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What is Big Data?
As the name infers, Big Data is an enormous measure of data that is mind-boggling and hard to store, keep up or access in the standard document framework utilizing customary data handling applications. Also, what are the wellsprings of this enormous arrangement of data:-

  • A typical large stock exchange
  • Mobile phones
  • Video sharing portal like YouTube, Vimeo, Dailymotion etc.
  • Social networks like Facebook, Twitter, Linkedin etc.
  • Network sensors
  • Web pages, text and documents
  • Weblogs
  • System logs
  • Search index data
  • CCTV images.

Characteristics of Big Data
A regular file system with a typical data processing application faces the following challenges:

Volume – The volume of data coming from different sources is high and potentially increasing day by day.

Velocity – A single processor, limited RAM, and limited storage-based system are not enough to process this high volume of data.

Variety – Data coming from different sources varies

And therefore, Big Data Technology comes into the picture:

  • It assists with putting away, oversee and processing high volume and assortment of data in a cost and time-viable way.
  • It investigates data in its local structure, which could be unstructured, organized, or spilling.
  • It catches data from live occasions progressively.
  • It has a very much characterized and solid framework disappointment instrument which gives high accessibility. It handles framework uptime and vacation.
  • Using item equipment for data stockpiling and investigation.
  • Maintain numerous duplicates of similar data across bunches.
  • It stores data in obstructs in various machines and afterward consolidates them on request.

Data Types
Structured Data: Data which is presented in a tabular format and stores in RDMS (Relational Database Management System)

Semi-structured Data: Data that does not have a formal data model and is stored in XML, JSON, etc.

Unstructured Data: Data that does not have a pre-defined data model like video, audio, image, text, weblogs, system logs, etc.

What is HADOOP?

  • Hadoop is an open-source framework or stage for putting away and handling enormous scope data which can be organized, semi-organized or unstructured, in a circulated way.
  • It is open-source, modified in JAVA, and dispersed by Apache Foundation.
  • Hadoop can without much of a stretch handle multi-terabyte of data dependably and in an issue tolerant way.
  • Hadoop parallelizes the preparing of the data on 1000s of PCs or hubs in a group.
  • This framework utilizes item equipment for putting away appropriated data across different hubs on the group.

HADOOP Ecosystem & Core Components

  • Hadoop Common: Common utilities supporting Hadoop components. These libraries provide file system and OS-level abstraction. These also contain necessary Java Files and Scripts to start Hadoop.
  • HDFS: Hadoop Distributed File System (Storage Component)
  • YARN: Framework for job scheduling and resource management.
  • Map Reduce: Parallel processing mechanism for distributed data (Processing Component)

Hadoop Sub Components

  • HBase: Column-oriented No-SQL Database
  • Hive: Data warehouse for Distributed File System supporting primarily for data analysis.
  • Pig: High-level data flow language for ETL-like implementations.
  • Sqoop: Data Migration tool from RDBMS to HDFS and vice-versa.
  • Flume: Data Collection mechanism for Log & Even data (Streaming data)
  • Oozie: Workflow Management Service
  • Zookeeper: Configuration management and coordination service
  • HCatalog: Common Interface for Hive, Pig, HBase
  • Avro: Data Serialization Framework.

Big Data Technologies
There are different advancements in the market from various sellers including Amazon, IBM, Microsoft, and so on., to deal with large data.

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    The Big Data Problem

    To understand the big data problem, let’s consider some of the examples below:

    Example-1: Consider there are 3 tables ??tab1 has 100 records, tab2 has 1000 and tab3 has 100000 records.

    Now consider the following queries:




    Here among the given queries, 1st one runs faster followed by 2nd and 3rd one. Here even if the algorithm (COUNT) is same, as the data volume increases (100 records in TAB1 to 100000 records in TAB3), the processing speed goes down. This is the first problem with traditional RDBMS.

    Problem-1: With an Increase in DATA VOLUME, processing speed decreases.

    Example-2: Now consider the following 3 queries with the same table (i.e., same number of records for all of the 3 queries):




    Here even if the record count is the same, still the first query runs faster followed by 2nd and 3rd one. Hence, as the complexity of the algorithm increases, the processing time decreases. This is the second problem with traditional RDBMS.

    Problem-2: With the increase in DATA COMPLEXITY, processing speed decreases.

    Example-3: Consider the following example of a Facebook Friends relationship:











    Here even if the data is stored in a structured format (in form of rows and columns), if we ask what is the relationship between Tanya & Ravi, it is very difficult to answer with a normal SQL query. This kind of data where record-to-record relationships exists is called Graph Data. This kind of data can’t be processed easily with traditional RDBMS. This is another problem.

    Problem-3: Traditional RDBMS can’t handle All Kinds of Structured Data.

    Example-4: Consider the following table structure:



















    In this case, even though we can store the photo of an employee on our table, we wouldn’t be able to validate the photo with traditional SQL. For example, for Ramesh, if I store Tanya's photo, RDBMS will still store it and I have no way with traditional SQL to validate that I am actually storing a Female photo for a Male record. This is another problem of RDBMS where it can’t handle all kinds of data.

    Problem-4: Traditional RDBMS can’t handle all VARIETIES of data

    Apart from the above problems with the processing of the data, traditional RDBMS is limited in terms of Storage Capacity. For example, if we consider Facebook posts in a day, there are Millions and Billions of transactions (posting a comment, posting a status update, updating profile pic, likes, shares, etc.) happening each second. Hence this is not possible with Traditional RDBMS to store all of that data and process it.

    Hence Big Data can be defined as the combination of Huge Volume Data and Complex Data.

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