NoSQL

NoSQL cloud technology as an option? {3}

As relational databases such as SQL and MySQL have been the standard model for storing, retrieving, and managing data throughout the IT industry for the past decades, relational databases finds itself losing its significance primarily due to fixed schema requirements and the inability to scale (Arora & Aggarwal, 2013). Three prominent trends occurring in the computer industry are Big Users, Big Data, and Cloud Computing. Relational databases finds itself struggling to keep up with these current trends. This is where NoSQL cloud technology can provide a solution to these problems.

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Mongo DB (No SQL database for web) {6}

Today’s highly social and interactive web has created a market for a database management system with the ability to offer fast real time access over the Internet while managing massive data sets that are growing by the minute in volume and complexity. MongoDB fills this need. As I will explain later in this blog, MongoDB is not the perfect solution for every project, but for certain tasks that are within it’s niche, it is the best solution.

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SQL vs NoSQL in the Cloud {2}

The article I choose this week is about the difference between SQL and NoSQL. According to the author, the different access patterns provided by NoSQL and SQL result in very different scalability and performance. He pointed out that NoSQL elements allow data access only in a narrow predefined access pattern. And by giving us an example, the author stated that DHT (Distributed Hash Table) is accessible via hash table API; given the exact key, the value is returned. The access pattern for other NoSQL data services is similarly narrow and well-defined, and as a result scalability and performance structure are predictable and reliable. However, In other words, in SQL, the access pattern is not known in advance, the data model does not enforce a specific way to work with the data. It is built with an emphasis on data integrity, simplicity, data normalization and abstraction, which are all extremely important for large complex applications.

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Google Chooses SQL {5}

The article I chose this week is “Google App Engine Goes Old School With SQL Database” by Caleb Garling. Article starts off by saying that Google was one of the leaders of the NoSQL movement. However, by choosing a typical SQL server they have reaffirmed SQL’s place in technology. They implemented this on top of their App Engine as well as their “BigTable” NoSQL database. The reason behind this was that developers did not want to have to develop on BigTable due to difficulties with translating their existing relational data models over to BigTable compliant models. The article goes on to talk briefly about the difference between NoSQL and SQL databases. Starting that NoSQL databases “are meant to “scale” across vast numbers of servers so they can accommodate the mountains of data facing companies in the internet age (Garling, 2011).” Whereas SQL databases “order data into neat rows and columns – give you more ways to slice and dice your data (Garling, 2011).”  The article finishes by saying that while Google moved back towards traditional SQL, Oracle moved towards the newer NoSQL system.

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Old Fashion SQL {4}

The article I chose to blog about this week was “Google App Engine Goes Old School With SQL Database” written by Caleb Garling of Wired.com. This article speaks about the addition of a SQL database to their Google App Engine. The Google App Engine is a means for Google customers to build and host applications on top of Google’s online infrastructure. Prior to this, Google was in the forefront of the NoSQL movement, but with this announcement, it shows that good old fashion SQL is alive and well. Google provides this SQL database so people can power their App Engine applications with a relational database, which will be more familiar to the masses, in a “fully managed cloud environment”(Garling, 2011).  Google is totally headed in the opposite direction of competitor Oracle which announced their Big Data Appliance (NoSQL database).

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You Can Still Use RDBMS over NoSQL {1}

NoSQL is definitely the buzz in the database world. With such open source packages such as MongoDB and FoundationDB, it makes us NoSQL as accessible as it can get. There are still benefits to using relational databases, such as the ability to use normalization, shared data, and maturity. Some instances in which using a relational database over NoSQL are advantageous include when you’re building smaller databases that are still going to change over time, when there is so much duplicate data that you have to normalize, and when there is no cost advantage to moving away from already proven technology.

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When Will NoSQL be the Giant in the Database World? {4}

In the article I read about, there seemed to be a generally negative feeling about Oracle. Oracle has been around for years now and is used by many businesses for their database management. The article spoke about how time consuming and expensive Oracle is for most companies. It was described as “software for the upper one percent”.  When implementing an Oracle system, you have to pay for several fees and line items that seem useless. Not only is it expensive but has fundamental flaws for such a mature product. This was explained in a separate article. So the Oracle DBMS is a widely used product while still having some problems and while being very very expensive. NoSQL has been a competing software that was developed in 2009. This system is cheaper and has several benefits. But this article was talking about how even though there is a better alternative to Oracle, NoSQL will not be replacing it very quickly.

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The Big 5 of Big Data {3}

The article I chose to blog about this week is,” Big Data Right Now: Five Trendy Open Source Technologies” by Mr. Tim Gasper of TechCrunch.com. The article starts of by saying Big Data is on everyones mind, and companies “will have spent $4.3 billion on Big Data technologies by the end of 2012″ (Gasper, 2012). However,  author believes this is just the tip of the iceberg and states these initial investments will cause a chain reaction for upwards of $34 Billion in spendings through 2013. The field is so expansive, and there are so many players in Big Data (with more to come) the author provided a picture to show just how big this field is.

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Why relational databases make sense for big data? {2}

In the article  ” Why relational databases make sense for big data”, Dave Rosenberg talked about the “big data” trend that more and more organizations are now (or soon will be) dealing with managing and extracting information from databases that are growing into the multi-petabyte range. This trend caused developers are forced to seek new “NoSQL” approaches and instead process data in a distributed manner. These so called “NoSQL” such as Cassandra and MongoDB databases, are built to scale easily and handle massive amounts of data in a highly fluid manner. Dave stated himself as a NoSQL supporter but he also pointed out that there is often a point where all of this data needs to be aggregated and parsed for different reasons, in a more traditional SQL data model.

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Placing Money On Big Data {3}

The article I chose to blog about this week is titled “Why VCs Will Continue to Invest In Big Data Startups For Many Years To Come” by Alex Williams. Mr. Williams first off mentions the great amount of money raised by Splice Machine, MongoHQ, and Bloomreach. All three companies have raised over $4million recently, which Bloomreach leading the way by raising “$25 million in funds for its big data applications” (Williams, 2012). These companies show investors are very interested and believe in the future of big data and the evolution of data infrastructures.  Big data has made so much noise in recent times that Gartner Researched recently published an article stating big data will bring about $232 Billion of spending in IT through 2016. These leaders of big data have much to do with it, due to their easily scalable database design based on NoSQL. The industry as a whole is seeing a shift as the author states, “business intelligence applications, for instance, have started to transition from an OLAP data source in a relational database to a new type of service that connects different data sources from social networks, third-party apps and other sources” (Williams, 2012).

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