Category

Courses

Search

Trending blog posts

Types Of Big Data

Big Data is creating a revolution in the IT field, every year the use of analytics is increasing drastically every year. We are creating 2.5 quintillion bytes of data every day hence the field is expanding in B2C apps. Big Data has entered almost every industry today and is a dominant driving force behind the success of enterprises and organizations across the Globe. Let us first discuss- “What is Big Data?” “Data” is defined as ‘the quantities, characters, or symbols on which operations are performed by a computer, which may be stored and transmitted in the form of electrical signals and recorded on magnetic, optical, or mechanical recording media’, as a quick google search will show. The concept of Big Data is nothing complex; as the name suggests, “Big Data” refers to copious amounts of data which are too large to be processed and analyzed by traditional tools, and the data is not stored or managed efficiently. Since the amount of Big Data increases exponentially- more than 500 terabytes of data are uploaded to Facebook alone, in a single day- it represents a real problem in terms of analysis. Before we jump into the article, let's have a visual introduction on what is Big data and its types. (Structured Data, Semi-Structured & Unstructured Data) Types of Big Data: Classification is essential for the study of any subject. So Big Data is widely classified into three main types, which are- Structured Unstructured Semi-structured 1. Structured data Structured Data is used to refer to the data which is already stored in databases, in an ordered manner. It accounts for about 20% of the total existing data and is used the most in programming and computer-related activities. There are two sources of structured data- machines and humans. All the data received from sensors, weblogs, and financial systems are classified under machine-generated data. These include medical devices, GPS data, data of usage statistics captured by servers and applications and the huge amount of data that usually move through trading platforms, to name a few. Human-generated structured data mainly includes all the data a human input into a computer, such as his name and other personal details. When a person clicks a link on the internet, or even makes a move in a game, data is created- this can be used by companies to figure out their customer behavior and make the appropriate decisions and modifications. Let’s understand Structured data with an example. Top 3 players who have scored most runs in international T20 matches are as follows: Player Country Scores No of Matches played                Brendon McCullum New Zealand                                 2140                                           71                    Rohit Sharma India     2237          90 Virat Kohli  India      2167          65 2. Unstructured data While structured data resides in the traditional row-column databases, unstructured data is the opposite- they have no clear format in storage. The rest of the data created, about 80% of the total account for unstructured big data. Most of the data a person encounters belong to this category- and until recently, there was not much to do to it except storing it or analyzing it manually. Unstructured data is also classified based on its source, into machine-generated or human-generated. Machine-generated data accounts for all the satellite images, the scientific data from various experiments and radar data captured by various facets of technology. Human-generated unstructured data is found in abundance across the internet since it includes social media data, mobile data, and website content. This means that the pictures we upload to Facebook or Instagram handle, the videos we watch on YouTube and even the text messages we send all contribute to the gigantic heap that is unstructured data. Examples of unstructured data include text, video, audio, mobile activity, social media activity, satellite imagery, surveillance imagery – the list goes on and on. The following image will clearly help you to understand what exactly Unstructured data is The Unstructured data is further divided into – Captured User-Generated data a. Captured data: It is the data based on the user’s behavior. The best example to understand it is GPS via smartphones which help the user each and every moment and provides a real-time output. b. User-generated data: It is the kind of unstructured data where the user itself will put data on the internet every movement. For example, Tweets and Re-tweets, Likes, Shares, Comments, on Youtube, Facebook, etc. 3. Semi-structured data: The line between unstructured data and semi-structured data has always been unclear since most of the semi-structured data appear to be unstructured at a glance. Information that is not in the traditional database format as structured data, but contains some organizational properties which make it easier to process, are included in semi-structured data. For example, NoSQL documents are considered to be semi-structured, since they contain keywords that can be used to process the document easily. Big Data analysis has been found to have definite business value, as its analysis and processing can help a company achieve cost reductions and dramatic growth. So it is imperative that you do not wait too long to exploit the potential of this excellent business opportunity. Diagram showing Semi-structured data Difference between Structured, Semi-structured and Unstructured data       Factors      Structured data       Semi-structured data     Unstructured data Flexibility It is dependent and less flexible It is more flexible than structured data but less than flexible than unstructured data It is flexible in nature and there is an absence of a schema Transaction Management Matured transaction and various concurrency technique The transaction is adapted from DBMS not matured No transaction management and no concurrency Query performance Structured query allow complex joining Queries over anonymous nodes are possible An only textual query is possible Technology It is based on the relational database table It is based on RDF and XML This is based on character and library data Big data is indeed a revolution in the field of IT. The use of Data analytics is increasing every year. In spite of the demand, organizations are currently short of experts. To minimize this talent gap many training institutes are offering courses on Big data analytics which helps you to upgrade skills set needed to manage and analyze big data. If you are keen to take up data analytics as a career then taking up Big data training will be an added advantage .

