for organizations to want to leverage existing LDAP servers for common user and group management. Kerberos integrates with LDAP servers – allowing the principals and encryption keys to be stored in the common repository. This simplifies the deployment and administration of the secure environment. Authorize Access to Sensitive Data Kerberos-based authentication ensures secure access to the system and the establishment of a trusted identity – a prerequisite for any authorization scheme. Once this identity is established, you need to authorize access to the data. HDFS will authorize access to files using ACLs with the authorization specification applied using classic Linux-style commands like chmod and chown (e.g. hadoop fs -chown oracle:oracle /user/hrdata changes the ownership of the /user/hrdata folder to oracle). Authorization is applied at the user or group level – utilizing group membership found in the Linux environment (i.e. /etc/group) or in the LDAP server. For SQL-based data stores – like Hive and Impala – finer grained access control is required. Access to databases, tables, columns, etc. must be controlled. And, you want to leverage roles to facilitate administration. Apache Sentry is a new project that delivers fine grained access control; both Cloudera and Oracle are the project’s founding members. Sentry satisfies the following three authorization requirements: Secure Authorization:  the ability to control access to data and/or privileges on data for authenticated users. Fine-Grained Authorization:  the ability to give users access to a subset of the data (e.g. column) in a database Role-Based Authorization:  the ability to create/apply template-based privileges based on functional roles. With Sentry, “all”, “select” or “insert” privileges are granted to an object. The descendants of that object automatically inherit that privilege. A collection of privileges across many objects may be aggregated into a role – and users/groups are then assigned that role. This leads to simplified administration of security across the system. Figure 2: Object Hierarchy – granting a privilege on the database object will be inherited by its tables and views. Sentry is currently used by both Hive and Impala – but it is a framework that other data sources can leverage when offering fine-grained authorization. For example, one can expect Sentry to deliver authorization capabilities to Cloudera Search in the near future. Audit Hadoop Cluster Activity Auditing is a critical component to a secure system and is oftentimes required for SOX, PCI and other regulations. The BDA integrates with Oracle Audit Vault and Database Firewall – tracking different types of activity taking place on the cluster: Figure 3: Monitored Hadoop services. At the lowest level, every operation that accesses data in HDFS is captured. The HDFS audit log identifies the user who accessed the file, the time that file was accessed, the type of access (read, write, delete, list, etc.) and whether or not that file access was successful. The other auditing features include: MapReduce:  correlate the MapReduce job that accessed the file Oozie:  describes who ran what as part of a workflow Hive:  captures changes were made to the Hive metadata The audit data is captured in the Audit Vault Server – which integrates audit activity from a variety of sources, adding databases (Oracle, DB2, SQL Server) and operating systems to activity from the BDA. Figure 4: Consolidated audit data across the enterprise.  Once the data is in the Audit Vault server, you can leverage a rich set of prebuilt and custom reports to monitor all the activity in the enterprise. In addition, alerts may be defined to trigger violations of audit policies. Conclusion Security cannot be considered an afterthought in big data deployments. Across most organizations, Hadoop is managing sensitive data that must be protected; it is not simply crunching publicly available information used for search applications. The BDA provides a strong security foundation – ensuring users are only allowed to view authorized data and that data access is audited in a consolidated framework.

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• Big 0 theta notation

  - by niggersak

  Can some pls help with the solution Use big-O notation to classify the traditional grade school algorithms for addition and multiplication. That is, if asked to add two numbers each having N digits, how many individual additions must be performed? If asked to multiply two N-digit numbers, how many individual multiplications are required? Suppose f is a function that returns the result of reversing the string of symbols given as its input, and g is a function that returns the concatenation of the two strings given as its input. If x is the string hrwa, what is returned by g(f(x),x)? Explain your answer - don't just provide the result!

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• Big Blue Bets Big on Red Hat's Virtualization Technology

  Virtually Speaking: In the next battleground for the cloud, IBM's choice of Red Hat's virtualization technology may be changing the landscape, but don't count VMware out just yet.

