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  • Setting up WHM nameservers

    - by Miskone
    I am new to server administration and I've just got a dedicated root server from Hetzner. First I set up in Hetzner's robot DNS entries Registered nameservers: ns1.raybear.com 88.198.32.57 ns2.raybear.com 88.198.32.57 Under DNS entires I have buzz-buzz.me pointing to 88.198.32.57 // My server IP address and on my WHM I have DNS zone for buzz-buzz.me ; cPanel first:11.42.1.17 (update_time):1402062640 Cpanel::ZoneFile::VERSION:1.3 hostname:hosting.raybear.com latest:11.42.1.17 ; Zone file for buzz-buzz.me $TTL 14400 buzz-buzz.me. 86400 IN SOA ns1.raybear.com. miskone.gmail.com. ( 2014060605 ;Serial Number 86400 ;refresh 7200 ;retry 3600000 ;expire 86400 ) buzz-buzz.me. 86400 IN NS ns1.raybear.com. buzz-buzz.me. 86400 IN NS ns2.raybear.com. buzz-buzz.me. 14400 IN A 88.198.32.57 localhost 14400 IN A 127.0.0.1 buzz-buzz.me. 14400 IN MX 0 buzz-buzz.me. mail 14400 IN CNAME buzz-buzz.me. www 14400 IN CNAME buzz-buzz.me. ftp 14400 IN CNAME buzz-buzz.me. agent 14400 IN A 88.198.32.57 src 14400 IN A 88.198.32.57 platform 14400 IN A 88.198.32.57 But still I have some problems accesing buzz-buzz.me, agent.buzz-buzz.me and platform.buzz-buzz.me Also I have problem getting mails on Google account, I can send but not receive emails. How to solve this. As I said I am completly new here and I need urgent help.:(

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  • Google I/O 2010 - Google Buzz, location, and social gaming

    Google I/O 2010 - Google Buzz, location, and social gaming Google I/O 2010 - Surf the stream: Google Buzz, location, and social gaming Social Web 201 Bob Aman, Timothy Jordan Google Buzz has a feature-rich API that allows you to do all kinds of interesting things with conversations and location. In this session we'll build a Buzz-tastic mobile game using App Engine, HTML5, and the Buzz API for social awesomeness. For all I/O 2010 sessions, please go to code.google.com From: GoogleDevelopers Views: 2 0 ratings Time: 31:18 More in Science & Technology

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  • SEO With Google Buzz

    The latest trend in the social-network market is Google Buzz. Still not many people are aware what Buzz is, and unknown about the features and advantages of Google Buzz!! So let's have a close look at what Google Buzz is and how it can help us.!!

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  • Error 404 when trying to fetch the Google Buzz @consumption feed

    - by Vladimiroff
    I'm writing an Qt application and I go through authorization process and everything. I'm even able to fetch the @self feed, but for some reason I get error 404 when trying to do the same thing with @consumption: "Download of https://www.googleapis.com/buzz/v1/activities/v.kiril/@consumption failed: Error downloading https://www.googleapis.com/buzz/v1/activities/v.kiril/@consumption - server replied: Not Found" I've got this url from the Google Buzz API. And I've tried to use my personal google profile ID and this @me namespace. Without success

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  • Google remodèle les paramètres de confidentialité de Buzz, suite aux plaintes essuyées

    Mise à jour du 06.04.2010 par Katleen Google remodèle les paramètres de confidentialité de Buzz, suite aux plaintes essuyées Suite au mécontentement de certains de ses utilisateurs, allant jusqu'à une plainte aux Etats-Unis, Google Buzz va se doter d'une nouvelle page de réglage. Depuis hier, les usagers du service verront apparaître un écran de validation de leurs réglages utilisateurs, et ils devront confirmer ou modifier les paramètres concernant les informations personnelles qu'ils partagent via Buzz. Un peu comme les paramètres de confidentialité de Facebook, ce panneau de contrôle permettra de définir qui peut suivre le compte, la diffusion des données, les liens avec Pica...

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  • Getting that buzz back?

    - by kyndigs
    I have been working in development for a good company for a while now since graduating from university, I really enjoy it and have some great fun in the office and enjoy everything I am doing. But recently I have lost that old buzz, I cant bring myself to code outside of work, a while back I could be outside of work and come up with a nice idea and go away and develop that idea, but I feel that buzz has gone, I still love developing and technology but I just cant find the energy to do it when I am not at work. Has anyone else gone through a phase like this? What did you do to combat it and get that energy and buzz back? Maybe I need a new tehcnology, or a holiday!

