Data Processing in Hadoop Ecosystem: Complete Data Flow Explained

With the exponential growth of the World Wide Web over the years, the data being generated also grew exponentially. This led to a massive amount of data being created and it was difficult to process and store this humungous amount of data with the traditional relational database systems.

Also, the data created was not only in the structured form but also in the unstructured format like videos, images, etc. Relational databases cannot process this kind of data. To counter these issues, Hadoop came into existence.

Before we dive into the data processing of Hadoop, let us have an overview of Hadoop and its components. Apache Hadoop is a framework that allows the storing and processing of huge quantities of data swiftly and efficiently. It can be used to store huge quantities of structured and unstructured data. Learn more about the Hadoop ecosystem and components.

Let’s begin with a workflow overview of data processing in Hadoop. 

Data Processing in Hadoop: Complete Workflow

Data processing in Hadoop follows a structured flow that ensures large datasets are efficiently processed across distributed systems. The process starts with raw data being divided into smaller chunks, processed in parallel, and finally aggregated to generate meaningful output. 

Understanding this step-by-step workflow is essential to optimizing performance and managing large-scale data effectively.

InputSplit and RecordReader

Before processing begins, Hadoop logically divides the dataset into manageable parts. This ensures that data is efficiently read and distributed across the cluster, optimizing resource utilization and parallel execution. Logical splitting prevents unnecessary data fragmentation, reducing processing overhead and enhancing cluster efficiency.

Below is how it works:

• InputSplit divides data into logical chunks: These chunks do not physically split files but create partitions for parallel execution. The size of each split depends on the HDFS block size (default 128MB) and can be customized based on cluster configuration. For example, a 10GB file might be divided into five 2GB splits, allowing multiple nodes to process different parts simultaneously without excessive disk seeks.
• RecordReader converts InputSplit data into key-value pairs: Hadoop processes data in key-value pairs. The RecordReader reads raw data from an InputSplit and structures it for the mapper. For instance, in a text processing job, it may convert each line into a key-value pair where the key is the byte offset and the value is the line content.

Also Read: Hadoop YARN Architecture: Comprehensive Guide to YARN Components and Functionality

With data split into smaller logical units and structured into key-value pairs, the next step involves processing this data to extract meaningful information. This is where the mapper and combiner come into play.

Mapper and Combiner

Once data is split and formatted, it enters the mapper phase. The mapper plays a critical role in processing and transforming input data before passing it to the next stage.A combiner, an optional step, optimizes performance by reducing data locally, minimizing the volume of intermediate data that needs to be transferred to reducers.

Below is how this stage functions:

• The mapper processes each key-value pair and transforms it: It extracts meaningful information by applying logic on input data. For example, in a word count program, the mapper receives lines of text, breaks them into words, and assigns each word a count of one. If the input is "Hadoop is powerful. Hadoop is scalable.", the mapper emits key-value pairs like ("Hadoop", 1), ("is", 1), ("powerful", 1), and so on.
• The combiner performs local aggregation to reduce intermediate data: Since mappers generate large amounts of intermediate data, the combiner helps by merging values locally before sending them to reducers. For example, in the same word count program, if the mapper processes 1,000 occurrences of "Hadoop" across different InputSplits, the combiner sums up word counts locally, reducing multiple ("Hadoop", 1) pairs to a single ("Hadoop", 1000) before the shuffle phase. This minimizes data transfer and speeds up processing.

Also Read: Top 10 Hadoop Commands [With Usages]

Now that the data has been processed and locally aggregated, it needs to be efficiently distributed to ensure balanced workload distribution. The partitioner and shuffle step handle this crucial process. Let’s take a close look in the next section. 

Partitioner and Shuffle

Once the mapper and optional combiner complete processing, data must be organized efficiently before reaching the reducer. The partitioner and shuffle phase ensures a smooth and evenly distributed data flow in Hadoop.

• The partitioner assigns key-value pairs to reducers based on keys – It ensures that related data reaches the same reducer. For example, in a word count job, words starting with 'A' may go to Reducer 1, while words starting with 'B' go to Reducer 2.
• Shuffling transfers and sorts intermediate data before reduction – After partitioning, data is shuffled across nodes, sorting it by key. This ensures that all values associated with the same key are sent to the same reducer. It prevents duplicate processing by grouping related data before reduction.  For instance, if "Hadoop" appears in multiple splits, all occurrences are grouped together before reaching the reducer.

