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The different Output formats in Hadoop are -

• Textoutputformat: TextOutputFormat is the default output FORMAT in Hadoop.
• Mapfileoutputformat: Mapfileoutputformat is USED to write the output as map files in Hadoop.
• DBoutputformat: DBoutputformat is just used for WRITING output in relational DATABASES and Hbase.
• Sequencefileoutputformat: Sequencefileoutputformat is used for writing SEQUENCE files.
• SequencefileAsBinaryoutputformat: SequencefileAsBinaryoutputformat is used to write keys to a sequence file in binary format.

The common INPUT formats in HADOOP are - 

• TEXT Input Format: This is the default input format in Hadoop.
• Key-Value Input Format: Used to READ Plain Text Files in Hadoop.
• SEQUENCE File Input format: This is used to read Files in a sequence in Hadoop.

• Local Mode or Standalone Mode:
  By default, Hadoop is configured to operate in a no distributed mode. It RUNS as a single Java process. Instead of HDFS, this mode utilizes the local file system. This mode is more helpful for debugging, and there isn't any requirement to configure core-site.xml, hdfs-site.xml, mapred-site.xml, masters & slaves. Standalone mode is ordinarily the quickest mode in Hadoop.
• Pseudo-distributed Mode:
  In this mode, each daemon runs on a separate java process. This mode requires custom configuration ( core-site.xml, hdfs-site.xml, mapred-site.xml). The HDFS is used for input and output. This mode of deployment is beneficial for testing and debugging purposes.
• Fully Distributed Mode:
  It is the production mode of Hadoop. One machine in the cluster is assigned as NameNode and another as RESOURCE Manager exclusively. These are masters. Rest nodes act as Data Node and Node Manager. These are the slaves. Configuration parameters and environment need to be defined for Hadoop Daemons. This mode gives fully distributed computing capacity, security, FAULT endurance, and SCALABILITY.

The term fsck stands for FILE SYSTEM Check, used by HDFS. It is used to check discrepancies and if there is any difficulty in the file. For instance, if there are any missing BLOCKS in the file, HDFS gets reported through this command.

Deploying a model into a Big Data Platform involves mainly three key steps they are,

• Data ingestion
• Data Storage
• Data Processing

Let’s have a look at what these are,

• Data Ingestion: This process involves COLLECTING data from different sources like social media platforms, business applications, log files, etc.
• Data Storage: When data extraction is completed, the challenge is to store this large volume of data in the database in which the Hadoop Distributed File system (HDFS) PLAYS a vital role.
• Data Processing: After storing the data in HDFS or HBase, the NEXT task is to analyze and visualize these large amounts of data using specific algorithms for better data processing. And yet again, this task is more straightforward if we use Hadoop, Apache SPARK, Pig, etc.

After performing these essential steps, one can deploy a big data model successfully.

Data Modeling as a business has been practiced in the IT sector for many decades. As an idea, the data model is a means to arrive at the diagram by examining the data in question and getting a deep knowledge. The method of representing the data visually encourages the business and the technology specialists to understand the data and understand how it will get used.

Kinds Of Data Models

The three principal types of data models are CONCEPTUAL, logical, and physical. Think of them as an improvement from an abstract layout to a detailed mapping of the database setup and final form:

• Conceptual Data Model:
  Conceptual data models are the most simplistic and abstract. Minor annotation happens in this model, but the overall layout and controls of the data relationships are set. You’ll find elements like basic MARKET rules that need to be applied, the levels or entity classes of data that you plan to cover, and any other regulations that may limit layout options. Data models are usually used in the development STAGE of a project.
• Logical Data Model:
  The logical data model extends on the basic framework laid out in the conceptual model, but it counts more relational factors. Thus, some basic annotations are related to overall properties or data attributes, but not many annotations concentrate on actual data units. Hence, this model is beneficial in data warehousing projects.
• Physical Data Model:
  The physical data model is the most comprehensive and the LAST step before database production, it usually accounts for database management system-specific properties and rules.