Types Of Big Data

4K
Types Of Big Data

Big Data is creating a revolution in the IT field, every year the use of analytics is increasing drastically every year. We are creating 2.5 quintillion bytes of data every day hence the field is expanding in B2C apps. Big Data has entered almost every industry today and is a dominant driving force behind the success of enterprises and organizations across the Globe.

Let us first discuss- “What is Big Data?”

“Data” is defined as ‘the quantities, characters, or symbols on which operations are performed by a computer, which may be stored and transmitted in the form of electrical signals and recorded on magnetic, optical, or mechanical recording media’, as a quick google search will show.

The concept of Big Data is nothing complex; as the name suggests, “Big Data” refers to copious amounts of data which are too large to be processed and analyzed by traditional tools, and the data is not stored or managed efficiently. Since the amount of Big Data increases exponentially- more than 500 terabytes of data are uploaded to Facebook alone, in a single day- it represents a real problem in terms of analysis.

Before we jump into the article, let's have a visual introduction on what is Big data and its types. (Structured Data, Semi-Structured & Unstructured Data)

Types of Big Data:

Classification is essential for the study of any subject. So Big Data is widely classified into three main types, which are-

Types of Big Data:

  • Structured
  • Unstructured
  • Semi-structured

1. Structured data

Structured Data is used to refer to the data which is already stored in databases, in an ordered manner. It accounts for about 20% of the total existing data and is used the most in programming and computer-related activities.

There are two sources of structured data- machines and humans. All the data received from sensors, weblogs, and financial systems are classified under machine-generated data. These include medical devices, GPS data, data of usage statistics captured by servers and applications and the huge amount of data that usually move through trading platforms, to name a few.

Human-generated structured data mainly includes all the data a human input into a computer, such as his name and other personal details. When a person clicks a link on the internet, or even makes a move in a game, data is created- this can be used by companies to figure out their customer behavior and make the appropriate decisions and modifications.

Let’s understand Structured data with an example.

Top 3 players who have scored most runs in international T20 matches are as follows:

Structured data in Big Data Types

Player Country Scores No of Matches played               
Brendon McCullum New Zealand                                 2140                                           71                   
Rohit Sharma India     2237          90
Virat Kohli  India      2167          65


2. Unstructured data

While structured data resides in the traditional row-column databases, unstructured data is the opposite- they have no clear format in storage. The rest of the data created, about 80% of the total account for unstructured big data. Most of the data a person encounters belong to this category- and until recently, there was not much to do to it except storing it or analyzing it manually.

Unstructured data is also classified based on its source, into machine-generated or human-generated. Machine-generated data accounts for all the satellite images, the scientific data from various experiments and radar data captured by various facets of technology.

Human-generated unstructured data is found in abundance across the internet since it includes social media data, mobile data, and website content. This means that the pictures we upload to Facebook or Instagram handle, the videos we watch on YouTube and even the text messages we send all contribute to the gigantic heap that is unstructured data.

Examples of unstructured data include text, video, audio, mobile activity, social media activity, satellite imagery, surveillance imagery – the list goes on and on.

The following image will clearly help you to understand what exactly Unstructured data is

Unstructured data in Big Data Types

The Unstructured data is further divided into –

  • Captured
  • User-Generated data

a. Captured data:

It is the data based on the user’s behavior. The best example to understand it is GPS via smartphones which help the user each and every moment and provides a real-time output.

b. User-generated data:

It is the kind of unstructured data where the user itself will put data on the internet every movement. For example, Tweets and Re-tweets, Likes, Shares, Comments, on Youtube, Facebook, etc.