With data properly partitioned and transferred to reducers, the final stage focuses on aggregation and output formatting. This ensures that the results are structured and stored appropriately for further analysis.

Reducer and OutputFormat

The reducer aggregates and finalizes data, producing the final output. The OutputFormat ensures that processed data is stored in the required format for further use, offering flexibility for integration with various systems.

Below is how this stage works:

• The reducer processes grouped key-value pairs and applies aggregation logic: It takes data from multiple mappers, processes it, and generates the final output. For example, in a word count program, if the word "Hadoop" appears 1,500 times across different splits, the reducer sums up all occurrences and outputs "Hadoop: 1500".
• OutputFormat determines how final data is stored and structured: Hadoop provides built-in options like TextOutputFormat, SequenceFileOutputFormat, and AvroOutputFormat, allowing data to be stored in various formats. Additionally, custom OutputFormats can be defined for specific needs, such as structured storage in databases or exporting results in JSON for log processing jobs. This flexibility allows seamless integration with data lakes, BI tools, and analytics platforms.

With the entire data flow in Hadoop completed, it’s important to understand the essential components that power this ecosystem. These building blocks ensure efficient data storage, processing, and retrieval.

What are the Building Blocks of Hadoop?

Hadoop’s ecosystem is built on several essential components that work together to enable efficient data storage, processing, and management. These building blocks ensure that large datasets are processed in a distributed manner, allowing organizations to handle massive volumes of structured and unstructured data.  

Let’s explore the building blocks in detail. 

1. HDFS (The Storage Layer)

As the name suggests, Hadoop Distributed File System is the storage layer of Hadoop and is responsible for storing the data in a distributed environment (master and slave configuration). It splits the data into several blocks of data and stores them across different data nodes. These data blocks are also replicated across different data nodes to prevent loss of data when one of the nodes goes down.

It has two main processes running for processing of the data: –

a. NameNode

 It is running on the master machine. It saves the locations of all the files stored in the file system and tracks where the data resides across the cluster i.e. it stores the metadata of the files. When the client applications want to make certain operations on the data, it interacts with the NameNode. When the NameNode receives the request, it responds by returning a list of Data Node servers where the required data resides.

b. DataNode

This process runs on every slave machine. One of its functionalities is to store each HDFS data block in a separate file in its local file system. In other words, it contains the actual data in form of blocks. It sends heartbeat signals periodically and waits for the request from the NameNode to access the data.

2. MapReduce (The processing layer)

It is a programming technique based on Java that is used on top of the Hadoop framework for faster processing of huge quantities of data. It processes this huge data in a distributed environment using many Data Nodes which enables parallel processing and faster execution of operations in a fault-tolerant way.

A MapReduce job splits the data set into multiple chunks of data which are further converted into key-value pairs in order to be processed by the mappers. The raw format of the data may not be suitable for processing. Thus, the input data compatible with the map phase is generated using the InputSplit function and RecordReader.

InputSplit is the logical representation of the data which is to be processed by an individual mapper. RecordReader converts these splits into records which take the form of key-value pairs. It basically converts the byte-oriented representation of the input into a record-oriented representation.

These records are then fed to the mappers for further processing the data. MapReduce jobs primarily consist of three phases namely the Map phase, the Shuffle phase, and the Reduce phase.

a. Map Phase

It is the first phase in the processing of the data. The main task in the map phase is to process each input from the RecordReader and convert it into intermediate tuples (key-value pairs). This intermediate output is stored in the local disk by the mappers.

The values of these key-value pairs can differ from the ones received as input from the RecordReader. The map phase can also contain combiners which are also called as local reducers. They perform aggregations on the data but only within the scope of one mapper.

As the computations are performed across different data nodes, it is essential that all the values associated with the same key are merged together into one reducer. This task is performed by the partitioner. It performs a hash function over these key-value pairs to merge them together.

It also ensures that all the tasks are partitioned evenly to the reducers. Partitioners generally come into the picture when we are working with more than one reducer.

b. Shuffle and Sort Phase

This phase transfers the intermediate output obtained from the mappers to the reducers. This process is called shuffling. The output from the mappers is also sorted before transferring it to the reducers. The sorting is done on the basis of the keys in the key-value pairs. It helps the reducers to perform the computations on the data even before the entire data is received and eventually helps in reducing the time required for computations.