Advantages Of Data Modeling:

• Data modeling offers several different benefits to companies as part of their data management:
• Before you even build a database, you’ve CLEANED, organized, and modeled your data to project what your next step should look like. Data modeling advances data quality and makes databases limited, prone to mistakes and bad design.
• Data modeling produces a visual flow of data and how you plan to organize it. This supports employees know what’s happening with data and how they relate to the data management puzzle. It also develops data-related communication across departments in an organization.
• Data modeling allows more profound database design, bringing forth more useful applications and data-based business insights down the line.

NFS (Network File system): A protocol that enables CUSTOMERS to access files over the network. NFS clients would allow files to be accessed as if the files live on the local device, even though they live on the disk of a networked device.

HDFS (Hadoop Distributed File System): A distributed file system is SHARED between multiple networked machines or nodes. HDFS is fault-tolerant because it saves various copies of files on the file system; the default replication level is 3.
The notable difference between the two is Replication/Fault TOLERANCE. HDFS was intended to withstand failures. NFS does not possess any fault tolerance built-in.

Benefits of HDFS over NFS:
Apart from fault tolerance, HDFS helps to create multiple replicas of files. This reduces the traditional bottleneck of many clients ACCESSING a single file. In addition, since files have multiple images on various physical DISKS, reading performance scales better than NFS.

HADOOP assists in not only store data but also processing big data. It is the most RELIABLE way to handle significant data hurdles. Some salient features of Hadoop are –

• Distributed Processing – Hadoop helps in distributed processing of data, i.e., QUICKER processing. In Hadoop HDFS, the data is collected in a distributed manner, and the data is parallel processing, and MapReduce is liable for the same.
• Open Source – Hadoop is independent of cost as it is an open-source framework. Changes are allowed in the source code as per the user’s requirements.
• Fault Tolerance – Hadoop is highly fault-tolerant. By default, for every block, it creates three replicas at distinct nodes. This number of replicas can be MODIFIED according to the requirement. So, we can retrieve the data from a different node if one of the nodes fails. The discovery of node failure and restoration of data is made automatically.
• Scalability –  It is fitted with different hardware, and we can promptly access the new device.
• Reliability – The data in Hadoop is stored on the cluster in a safe manner that is AUTONOMOUS of the machine. So, the data stored in the Hadoop ecosystem’s data does not get affected by any machine breakdowns.

Hadoop is an open-source framework intending to store and process big data in a distributed manner.

Hadoop’s ESSENTIAL Components:

• HDFS (Hadoop Distributed File System) – Hadoop’s key storage system is HDFS. The extensive data is stored on HDFS. It is mainly devised for storing massive datasets in commodity hardware.
• Hadoop MapReduce – The responsible layer of Hadoop for data processing is MapReduce. It puts a request for processing of structured and unstructured data which is already stored in HDFS. It is liable for the parallel processing of a high volume of data by distributing data into detached tasks. There are two stages of processing: Map and Reduce. In simple terms, Map is a stage where data blocks are read and made available to the executors (COMPUTERS /nodes /containers) for processing. Reduce is a stage where all processed data is collected and collated.
• YARN – The framework which is USED to process in Hadoop is YARN. For resource management and to provide multiple data processing engines like real-time streaming, data science, and BATCH processing is done by YARN.

Since BIG data includes a large volume of data, i.e., structured, semi-structured, and unstructured data, analyzing and processing this data is quite a big task. There was a need for a tool or technology to help process the data at a rapid speed. THEREFORE, Hadoop is used because of its capabilities like storage, processing capability. Moreover, Hadoop is an open-source software. If you want to consider the cost, it’s beneficial for business solutions.
The main reason for its popularity in recent years is that this FRAMEWORK permits distributed processing of enormous data sets using CROSSWISE clusters of computers practicing simple programming models.

