Introduction


Big Data refers to voluminous, unstructured, and complex data sets that cannot be processed using traditional software. Such software lags behind when it comes to big data processing, analysis, management, sharing, visualization, security, and storage. Because of its unstructured nature, any attempt to use traditional software in big data integration leads to errors and clumsy operations. Big data platforms seek to process data more efficiently while minimizing error rates compared to relational databases used for standard data manipulation.

With so many statistics and data coming out, scientists are always confused about what data science is and whether Hadoop is an essential part of it or not. Here you will know whether learning Hadoop is required to become a data scientist or not.

What is Hadoop?


Hadoop is an open-source framework from Apache and is used to store process and analysis data with a large volume. Hadoop is not OLAP(Online Analytical Processing) and is written in Java. Used for batch/offline processing. It is used by Facebook, Yahoo, Google, Twitter, LinkedIn, and many others. Additionally, it can be scaled up simply by adding nodes to the cluster.

Hadoop Modules


HDFS: Hadoop Distributed File System. Google published their GFS paper and based on that, HDFS was developed. It states that the files will be divided into blocks and stored in the nodes of the distributed architecture.

Yarn: Yet Another Resource Negotiator used for job scheduling and cluster management.

Map Reduce: This framework helps Java Programs perform parallel computations on data using key-value pairs. The Map task takes the input data and converts it to a dataset that can be calculated in a key-value pair. The reduced task consumes the output of the map task, and the output tool from the reducer then produces the desired outcome.

Common Hadoop: These Java libraries are needed by other Hadoop modules to execute Hadoop.

Hadoop Architecture


HDFS, the MapReduce engine, and the file system are all included in the Hadoop architecture (Hadoop Distributed File System). MapReduce engines come in two flavors: MR1 and MR2.
A Hadoop cluster is made up of a master node and numerous slave nodes. A master node includes a Job Tracker, a Task Tracker, a NameNode, and a DataNode, while a child node includes a DataNode and a TaskTracker.


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How Does Hadoop Work?


Building larger servers with heavy configurations that handle large-scale processing is pretty expensive. Still, as an alternative, you can connect many commodity computers with a single CPU as a single functional distributed system, and practically the clustered machines can read the data set. in parallel and provide much higher throughput. In addition, it is cheaper than a single high-end server. So this is the first motivating factor behind using Hadoop, which runs on clustered and affordable machines.
Hadoop runs code on a cluster of computers. Does this process include the following basic tasks that Hadoop performs?
  • Data is initially divided into directories and files. Files are divided into blocks of uniform size of 128M and 64M (preferably 128M).
  • These files are then distributed among the different nodes of the cluster for further processing.
  • HDFS, which is on top of the local file system, oversees the processing.
  • Blocks are replicated to handle hardware failure.
  • Checking if the code was executed successfully.
  • Doing the sorting that takes place between the map and shrinking phases.
  • Sending sorted data to a specific computer.
  • Writing debug logs for each job.

Why Should Data Scientists opt for Hadoop?


Let's say a job based on data analysis would take around 20-25 minutes to complete. The identical task will be finished in half the time if we use twice as many computers to accomplish the activity. This is where Hadoop comes into play. With Hadoop, we can achieve linear scalability through hardware. The data can first be loaded into Hadoop, and then any questions can be asked about the dataset.

Another essential part of working with Hadoop is that a data scientist does not need to master distributed systems because Hadoop offers transparent parallelism. A data scientist needs to write Java-based code using Pig and Hive etc.

Hadoop Is An Important Tool For Data Scientists


Hadoop plays a large and essential role when the volume of data surpasses the system memory or the business demands data distribution across numerous servers. With Hadoop, data can be easily transferred to different system nodes at a much faster speed, providing Data Scientists with greater efficiency.

Hadoop for data exploration task
More than 80% of a data scientist's time is spent preparing data, and data exploration is crucial. Hadoop works really well with data exploration because it helps in figuring out all the intricacies related to data that are difficult for data scientists to understand. They can store data in Hadoop as-is without having to analyze it and engage in extensive data research.

Hadoop as a data filtering tool
Data needs to be filtered based on business requirements. With Hadoop, data scientists can easily filter a subset of data, and business problems can be easily solved.

Hadoop for data sampling
Since comparable types of data are frequently grouped together and data is made available in a very sophisticated way, it is not always feasible for a data scientist to work with just the first 1000 entries. Therefore, proper data sampling is necessary to obtain a correct representation of the data. Using Hadoop to sample data helps with data modeling and helps reduces the number of records.

Hadoop to summarize
Data scientists can gain insight into better data creation models using Hadoop MapReduce to summarize the data. Hadoop MapReduce is a data summarization system in which mappers collect data and reducers summarize it.

Impact of Using Hadoop on Data Scientists


Hadoop has had a significant impact on Data Scientists in four ways:

Advancing data agility:
Unlike traditional database systems that require a fixed schema structure, Hadoop allows users to create a flexible schema. This "schema on reading" or flexible schema reduces the requirement to rework the schema whenever a new field is required.

Preprocessing of large-scale data:
Most data pre-processing is done by data capture, transformation, cleaning, and feature extraction in Data Science tasks. Transforming the raw data into standardized character vectors requires this step.
For data scientists, Hadoop makes it easy to prepare data at scale. It includes tools like MapReduce, PIG, and Hive for efficiently managing large amounts of data.

Exploring data with big data:
Data Scientists must be able to work with vast amounts of data. Previously, they were limited to storing datasets on a local workstation. However, with the increasing amount of data and the increasing demand for big data analysis, Hadoop provides a platform for exploratory data analysis.
You can build a MapReduce job, a HIVE script, or a PIG script in Hadoop and run them over the entire dataset to get results. Students who want to master these additional tools should take a Hadoop administration course to build job-ready skills.

Facilitating data mining at scale:
Machine Learning algorithms have been shown to train better and provide better results with larger datasets. Clustering, outlier identification, and product recommendations are some statistical tools available.
Previously, machine learning experts had to work with a limited amount of data, which resulted in their models performing poorly. However, you can store all data in RAW format using the Hadoop environment, which provides linearly scalable storage.

Verdict on Hadoop
Because of its scalability and fault tolerance, Hadoop is commonly used to store huge volumes of data. It also provides a robust analytics platform with tools like Pig and Hive.

Conclusion


Finally, for data science, we conclude that Hadoop is a must. It is widely used for storing massive amounts of data due to its scalability and fault tolerance. It also enables a comprehensive analytics platform through tools like Pig and Hive. In addition, Hadoop has evolved to become a comprehensive platform for data science.