3. Semi-structured data:

The line between unstructured data and semi-structured data has always been unclear since most of the semi-structured data appear to be unstructured at a glance. Information that is not in the traditional database format as structured data, but contains some organizational properties which make it easier to process, are included in semi-structured data. For example, NoSQL documents are considered to be semi-structured, since they contain keywords that can be used to process the document easily.

Big Data analysis has been found to have definite business value, as its analysis and processing can help a company achieve cost reductions and dramatic growth. So it is imperative that you do not wait too long to exploit the potential of this excellent business opportunity.

Diagram showing Semi-structured data

Semi-structured data in Big Data Types

Difference between Structured, Semi-structured and Unstructured data

      Factors      Structured data       Semi-structured data     Unstructured data
Flexibility It is dependent and less flexible It is more flexible than structured data but less than flexible than unstructured data It is flexible in nature and there is an absence of a schema
Transaction Management Matured transaction and various concurrency technique The transaction is adapted from DBMS not matured No transaction management and no concurrency
Query performance Structured query allow complex joining Queries over anonymous nodes are possible An only textual query is possible
Technology It is based on the relational database table It is based on RDF and XML This is based on character and library data

Big data is indeed a revolution in the field of IT. The use of Data analytics is increasing every year. In spite of the demand, organizations are currently short of experts. To minimize this talent gap many training institutes are offering courses on Big data analytics which helps you to upgrade skills set needed to manage and analyze big data. If you are keen to take up data analytics as a career then taking up Big data training will be an added advantage
.

KnowledgeHut

KnowledgeHut

Author

KnowledgeHut is an outcome-focused global ed-tech company. We help organizations and professionals unlock excellence through skills development. We offer training solutions under the people and process, data science, full-stack development, cybersecurity, future technologies and digital transformation verticals.
Website : https://www.knowledgehut.com

Join the Discussion

Your email address will not be published. Required fields are marked *

2 comments

Tony 20 Apr 2017

Hi, Thanks for sharing the information. These information will really help us a lot.

Rebheka 31 May 2018

This data is useful

Trending blog posts

Who Should Get CSPO® Certified? Published 01 Oct 2020 Blogs
Top Learning Outcomes of Leading SAFe 5 Certification Published 01 Oct 2020 Blogs
What Are React.Js Prop Published 01 Oct 2020 Blogs
Why Do We Use Fibonacci Series for Estimation Published 25 Sep 2020 Blogs
Data Science: Correlation vs Regression in Statistics Published 25 Sep 2020 Blogs
A Peek Into the World of Data Science Published 25 Sep 2020 Blogs
Write for US