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• Big Data – Buzz Words: What is NewSQL – Day 10 of 21

  - by Pinal Dave

  In yesterday’s blog post we learned the importance of the relational database. In this article we will take a quick look at the what is NewSQL. What is NewSQL? NewSQL stands for new scalable and high performance SQL Database vendors. The products sold by NewSQL vendors are horizontally scalable. NewSQL is not kind of databases but it is about vendors who supports emerging data products with relational database properties (like ACID, Transaction etc.) along with high performance. Products from NewSQL vendors usually follow in memory data for speedy access as well are available immediate scalability. NewSQL term was coined by 451 groups analyst Matthew Aslett in this particular blog post. On the definition of NewSQL, Aslett writes: “NewSQL” is our shorthand for the various new scalable/high performance SQL database vendors. We have previously referred to these products as ‘ScalableSQL‘ to differentiate them from the incumbent relational database products. Since this implies horizontal scalability, which is not necessarily a feature of all the products, we adopted the term ‘NewSQL’ in the new report. And to clarify, like NoSQL, NewSQL is not to be taken too literally: the new thing about the NewSQL vendors is the vendor, not the SQL. In other words - NewSQL incorporates the concepts and principles of Structured Query Language (SQL) and NoSQL languages. It combines reliability of SQL with the speed and performance of NoSQL. Categories of NewSQL There are three major categories of the NewSQL New Architecture – In this framework each node owns a subset of the data and queries are split into smaller query to sent to nodes to process the data. E.g. NuoDB, Clustrix, VoltDB MySQL Engines – Highly Optimized storage engine for SQL with the interface of MySQ Lare the example of such category. E.g. InnoDB, Akiban Transparent Sharding – This system automatically split database across multiple nodes. E.g. Scalearc  Summary In simple words – NewSQL is kind of database following relational database principals and provides scalability like NoSQL. Tomorrow In tomorrow’s blog post we will discuss about the Role of Cloud Computing in Big Data. 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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• In Email, Image (img) Source (src) Tags are rewritten as relative links. How to fix?