As the keys are sorted, whenever the reducer gets a different key as the input it starts to perform the reduced tasks on the previously received data.

c. Reduce Phase

The output of the map phase serves as an input to the reduce phase. It takes these key-value pairs and applies the reduce function on them to produce the desired result. The keys and the values associated with the key are passed on to the reduce function to perform certain operations.

We can filter the data or combine it to obtain the aggregated output. Post the execution of the reduce function, it can create zero or more key-value pairs. This result is written back in the Hadoop Distributed File System. 

3. YARN (The Management Layer)

Yet Another Resource Navigator is the resource managing component of Hadoop. There are background processes running at each node (Node Manager on the slave machines and Resource Manager on the master node) that communicate with each other for the allocation of resources. The Resource Manager is the centrepiece of the YARN layer which manages resources among all the applications and passes on the requests to the Node Manager.

The Node Manager monitors the resource utilization like memory, CPU, and disk of the machine and conveys the same to the Resource Manager. It is installed on every Data Node and is responsible for executing the tasks on the Data Nodes.

From distributed storage to parallel data processing, each component plays a key role in maintaining a smooth data flow in Hadoop. The next section explores the key benefits of this data flow and its real-world applications.

Key Benefits and Use Cases of Data Flow in Hadoop

Hadoop enables scalable, cost-effective, and fault-tolerant data processing across distributed environments. Below are some of the major benefits and practical use cases of data processing in Hadoop.

• Scalability for large-scale data processing – Hadoop’s distributed architecture allows you to scale storage and processing power horizontally. For example, companies handling petabytes of data, such as Facebook and Twitter, use Hadoop clusters to process user interactions, ad targeting, and recommendation algorithms.
• Cost-effective storage and processing – Traditional relational databases can be expensive for storing and processing big data. Hadoop provides a low-cost alternative by using commodity hardware, making it ideal for businesses dealing with vast data volumes. A retail giant like Walmart, for instance, uses Hadoop to analyze consumer purchasing behavior at scale.
• Fault tolerance and high availability – Hadoop replicates data across multiple nodes, ensuring no single point of failure. If one node fails, another automatically takes over, maintaining uninterrupted processing. This feature is critical for financial institutions that cannot afford data loss or downtime.
• Efficient handling of structured and unstructured data – Unlike traditional databases that struggle with unstructured data, Hadoop seamlessly processes images, videos, social media posts, and sensor data. Companies like Netflix rely on Hadoop to analyze streaming preferences and enhance user recommendations.
• Real-time and batch data processing – Hadoop supports both real-time and batch processing through its ecosystem tools like Apache Spark and MapReduce. This flexibility is essential for cybersecurity firms detecting fraudulent transactions in real-time while also analyzing historical trends.
• Optimized data flow for analytics and machine learning – With frameworks like Apache Mahout and Spark MLlib, Hadoop simplifies large-scale machine learning applications. Organizations use this capability to build predictive models for healthcare, finance, and e-commerce.
• Real-time fraud detection in financial services – Hadoop enables banks and financial institutions to detect fraudulent transactions in real time by analyzing large volumes of customer transaction data. PayPal and Mastercard use Hadoop to identify anomalies and trigger fraud alerts within milliseconds.
• IoT data processing in smart cities – Hadoop helps smart cities process vast IoT sensor data from traffic systems, surveillance cameras, and environmental monitors. Barcelona and Singapore use Hadoop-based analytics to optimize urban planning, reduce congestion, and improve public safety.

Also Read: Top 10 Hadoop Tools to Make Your Big Data Journey Easy

With its efficient data flow, Hadoop is a cornerstone for businesses aiming to manage vast amounts of information. Understanding how to work with Hadoop clusters can significantly improve your ability to handle big data challenges. The next section explores how upGrad can help you gain expertise in this field.

How upGrad Can Help You Excel Hadoop Clusters in Big Data?

upGrad is a leading online learning platform with over 10 million learners, 200+ courses, and 1400+ hiring partners. Whether you want to master Hadoop, advance your data engineering skills, or explore big data technologies, upGrad provides industry-relevant courses designed for career growth. 

Below are some top courses that can help you gain expertise in data flow in Hadoop and big data processing.

• Master’s Degree in Artificial Intelligence and Data Science
• Post Graduate Diploma in Data Science & AI
• Post Graduate Certificate in Data Science and AI
• Free Data Science Course From upGrad

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