Suggested Blogs

5 Best Data Processing Frameworks

“Big data Analytics” is a phrase that was coined to refer to amounts of datasets that are so large, traditional data processing software simply can’t manage them. For example, big data is used to pick out trends in economics, and those trends and patterns are used to predict what will happen in the future. These vast amounts of data require more robust computer software for processing, best handled by data processing frameworks. These are the top preferred data processing frameworks, suitable for meeting a variety of different needs of businesses. Hadoop This is an open-source batch processing framework that can be used for the distributed storage and processing of big data sets. Hadoop relies on computer clusters and modules that have been designed with the assumption that hardware will inevitably fail, and those failures should be automatically handled by the framework. There are four main modules within Hadoop. Hadoop Common is where the libraries and utilities needed by other Hadoop modules reside. The Hadoop Distributed File System (HDFS) is the distributed file system that stores the data. Hadoop YARN (Yet Another Resource Negotiator) is the resource management platform that manages the computing resources in clusters, and handles the scheduling of users’ applications. The Hadoop MapReduce involves the implementation of the MapReduce programming model for large-scale data processing. Hadoop operates by splitting files into large blocks of data and then distributing those datasets across the nodes in a cluster. It then transfers code into the nodes, for processing data in parallel. The idea of data locality, meaning that tasks are performed on the node that stores the data, allows the datasets to be processed more efficiently and more quickly. Hadoop can be used within a traditional onsite datacenter, as well as through the cloud. Apache Spark Apache Spark is a batch processing framework that has the capability of stream processing, as well, making it a hybrid framework. Spark is most notably easy to use, and it’s easy to write applications in Java, Scala, Python, and R. This open-source cluster-computing framework is ideal for machine-learning, but does require a cluster manager and a distributed storage system. Spark can be run on a single machine, with one executor for every CPU core. It can be used as a standalone framework, and you can also use it in conjunction with Hadoop or Apache Mesos, making it suitable for just about any business. Spark relies on a data structure known as the Resilient Distributed Dataset (RDD). This is a read-only multiset of data items that is distributed over the entire cluster of machines. RDDs operate as the working set for distributed programs, offering a restricted form of distributed shared memory. Spark is capable of accessing data sources like HDFS, Cassandra, HBase, and S3, for distributed storage. It also supports a pseudo-distributed local mode that can be used for development or testing. The foundation of Spark is Spark Core, which relies on the RDD-oriented functional style of programming to dispatch tasks, schedule, and handle basic I/O functionalities. Two restricted forms of shared variables are used: broadcast variables, which reference read-only data that has to be available for all the nodes, and accumulators, which can be used to program reductions. Other elements included in Spark Core are: Spark SQL, which provides domain-specific language used to manipulate DataFrames. Spark Streaming, which uses data in mini-batches for RDD transformations, allowing the same set of application code that is created for batch analytics to also be used for streaming analytics. Spark MLlib, a machine-learning library that makes the large-scale machine learning pipelines simpler. GraphX, which is the distributed graph processing framework at the top of Apache Spark. Apache Storm This is another open-source framework, but one that provides distributed, real-time stream processing. Storm is mostly written in Clojure, and can be used with any programming language. The application is designed as a topology, with the shape of a Directed Acyclic Graph (DAG). Spouts and bolts act as the vertices of the graph. The idea behind Storm is to define small, discrete operations, and then compose those operations into a topology, which acts as a pipeline to transform data. Within Storm, streams are defined as unbounded data that continuously arrives at the system. Sprouts are sources of data streams that are at the edge of the topology, while bolts represent the processing aspect, applying an operation to those data streams. The streams on the edges of the graph direct data from one node to another. These bolts and sprouts define sources of information and allow batch, distributed processing of streaming data, in real-time. Samza Samza is another open-source framework that offers near a real-time, asynchronous framework for distributed stream processing. More specifically, Samza handles immutable streams, meaning transformations create new streams that will be consumed by other components without any effect on the initial stream. This framework works in conjunction with other frameworks, using Apache Kafka for messaging and Hadoop YARN for fault tolerance, security, and management of resources. Samza uses the semantics of Kafka to define how it handles streams. Topic refers to each stream of data that enters a Kafka system. Brokers are the individual nodes that are combined to make a Kafka cluster. A producer is any component that writes to a Kafka topic, and a consumer is any component that reads from a Kafka topic. Partitions are used to divide incoming messages in order to distribute a topic among the different nodes. Flink Flink is a hybrid framework, open-source, and stream processes, but can also manage batch tasks. It uses a high-throughput, low-latency streaming engine that is written in Java and Scala, and the runtime system that is pipelined allows for the execution of both batch and stream processing programs. The runtime also supports the execution of iterative algorithms natively. Flink’s applications are all fault-tolerant and can support exactly-once semantics. Programs can be written in Java, Scala, Python, and SQL, and Flink offers support for event-time processing and state management. The components of the stream processing model in Flink include streams, operators, sources, and sinks. Streams are immutable, unbounded datasets that go through the system. Operators are functions that are used on data streams to create other streams. Sources are the entry points for streams that enter into the system. Sinks are places where streams flow out of the Flink system, either into a database or into a connection to another system. Flink’s batch processing system is really just an extension of the stream processing model. Flink does not provide its own storage system, however, so that means you will have to use it in conjunction with another framework. That should not be a problem, as Flink is able to work with many other frameworks. Data processing frameworks are not intended to be one-size-fits-all solutions for businesses. Hadoop was originally designed for massive scalability, while Spark is better with machine learning and stream processing. A good IT services consultant can evaluate your needs and offer advice. What works for one business may not work for another, and to get the best possible results, you may find that it’s a good idea to use different frameworks for different parts of your data processing.
5 Best Data Processing Frameworks
Blogs
3817
5 Best Data Processing Frameworks