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  • How to mail to a particular email id using my gmail credentials from desktop application

    - by GG
    Hello all, I am just developing an a desktop application for Twitter, Buzz and facebook. Google Buzz has not released their whole api to post buzz, but today I came to know that to create a new Buzz just mail to [email protected] with subject as Buzz content you want to create. Now I got stuck that how to mail to [email protected] from my gmail id using destop application which I am developing. Is there any kind of google webservice or api is available to do the task? Thanks, GG

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  • Google Buzz essuie les critiques de 10 pays, qui ont co-signé une lettre officielle

    Mise à jour du 22.04.2010 par Katleen Google Buzz essuie les critiques de 10 pays, qui ont co-signé une lettre officielle La Commission nationale de l'informatique et des libertés (CNIL) a suivi de très près le lancement de Google Buzz. Et, très vite, des mécontentements sont arrivés. C'est pourquoi, à peine deux mois après l'arrivée de ce nouveau service communautaire, la CNIL à envoyé un courrier plutôt salé à Eric Schmidt, CEO de Google. Mais la missive se veut encore plus générale, elle s'adresse à "toutes les entreprises en ligne" et leur demande de respecter "le droit à la vie privée des citoyens du monde". Co-signé par dix autorités de ...

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  • Apple trouve "normal" que la coque de l'iPhone 5 soit facilement rayée, le buzz négatif continue

    Apple trouve "normal" que la coque de l'iPhone 5 soit facilement rayée Le buzz négatif continue Plus d'une semaine après sa sortie officielle, l'iPhone 5 continue de s'offrir un méga-buzz négatif. Les premiers propriétaires de cet appareil classé haut de gamme s'interrogent au sujet de ses contours qui se rayent facilement en laissant transparaître la couleur naturelle de l'aluminium. Selon le site spécialisé 9ToMac, un acheteur désappointé aurait reçu une réponse surprenante à sa requête adressée à Phil Schiller, vice-président en charge du marketing chez Apple. Le client se plaint dans son email d'éraflures et de traces sur la bande extérieure de son nouvel iPhone et se deman...

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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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  • Google Buzz buttons

    We've seen lots of people using Google Buzz to share interesting links from around the web. To do so, you had to copy and paste the link from...

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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 – 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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  • Addicted to the MIX Buzz

    - by Dave Campbell
    Well it's the Friday before MIX10, and I'm officially of no use to anybody. I'll be driving up to 'Vegas Sunday ... hopefully rolling in mid-late afternoon, checking in at my $31.50/night (including WiFi) Motel, and getting registered then hanging out around registration to see who is there. First organized thing to do is 9PM, so I'm open to suggestions Sunday evening... maybe we can get a gang together for dinner ?? Monday is the Keynote ... I'm addicted to the buzz in the ballroom the first day, hope to be close to the front, trying to live blog. Then straight to Ballroom A and stake out the spot I'll be in for all 3 days, and you all know why :) I've tagged 40 sessions that I 'want' to see, and there's only 12 slots... damn... if I could, I'd try the Multiplicity thing, but I'm afraid I'd get the idiot first try -- or maybe got that one already :) ... but at least I tagged them to make it easy to find after the videos are up. Stuff going on Sunday, Monday, and Tuesday night. I'm staying over for an event on Thursday, and driving back on Friday. I'm not sure how much blogging I'll be doing, but I'll try to hit some 'Cream high spots. I'm sure everyone #NotAtMIX is going to be tuned into the sessions online. I'll be wearing TShirts with WynApse.com and SilverlightCream.com printed on the back... so if you see some old curmudgeon with such a shirt, IT'S ME! I look forward to seeing all the people I only see once or twice a year, and meeting ones I haven't met yet What a week... Bring It On and Stay in the 'Light! Technorati Tags: Silverlight    Silverlight 4    MIX10

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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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  • How to get a OAuth token for Google Buzz using username and password without showing Googles login p

    - by Witek
    To read Google Buzz activities, an authorization token is required. A web application would redirect to Googles login page, where the user logs in and a token is returned back to the web application. But I have a local Java application without a UI (like a script). This application knows username and password. How to get an authorization token, using this username and password, without presenting the Google login page?

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