  - by Noah Goodrich

  I'm working on sending out an html based email, and every time it sends the image src tags and some of the anchor href tags are modified to be relative url's. Update 2: This is happening between when the body of the email is generated and sent and when it arrives in my inbox. Update: I am using Postfix on a LAMPP server. In addition, I am using Zend_Mail to send the emails out. For example, I have a link: src="http://www.furnituretrainingcompany.com/images/email/highpoint_2009_04/header.jpg" And it gets rewritten as: src="../../../../images/email/highpoint_2009_04/header.jpg" What can cause this to occur and how is it corrected? Email headers: Return-Path: <[email protected]> X-Original-To: [email protected] Delivered-To: [email protected] Received: by mail.example.com (Postfix, from userid 0) id 6BF012252; Tue, 14 Apr 2009 12:15:20 -0600 (MDT) To: Gabriel <[email protected]> Subject: Free Map to Sales Success From: Somebody <[email protected]> Date: Tue, 14 Apr 2009 12:15:20 -0600 Content-Type: text/html; charset="utf-8" Content-Transfer-Encoding: multipart/related Content-Disposition: inline Message-Id: <[email protected]> Original content to be sent out: <table align="center" border="0" cellpadding="0" cellspacing="0" width="600"> <tbody> <tr> <td valign="top"> <a href="http://www.furnituretrainingcompany.com"> <img moz-do-not-send="true" alt="The Furniture Training Company - Know More. Sell More." src="http://www.furnituretrainingcompany.com/images/email/highpoint_2009_04/header.jpg" border="0" height="123" width="600"> </a> </td> </tr> </tbody> </table> <table align="center" border="0" cellpadding="0" cellspacing="0" width="600"> <tbody> <tr> <td valign="top"><img alt="Visit us at High Point to receive your free training poster" src="http://www.furnituretrainingcompany.com/images/email/highpoint_2009_04/hero.jpg" moz-do-not-send="true" height="150" width="600"><br> </td> </tr> </tbody> </table> <table align="center" border="0" cellpadding="0" cellspacing="0" width="600"> <tbody> <tr> <td bgcolor="#ffffff" valign="top"><img alt="" src="http://www.furnituretrainingcompany.com/images/email/highpoint_2009_04/spacer_content_left.jpg" moz-do-not-send="true" height="30" width="30"><br> </td> <td bgcolor="#ffffff" valign="top"><font originaltag="yes" style="font-size: 9px; font-family: Verdana,Arial,Helvetica,sans-serif;" color="#000000" face="Verdana, Arial, Helvetica, sans-serif" size="1"><big><big><big><big><small><big><b>See you at Market</b></big><br> </small></big></big></big></big></font> <font originaltag="yes" style="font-size: 9px; font-family: Verdana,Arial,Helvetica,sans-serif;" color="#000000" face="Verdana, Arial, Helvetica, sans-serif" size="1"><big><big><big><big><small><br> </small></big></big></big></big></font><small><font face="Helvetica, Arial, sans-serif">Visit our space to get your free Map to Sales Success poster! This unique 24 X 36 color poster is your guide to developing high volume salespeople with larger tickets. Find us in the new NHFA Retailer Resource Center located in the Plaza. <br> <br> Don&#8217;t miss Mark Lacy&#8217;s entertaining seminar "Help Wanted! My Sales Associates Can&#8217;t Sell Water to a Thirsty Camel." He&#8217;ll reveal powerful secrets for turning sales associates into furniture experts that will sell. See him Saturday, April 25th at 11:30 AM in the seminar room of the new NHFA Retail Resource Center in the Plaza. <br> <br> Stop by our space to learn how our ingenious internet-delivered training courses are easy to use, guaranteed to work, and cheaper than the daily donuts. Over 95% report increased sales. <br> <br> Plan to see us at High Point. </font></small> <font originaltag="yes" style="font-size: 9px; font-family: Verdana,Arial,Helvetica,sans-serif;" color="#000000" face="Verdana, Arial, Helvetica, sans-serif" size="1"><big><big><big><big><small><small><br> <br> <br> <br> </small></small></big></big></big></big></font><small><font originaltag="yes" style="font-size: 9px; font-family: Verdana,Arial,Helvetica,sans-serif;" color="#000000" face="Verdana, Arial, Helvetica, sans-serif" size="1"><big><big><big><small> </small></big></big></big></font></small> <a href="http://www.furnituretrainingcompany.com/map"><img alt="Find out more" src="http://www.furnituretrainingcompany.com/images/email/highpoint_2009_04/image_content_left.jpg" moz-do-not-send="true" border="0" height="67" width="326"></a><br> <br> </td> <td bgcolor="#ffffff" valign="top"> <img alt="" src="http://www.furnituretrainingcompany.com/images/email/highpoint_2009_04/spacer_content_middle.jpg" moz-do-not-send="true" height="28" width="28"><br> </td> <td bgcolor="#ffffff" valign="top"><img alt="Roadmap to Sales Success poster" src="http://www.furnituretrainingcompany.com/images/email/highpoint_2009_04/image_content_right.jpg" moz-do-not-send="true" height="267" width="186"><br> <font face="Helvetica, Arial, sans-serif"><small><font originaltag="yes" style="font-size: 9px; font-family: Verdana,Arial,Helvetica,sans-serif;" color="#000000" size="1"><big><big><big><small><b>Road Map to Sales Success<br> </b><br> </small></big></big></big></font>This