“Big data Analytics” is a phrase that was coin... Read More

5 Big Data Challenges in 2021

The year 2019 saw some enthralling changes in volume and variety of data across businesses, worldwide. The surge in data generation is only going to continue. Foresighted enterprises are the ones who will be able to leverage this data for maximum profitability through data processing and handling techniques. With the rise in opportunities related to Big Data, challenges are also bound to increase.Below are the 5 major Big Data challenges that enterprises face in 2020:1. The Need for More Trained ProfessionalsResearch shows that since 2018, 2.5 quintillion bytes (or 2.5 exabytes) of information is being generated every day. The previous two years have seen significantly more noteworthy increments in the quantity of streams, posts, searches and writings, which have cumulatively produced an enormous amount of data. Additionally, this number is only growing by the day. A study has predicted that by 2025, each person will be making a bewildering 463 exabytes of information every day.A report by Indeed, showed a 29 percent surge in the demand for data scientists yearly and a 344 percent increase since 2013 till date. However, the searches by job seekers skilled in data science continue to grow at a snail’s pace at 14 percent. In August 2018, LinkedIn reported claimed that US alone needs 151,717 professionals with data science skills. This along with a 15 percent discrepancy between job postings and job searches on Indeed, makes it quite evident that the demand for data scientists outstrips supply. The greatest data processing challenge of 2020 is the lack of qualified data scientists with the skill set and expertise to handle this gigantic volume of data.2. Inability to process large volumes of dataOut of the 2.5 quintillion data produced, only 60 percent workers spend days on it to make sense of it. A major portion of raw data is usually irrelevant. And about 43 percent companies still struggle or aren’t fully satisfied with the filtered data. 3. Syncing Across Data SourcesOnce you import data into Big Data platforms you may also realize that data copies migrated from a wide range of sources on different rates and schedules can rapidly get out of the synchronization with the originating system. This implies two things, one, the data coming from one source is out of date when compared to another source. Two, it creates a commonality of data definitions, concepts, metadata and the like. The traditional data management and data warehouses, and the sequence of data transformation, extraction and migration- all arise a situation in which there are risks for data to become unsynchronized.4. Lack of adequate data governanceData collected from multiple sources should have some correlation to each other so that it can be considered usable by enterprises. In a recent Big Data Maturity Survey, the lack of stringent data governance was recognized the fastest-growing area of concern. Organizations often have to setup the right personnel, policies and technology to ensure that data governance is achieved. This itself could be a challenge for a lot of enterprises.5. Threat of compromised data securityWhile Big Data opens plenty of opportunities for organizations to grow their businesses, there’s an inherent risk of data security. Some of the biggest cyber threats to big players like Panera Bread, Facebook, Equifax and Marriot have brought to light the fact that literally no one is immune to cyberattacks. As far as Big Data is concerned, data security should be high on their priorities as most modern businesses are vulnerable to fake data generation, especially if cybercriminals have access to the database of a business. However, regulating access is one of the primary challenges for companies who frequently work with large sets of data. Even the way Big Data is designed makes it harder for enterprises to ensure data security. Working with data distributed across multiple systems makes it both cumbersome and risky.Overcoming Big Data challenges in 2020Whether it’s ensuring data governance and security or hiring skilled professionals, enterprises should leave no stone unturned when it comes to overcoming the above Big Data challenges. Several courses and online certifications are available to specialize in tackling each of these challenges in Big Data. Training existing personnel with the analytical tools of Big Data will help businesses unearth insightful data about customer. Frameworks related to Big Data can help in qualitative analysis of the raw information.
5 Big Data Challenges in 2021
Blogs
1344
5 Big Data Challenges in 2021

The year 2019 saw some enthralling changes in volu... Read More

How to install Apache Spark on Windows?

