![]() ![]() Free Computer Science ebooks,Free Computer Science ebooks download,computer science online, microsoft windows programming, Cisco certification books.Resource & Design Center for Development with Intel. Access the documentation, software, and tools you need to design with Intel® processors, chipsets, solid state devices, and more. Big Data Testing – Complete beginner’s guide for Software Testers. In this comprehensive beginners guide to big data testing, we cover concepts related to testing of big data applications. This tutorial is ideal for software testers and anyone else who wants to understand big data testing but is completely new to the field. With the exponential growth in the number of big data applications in the world, the demand and opportunity for testers who have knowledge of testing big data applications has increased. The SAP Community is the quickest way for users to solve problems, learn more about SAP solutions, and invent new ways to get things done. According to IDC Big data market is projected to be a $5. Table Of Contents. What Is Big Data? Examples And Usage Of Big Data. Data formats in Big Data. Why traditional relational databases cannot be used to support big data. Test Strategy And Steps For Testing Big Data Applications. Database Testing Of Big Data Applications. Performance Testing of Big Data Applications. Big Data / Hadoop Performance Testing. Performance Testing Approach.
Functional Testing of Big Data Applications. Roles and Responsibilities Of A Tester In Big Data Applications. Advantages Of Using Big Data / Hadoop. Disadvantages Of Using Big Data / Hadoop. ![]() Hadoop Architecture. Big Data Tools / Common Terminologies. Big Data Automation Testing Tools. Big Data Testing Services. Software Testers new to the field often ask questions like – what is big data?, how to test big data applications? What are the steps or processes to test big data applications? We answer all this and more in our big data testing tutorial below. What Is Big Data? Big Data refers to large volume of data, that cannot be processed using traditional databases. When we have reasonable amounts of data we typically use traditional relational databases like Oracle, My. SQL, SQL Server to store and work with the data. However when we have large volume of data then traditional databases will not be able to handle the data. Traditional databases are good at working with structured data that can be stored in rows and columns. However, if we have unstructured data that does not follow a structure then using a relational database is not be the right choice. In case of Big data we have large amounts of data which can be in any format like images, flat files, audio etc whose structure and format may not be the same for every record. The size of the big data, the volume of data that gets created from time to time, may be significantly larger compared to traditional databases. This will be difficult to handle with traditional databases. Big data is characterized by the 3 V’s – Volume, Velocity and Variety. Volume : The volume of data collected is organizations is large and comes from different sources like sensors, meter readings, business transactions etc. Velocity : Data is created at high speed and has to be handled and processed quickly. Instruments like IOT devices, RFID tags, Smart meters and others lead to automated generation of data at unprecedented speed. Variety : Data comes in all formats. It can be in audio, video, numeric, text, email, satellite images, atmospheric sensors etc. Examples And Usage Of Big Data. Storing data without analyzing it to gain meaningful insights from the data would be a waste of resources. Before we look at testing of big data it would be useful to understand how it is being used in the real world. E- commerce. Amazon, Flipkart and other e- commerce sites have millions of visitors each day with hundreds of thousands of products. Amazon uses big data to store information regarding products, customer and purchases. Apart from this data is also gathered around the product searches, views, products being added to cart, cart abandonment, products that are bought together etc. All of this data is stored and processed in order to suggest products that the customer is most likely to buy. If you open a product page, you can see this in action under the “Frequently bought together”, “Customers who bought this item also bought” and “Customers who viewed this item also viewed” sections. This information is also used to recommend deals / discounts and rank the products in the search results. All of this data has to be processed very quickly which is not feasible with traditional databases. Social Media. Social media sites generate huge amounts of data in terms of pictures, videos, likes, posts, comments etc. Not only is data stored in big data platforms, they are also processed and analyzed to offer recommendations on content that you might like. Twitter. There are 3. Twitter. A total of 1. Twitter. Each day 5. Over 6. 18,7. 25 tweets were sent in a minute during FIFA World Cup final in 2. Facebook. There are 1. Facebook. Over 1. Facebook everyday. Everyday videos generate 8 billion views. Instagram. 70. 0 million people use Instagram every month. Instagram. Users like 4. Not only is data stored in big data platforms, they are also processed and analyzed to offer recommendations of things you may be interested in. For example, if you search for a washing machine on Amazon and go to Facebook, Facebook will show you ads for the same. This is a big data use case because, there millions of websites that advertise on Facebook and there are billions of users. Storing and processing this information to display the right advertisement to the right users cannot be accomplished by traditional databases in the same amount of time. Targeting the right customer with the right ad is important because a person searching for washing machines is more likely to click on an ad of a washing machine than an ad for a Television. Healthcare. FDA and CDC created the Genome. Trakr program which processes 1. This helped FDA in identifying one nut- butter production centre as the source of a multi state Salmonella outbreak. FDA halted the production at the factory which stopped the outbreak. Aetna, an insurance provider processed 6. Data formats in Big Data. One common question that people ask is – why we cannot use traditional relational database for big data. To answer this, first we need to understand the different data formats in big data. Data formats in big data can be classified into three categories. They are: Structured Data. Semi Structured Data. Unstructured data. Structured Data. This refers to data that is highly organized. It can be easily stored in any relational database. This also means that it can be easily retrieved / searched using simple queries. Examples of Structured Data. The image below depicts the data model for an application. Here you can see the tables and associated columns in the tables. In this example the user table t_user stores details like the users name, password, email, phone numbers etc. The length of the fields and their data types are predefined and have a fixed structure. Semi- Structured Data. Semi- structured data is not rigidly organized in a format that can allow it to be easily accessed and searched. Semi- structured data is not usually stored in a relational database. However they can be stored in a relational database after some processing and converted to structured format. Semi- structured data lies between structured and unstructured data. They can contain tags and other metadata to implement a hierarchy and order. In semi- structured data, the same type of entities may have different attributes in different order. Examples of Semi- Structured Data. CSV, XML and Java. Script Object Notation (JSON) are examples of semi- structured data which are used in almost all applications. Sample of an XML file is given below. We can see that the XML file refers to a catalog and the books which are part of the catalog. This data can be stored in a relational database with some processing.< ? Gambardella, Matthew< /author>. XML Developer's Guide< /title>. Computer< /genre>. An in- depth look at creating applications. XML.< /description>. Ralls, Kim< /author>. Midnight Rain< /title>. Fantasy< /genre>. A former architect battles corporate zombies. Sample JSON content is given below. In the below example, we have the address and phone numbers of a user along with some other details. This information can also be stored in a relational database after processing.{. Name": "Adam". "last. Name": "Levine". "age": 2. Address": "1. 8 Elm Street". San Jose". "state": "CA". Code": "9. 40. 88". Number". "type": "home". Unstructured Data. Unstructured data does not have any predefined format. It does not follow a structured data model. It is not organized into a predefined structure. Images, videos, word documents, mp.
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