If we talk about Big Data, we do talk about HADOOP as well. So, this is ONE of the most CRITICAL questions from an interview perspective. That you MIGHT surely face. Hadoop is an open-source framework for SAVING, processing, and interpreting complex, disorganized data sets for obtaining insights and knowledge. So, that is how Hadoop and Big Data are related to each other. 

Irrespective of the division and scope of the firm, data is now an essential tool for businesses to utilise. COMPANIES are frequently using big data to gain a competing edge over business rivals.

Checking the datasets a company collects is just one part of the big data process. Big data professionals also need to know what the company requires from the APPLICATION and how they plan to use the data to their advantage.

• Confident decision-building: Analytics aims to develop decision building, and big data endures to sustain this. Big data can help enterprises speed up their decision-making method with so much data available while still being assured of their choice. Nowadays, moving fast and reacting to broader trends and operational changes is a huge business benefit in a quick-paced society.
• Asset optimisation: Big data signifies that businesses can control assets at a personal level. This implies they can adequately optimise assets depending on the data source, improve productivity, extend the lifespan of help, and reduce the downtime some assets may require. This gives a competing advantage by assuring the company is getting the most out of its assets and links with decreasing costs.
• Cost reduction: Big data can support businesses to reduce their outgoings. From analysing energy usage to assessing the effectiveness of staff operating patterns, data collected by companies can help them RECOGNISE where they can make cost savings without having a negative impact on company operations.
• Improve customer engagement: When surveying online, consumers make confident choices indicating their decisions, habits, and tendencies that can then be used to develop and tailor consumer dialogue, which could then be interpreted into increased sales. Understanding what each client is looking for through the data collected on them means you can target them with specific products, but it also gives a personal FEEL that many consumers today have come to await.
• Identify new revenue streams: Analytics can further assist companies in identifying new revenue streams and expanding into other areas. For example, knowing customer trends and decisions allow firms to decide the way they should GO. The data companies accumulate can also likely be sold, adding income streams and the potential to build alliances with other businesses.

• VOLUME: A considerable amount of data stored in data warehouses reflects the volume. The data may reach random HEIGHTS; these large volumes of data need to be examined and processed. Which may exist up to or more than terabytes and petabytes.
• Velocity: Velocity basically introduces the pace at which data is being produced in real-time. To give a simple example for recognition, imagine the rate at which Facebook, Instagram, or Twitter posts are generated per second, an hour or more.
• Variety:  Big Data comprises structured, unstructured, and semi-structured data collected from VARIED sources. This different variety of data requires very different and specific analyzing and processing techniques with unique and appropriate algorithms.
• Veracity: Data veracity basically relates to how reliable the data is, or in a fundamental way, we can DEFINE it as the quality of the data analyzed.
• Value: Raw data is of no use or meaning but once CONVERTED into something valuable. We can extract helpful information. 

Big Data refers to extensive and often complicated data sets so huge that they’re beyond the capacity of managing with conventional software TOOLS. Big Data COMPRISES unstructured and structured data sets such as videos, photos, audio, websites, and multimedia content.

Businesses collect the data they need in countless ways, such as:

• Internet cookies
• Email tracking
• Smartphones
• Smartwatches
• Online purchase transaction forms
• Website interactions
• Transaction histories
• Social media posts
• Third-party trackers -companies that collect and sell clients and profitable data

Working with big data involves three sets of activities:

• Integration: This involves merging data often from different sources – and molding it into a form that can be analysed in a way to provide insights.
• Management: Big data MUST be stored in a repository where it can be collected and readily reached. The largest amount of Big Data is unstructured, CAUSING it ill-suited for conventional relational databases, which need data in tables-and-rows format.
• Analysis: The Big Data investment return is a spectrum of worthy market insights, including details on BUYING patterns and customer choices. These are represented by examining large data sets with tools driven by AI and machine learning.