beautiful poster is yours free for simply stopping by and visiting with us at High Point. <span class="moz-txt-slash">Our space is located inside the </span>new NHFA Retailer Resource Center in the Plaza Suites, 222 South Main St, 1st Floor. We will be at market from Sat April 25th until Thur April 30th. </small></font><br> </td> <td bgcolor="#ffffff" valign="top"><img alt="" src="http://www.furnituretrainingcompany.com/images/email/highpoint_2009_04/spacer_content_right.jpg" moz-do-not-send="true" height="30" width="30"><br> <br> </td> </tr> </tbody> </table> <table align="center" border="0" cellpadding="0" cellspacing="0" width="600"> <tbody> <tr> <td bgcolor="#ffffff" valign="top"><img alt="" src="http://www.furnituretrainingcompany.com/images/email/highpoint_2009_04/disclaimer_divider.jpg" moz-do-not-send="true" height="25" width="600"><br> </td> </tr> </tbody> </table> <table align="center" border="0" cellpadding="0" cellspacing="0" width="600"> <tbody> <tr> <td bgcolor="#ffffff" valign="top"><img alt="" src="http://www.furnituretrainingcompany.com/images/email/highpoint_2009_04/spacer_disclaimer_left.jpg" moz-do-not-send="true"></td> <td bgcolor="#ffffff" valign="top"><img alt="" src="http://www.furnituretrainingcompany.com/images/email/highpoint_2009_04/spacer_disclaimer_middle.jpg" moz-do-not-send="true"><br> <font originaltag="yes" style="font-size: 9px; font-family: Verdana,Arial,Helvetica,sans-serif;" color="#666666" face="Verdana, Arial, Helvetica, sans-serif" size="1"><big><big><big><big><small><small><small>If you are not attending the High Point market in April but would still like to receive a free Road Map to Sales Success poster visit us on the web at <u><a moz-do-not-send="true" class="moz-txt-link-abbreviated" href="http://www.furnituretrainingcompany.com">www.furnituretrainingcompany.com</a></u>, or to speak with a Furniture Training Company representative, call toll free (866) 755-5996. We do not offer free shipping outside of the U.S. and Canada. Retailers outside of the U.S. and Canada may call for more information. Limit one free Road Map to Sales Success per company. Other copies of the poster may be purchased on our web site.<br> <br> </small></small></small></big></big></big></big></font> <font color="#666666"><small><font originaltag="yes" style="font-size: 9px; font-family: Verdana,Arial,Helvetica,sans-serif;" face="Verdana, Arial, Helvetica, sans-serif" size="1"><big><big><big><small><small>We hope you found this message to be useful. However, if you'd rather not receive future emails of this sort from The Furniture Training Company, please <a moz-do-not-send="true" href="http://www.furnituretraining.com/contact">click here to unsubscribe</a>.<br> <br> </small></small></big></big></big></font></small><small><font originaltag="yes" style="font-size: 9px; font-family: Verdana,Arial,Helvetica,sans-serif;" face="Verdana, Arial, Helvetica, sans-serif" size="1"><big><big><big><small><small>&copy;Copyright 2009 The Furniture Training Company.<br> 1770 North Research Park Way, <br> North Logan, UT 84341. <br> All Rights Reserved.</small></small></big></big></big></font></small></font><br> </td> <td bgcolor="#ffffff" valign="top"><img alt="" src="http://www.furnituretrainingcompany.com/images/email/highpoint_2009_04/spacer_disclaimer_right.jpg" moz-do-not-send="true"></td> </tr> </tbody> </table> <table align="center" border="0" cellpadding="0" cellspacing="0" width="600"> <tbody> <tr> <td bgcolor="#ffffff" valign="top"><img alt="" src="http://www.furnituretrainingcompany.com/images/email/highpoint_2009_04/footer.jpg" moz-do-not-send="true"> </td> </tr> </tbody> </table> <br> <br> Content that gets sent: <table border=3D"0" cellspacing=3D"0" cellpadding=3D"0" width=3D"600" al= ign=3D"center">=0D=0A<tbody>=0D=0A<tr>=0D=0A<td valign=3D"top"><a href= =3D"http://www.furnituretrainingcompany.com"> <img src=3D"http://www.fur= nituretrainingcompany.com/images/email/highpoint_2009_04/header.jpg" bor= der=3D"0" alt=3D"The Furniture Training Company - Know More. Sell More."= width=3D"600" height=3D"123" /> </a></td>=0D=0A</tr>=0D=0A</tbody>=0D= =0A</table>=0D=0A<table border=3D"0" cellspacing=3D"0" cellpadding=3D"0"= width=3D"600" align=3D"center">=0D=0A<tbody>=0D=0A<tr>=0D=0A<td valign= =3D"top"><img src=3D"http://www.furnituretrainingcompany.com/images/emai= l/highpoint_2009_04/hero.jpg" alt=3D"Visit us at High Point to receive y= our free training poster" width=3D"600" height=3D"150" /><br /></td>=0D= =0A</tr>=0D=0A</tbody>=0D=0A</table>=0D=0A<table border=3D"0" cellspacin= g=3D"0" cellpadding=3D"0" width=3D"600" align=3D"center">=0D=0A<tbody>= =0D=0A<tr>=0D=0A<td valign=3D"top" bgcolor=3D"#ffffff"><img src=3D"http:= //www.furnituretrainingcompany.com/images/email/highpoint_2009_04/spacer= _content_left.jpg" alt=3D"" width=3D"30" height=3D"30" /><br /></td>=0D= =0A<td valign=3D"top" bgcolor=3D"#ffffff"><span style=3D"font-size: xx-s= mall; font-family: Verdana,Arial,Helvetica,sans-serif; color: #000000;">= <big><big><big><big><small><big><strong>See you at Market</strong></big>= <br /> </small></big></big></big></big></span> <span