Apache Spark is a fast and general-purpose cluster computing system. It provides high-level APIs in Java, Scala, Python and R, and an optimized engine that supports general execution graphs. It also supports a rich set of higher-level tools including Spark SQL for SQL and structured data processing, MLlib for machine learning, GraphX for graph processing, and Spark Streaming.In this document, we will cover the installation procedure of Apache Spark on Windows 10 operating systemPrerequisitesThis guide assumes that you are using Windows 10 and the user had admin permissions.System requirements:Windows 10 OSAt least 4 GB RAMFree space of at least 20 GBInstallation ProcedureStep 1: Go to the below official download page of Apache Spark and choose the latest release. For the package type, choose ‘Pre-built for Apache Hadoop’.The page will look like below.Step 2:  Once the download is completed unzip the file, to unzip the file using WinZip or WinRAR or 7-ZIP.Step 3: Create a folder called Spark under your user Directory like below and copy paste the content from the unzipped file.C:\Users\\SparkIt looks like below after copy-pasting into the Spark directory.Step 4: Go to the conf folder and open log file called, log4j.properties. template. Change INFO to WARN (It can be ERROR to reduce the log). This and next steps are optional.Remove. template so that Spark can read the file.Before removing. template all files look like below.After removing. template extension, files will look like belowStep 5: Now we need to configure path.Go to Control Panel -> System and Security -> System -> Advanced Settings -> Environment VariablesAdd below new user variable (or System variable) (To add new user variable click on New button under User variable for )Click OK.Add %SPARK_HOME%\bin to the path variable.Click OK.Step 6: Spark needs a piece of Hadoop to run. For Hadoop 2.7, you need to install winutils.exe.You can find winutils.exe from below pageDownload it.Step 7: Create a folder called winutils in C drive and create a folder called bin inside. Then, move the downloaded winutils file to the bin folder.C:\winutils\binAdd the user (or system) variable %HADOOP_HOME% like SPARK_HOME.Click OK.Step 8: To install Apache Spark, Java should be installed on your computer. If you don’t have java installed in your system. Please follow the below processJava Installation Steps:Go to the official Java site mentioned below  the page.Accept Licence Agreement for Java SE Development Kit 8u201Download jdk-8u201-windows-x64.exe fileDouble Click on Downloaded .exe file, you will the window shown below.Click Next.Then below window will be displayed.Click Next.Below window will be displayed after some process.Click Close.Test Java Installation:Open Command Line and type java -version, then it should display installed version of JavaYou should also check JAVA_HOME and path of %JAVA_HOME%\bin included in user variables (or system variables)1. In the end, the environment variables have 3 new paths (if you need to add Java path, otherwise SPARK_HOME and HADOOP_HOME).2. Create c:\tmp\hive directory. This step is not necessary for later versions of Spark. When you first start Spark, it creates the folder by itself. However, it is the best practice to create a folder.C:\tmp\hiveTest Installation:Open command line and type spark-shell, you get the result as below.We have completed spark installation on Windows system. Let’s create RDD and     Data frameWe create one RDD and Data frame then will end up.1. We can create RDD in 3 ways, we will use one way to create RDD.Define any list then parallelize it. It will create RDD. Below is code and copy paste it one by one on the command line.val list = Array(1,2,3,4,5) val rdd = sc.parallelize(list)Above will create RDD.2. Now we will create a Data frame from RDD. Follow the below steps to create Dataframe.import spark.implicits._ val df = rdd.toDF("id")Above code will create Dataframe with id as a column.To display the data in Dataframe use below command.Df.show()It will display the below output.How to uninstall Spark from Windows 10 System: Please follow below steps to uninstall spark on Windows 10.Remove below System/User variables from the system.SPARK_HOMEHADOOP_HOMETo remove System/User variables please follow below steps:Go to Control Panel -> System and Security -> System -> Advanced Settings -> Environment Variables, then find SPARK_HOME and HADOOP_HOME then select them, and press DELETE button.Find Path variable Edit -> Select %SPARK_HOME%\bin -> Press DELETE ButtonSelect % HADOOP_HOME%\bin -> Press DELETE Button -> OK ButtonOpen Command Prompt the type spark-shell then enter, now we get an error. Now we can confirm that Spark is successfully uninstalled from the System.
How to install Apache Spark on Windows?
Blogs
9449
How to install Apache Spark on Windows?

Apache Spark is a fast and general-purpose cluster... Read More

Useful links