style=3D"font-size:= xx-small; font-family: Verdana,Arial,Helvetica,sans-serif; color: #0000= 00;"><big><big><big><big><small><br /> </small></big></big></big></big><= /span><small><span style=3D"font-family: Helvetica,Arial,sans-serif;">Vi= sit our space to get your free Map to Sales Success poster! This unique= 24 X 36 color poster is your guide to developing high volume salespeopl= e with larger tickets. Find us in the new NHFA Retailer Resource Center= located in the Plaza. <br /> <br /> Don&rsquo;t miss Mark Lacy&rsquo;s= entertaining seminar "Help Wanted! My Sales Associates Can&rsquo;t Sell= Water to a Thirsty Camel." He&rsquo;ll reveal powerful secrets for turn= ing sales associates into furniture experts that will sell. See him Satu= rday, April 25th at 11:30 AM in the seminar room of the new NHFA Retail= Resource Center in the Plaza. <br /> <br /> Stop by our space to learn= how our ingenious internet-delivered training courses are easy to use,= guaranteed to work, and cheaper than the daily donuts. Over 95% report= increased sales. <br /> <br /> Plan to see us at High Point. </span></s= mall> <span style=3D"font-size: xx-small; font-family: Verdana,Arial,Hel= vetica,sans-serif; color: #000000;"><big><big><big><big><small><small><b= r /> <br /> <br /> <br /> </small></small></big></big></big></big></span= ><small><span style=3D"font-size: xx-small; font-family: Verdana,Arial,H= elvetica,sans-serif; color: #000000;"><big><big><big><small> </small></b= ig></big></big></span></small> <a href=3D"http://www.furnituretrainingco= mpany.com/map"><img src=3D"http://www.furnituretrainingcompany.com/image= s/email/highpoint_2009_04/image_content_left.jpg" border=3D"0" alt=3D"Fi= nd out more" width=3D"326" height=3D"67" /></a><br /> <br /></td>=0D=0A<= td valign=3D"top" bgcolor=3D"#ffffff"><img src=3D"http://www.furnituretr= ainingcompany.com/images/email/highpoint_2009_04/spacer_content_middle.j= pg" alt=3D"" width=3D"28" height=3D"28" /><br /></td>=0D=0A<td valign=3D= "top" bgcolor=3D"#ffffff"><img src=3D"http://www.furnituretrainingcompan= y.com/images/email/highpoint_2009_04/image_content_right.jpg" alt=3D"Roa= dmap to Sales Success poster" width=3D"186" height=3D"267" /><br /> <spa= n style=3D"font-family: Helvetica,Arial,sans-serif;"><small><span style= =3D"font-size: xx-small; color: #000000;"><big><big><big><small><strong>= Road Map to Sales Success<br /> </strong><br /> </small></big></big></bi= g></span>This beautiful poster is yours free for simply stopping by and= visiting with us at High Point. <span class=3D"moz-txt-slash">Our space= is located inside the </span>new NHFA Retailer Resource Center in the P= laza Suites, 222 South Main St, 1st Floor. We will be at market from Sat= April 25th until Thur April 30th. </small></span><br /></td>=0D=0A<td v= align=3D"top" bgcolor=3D"#ffffff"><img src=3D"http://www.furnituretraini= ngcompany.com/images/email/highpoint_2009_04/spacer_content_right.jpg" a= lt=3D"" width=3D"30" height=3D"30" /><br /> <br /></td>=0D=0A</tr>=0D=0A= </tbody>=0D=0A</table>=0D=0A<table border=3D"0" cellspacing=3D"0" cellpa= dding=3D"0" width=3D"600" align=3D"center">=0D=0A<tbody>=0D=0A<tr>=0D=0A= <td valign=3D"top" bgcolor=3D"#ffffff"><img src=3D"http://www.furnituret= rainingcompany.com/images/email/highpoint_2009_04/disclaimer_divider.jpg= " alt=3D"" width=3D"600" height=3D"25" /><br /></td>=0D=0A</tr>=0D=0A</t= body>=0D=0A</table>=0D=0A<table border=3D"0" cellspacing=3D"0" cellpaddi= ng=3D"0" width=3D"600" align=3D"center">=0D=0A<tbody>=0D=0A<tr>=0D=0A<td= valign=3D"top" bgcolor=3D"#ffffff"><img src=3D"http://www.furnituretrai= ningcompany.com/images/email/highpoint_2009_04/spacer_disclaimer_left.jp= g" alt=3D"" /></td>=0D=0A<td valign=3D"top" bgcolor=3D"#ffffff"><img src= =3D"http://www.furnituretrainingcompany.com/images/email/highpoint_2009_= 04/spacer_disclaimer_middle.jpg" alt=3D"" /><br /> <span style=3D"font-s= ize: xx-small; font-family: Verdana,Arial,Helvetica,sans-serif; color: #= 666666;"><big><big><big><big><small><small><small>If you are not attendi= ng the High Point market in April but would still like to receive a free= Road Map to Sales Success poster visit us on the web at <span style=3D"= text-decoration: underline;"><a class=3D"moz-txt-link-abbreviated" href= =3D"http://www.furnituretrainingcompany.com">www.furnituretrainingcompan= y.com</a></span>, or to speak with a Furniture Training Company represen= tative, call toll free (866) 755-5996. We do not offer free shipping out= side of the U.S. and Canada. Retailers outside of the U.S. and Canada ma= y call for more information. Limit one free Road Map to Sales Success pe= r company. Other copies of the poster may be purchased on our web site.<= br /> <br /> </small></small></small></big></big></big></big></span> <sp= an style=3D"color: #666666;"><small><span style=3D"font-size: xx-small;= font-family: Verdana,Arial,Helvetica,sans-serif;"><big><big><big><small= ><small>We hope you found this message to be useful. 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• Big Data – ClustrixDB – Extreme Scale SQL Database with Real-time Analytics, Releases Software Download – NewSQL

  - by Pinal Dave

  There are so many things to learn and there is so little time we all have. As we have little time we need to be selective to learn whatever we learn. I believe I know quite a lot of things in SQL but I still do not know what is around SQL. I have started to learn about NewSQL recently. If you wonder what is NewSQL I encourage all of you to read my blog post about NewSQL over here Big Data – Buzz Words: What is NewSQL – Day 10 of 21. NewSQL databases are quickly becoming popular – providing the scale of NoSQL with the SQL features and transactions. As a part of learning NewSQL database, I have recently started to learn about ClustrixDB. ClustrixDB has been the most mature NewSQL database used by some of the largest internet sites in the world for over 3 years, with extensive SQL support. In addition to scale, it provides fast real-time analytics by bringing massively parallel processing (MPP), available only in warehousing databases, to the transactional database. The reason I am more intrigued about learning ClustrixDB is their recent announcement on Oct 31. ClustrixDB was only available as an appliance, but now with their software release on Oct 31, everyone can use it. It is now available as forever free for up to 12 cores with community support, and there is a 45 day trial for unlimited cluster sizes. With the forever free world, I am indeed interested in ClustrixDB now. I know that few of the leading eCommerce sites in the world uses them for their transactional database. Here are few of the details I have quickly noted for ClustrixDB. ClustrixDB allows user to: Scale by simply adding nodes to the cluster with a single command Run billions of transactions a day Run fast real-time analytics Achieve high-availability with recovery from node failure Manages itself Easily migrate from MySQL as it is nearly plug-and-play compatible, use MySQL drivers, tools and replication. While I was going through the documentation I realized that ClustrixDB also has extensive support for SQL features including complex queries involving joins on a dozen or more tables, aggregates, sorts, sub-queries. It also supports stored procedures, triggers, foreign keys, partitioned and temporary tables, and fully online schema changes. It is indeed a very matured product and SQL solution. Indeed Clusterix sound very promising solution, I decided to dig a bit deeper to understand who are current customers of the Clustrix as they exist in the industry for quite a few years. Their client list is indeed very interesting and here is my quick research about them. Twoo.com – Europe’s largest social discovery (dating) site runs 4.4 Billion Transactions a day with table sizes over a Terabyte, on a 168 core cluster. EngageBDR – Top 3 in the online advertising category uses ClustrixDB to serve 6.9 billion ads a day through real-time bidding platform. Their reports went from 4 hours to 15 seconds. NoMoreRack – Top 2 fastest growing e-commerce company in US used ClustrixDB for high availability and fast growth through Amazon cloud. MakeMyTrip – India’s leading travel site runs on ClustrixDB with two clusters running as multi-master in Chennai and Bangalore. Many enterprises such as AOL, CSC, Rakuten, Symantec use ClustrixDB when their applications need scale. I must accept that I am impressed with the information I have learned so far and now is the time to do some hand’s on experience with their product. I want to learn this technology so in future when it is about NewSQL, I know what I am talking about. Read more why Clustrix explains why you ClustrixDB might be the right database for you. Download ClustrixDB with me today and install it on your machine so in future when we discuss the technical aspects of it, we all are on the same page. The software can be downloaded here. Reference : Pinal Dave (http://blog.SQLAuthority.com)Filed under: Big Data, MySQL, PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL Tagged: Clustrix

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• Live from ODTUG - Big Data and SQL session #2

  - by Jean-Pierre Dijcks

  Sitting in Dominic Delmolino's session at ODTUG (KScope 12). If the session count at conferences is any indication then we will see more and more people start to deploy MapReduce in the database. And yes, that would be with SQL and PL/SQL first and foremost. Both Dominic and our own Bryn Llewellyn are doing MapReduce in the database presentations.  Since I have seen both, I would advice people to first look through Dominic's session to get a good grasp on what mappers do and what reducers do, then dive into Bryn's for a bunch of PL/SQL example. The thing I like about Dominic's is the last slide (a recursive WITH statement) to do this in SQL... Now I am hoping that next year we will see tools vendors show off how they work with Hadoop and MapReduce (at least talking about the concepts!!).

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• Start your journey into Big Data with the Oracle Academy today!

  - by KLaker

   Big Data has the power to change the way we work, live, and think. The datafication of everything will create unprecedented demand for data scientists, software developers and engineers who can derive value from unstructured data to transform the world. The Oracle Academy Big Data Resource Guide is a collection of articles, videos, and other resources organized to help you gain a deeper understanding of the exciting field of Big Data. To start your journey visit the Oracle Academy website here: https://academy.oracle.com/oa-web-big-data.html. This landing pad will guide through the whole area of big data using the following structure: What is “Big Data” Engineered Systems Integration Database and Data Analytics Advanced Information Supplemental Information This is great resource packed with must-see videos and must-read whitepapers and blog posts by industry leaders.  Enjoy Technorati Tags: Big Data, Data Warehousing, Oracle, Training

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• Why Oracle Data Integrator for Big Data?

  - by Mala Narasimharajan

  Big Data is everywhere these days - but what exactly is it? It’s data that comes from a multitude of sources – not only structured data, but unstructured data as well.  The sheer volume of data is mindboggling – here are a few examples of big data: climate information collected from sensors, social media information, digital pictures, log files, online video files, medical records or online transaction records.  These are just a few examples of what constitutes big data.   Embedded in big data is tremendous value and being able to manipulate, load, transform and analyze big data is key to enhancing productivity and competitiveness.  The value of big data lies in its propensity for greater in-depth analysis and data segmentation -- in turn giving companies detailed information on product performance, customer preferences and inventory.  Furthermore, by being able to store and create more data in digital form, “big data can unlock significant value by making information transparent and usable at much higher frequency." (McKinsey Global Institute, May 2011) Oracle's flagship product for bulk data movement and transformation, Oracle Data Integrator, is a critical component of Oracle’s Big Data strategy. ODI provides automation, bulk loading, and validation and transformation capabilities for Big Data while minimizing the complexities of using Hadoop.  Specifically, the advantages of ODI in a Big Data scenario are due to pre-built Knowledge Modules that drive processing in Hadoop. This leverages the graphical UI to load and unload data from Hadoop, perform data validations and create mapping expressions for transformations.  The Knowledge Modules provide a key jump-start and eliminate a significant amount of Hadoop development.  Using Oracle Data Integrator together with Oracle Big Data Connectors, you can simplify the complexities of mapping, accessing, and loading big data (via NoSQL or HDFS) but also correlating your enterprise data – this correlation may require integrating across heterogeneous and standards-based environments, connecting to Oracle Exadata, or sourcing via a big data platform such as Oracle Big Data Appliance. To learn more about Oracle Data Integration and Big Data, download our resource kit to see the latest in whitepapers, webinars, downloads, and more… or go to our website on www.oracle.com/bigdata

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• What is the Big-O time complexity of this algorithm

  - by grebwerd

  I was wondering what the run time of this small program would be? #include <stdio.h> int main(int argc, char* argv[]) { int i; int j; int inputSize; int sum = 0; if(argc == 1) inputSize = 16; else inputSize = atoi(argv[i]); for(i = 1; i <= inputSize; i++){ for(j = i; j < inputSize; j *=2 ){ printf("The value of sum is %d\n",++sum); } } } n S floor(log n - log (n-i)) = ? i =1 and that each summation would be the floor value between log(n) - log(n-i). Would the run time be n log n?

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