Data Processing in Hadoop Ecosystem: Complete Data Flow Explained

Data processing in Hadoop is preferred for its ability to handle vast volumes of structured and unstructured data efficiently across distributed systems. Unlike traditional databases, data processing in Hadoop follows a data-locality principle, bringing computation to data, which makes it ideal for handling logs, videos, images, and more at scale. 

For instance, companies like Facebook use Hadoop to process petabytes of user-generated data daily, enabling real-time insights into user behavior and system performance. This showcases Hadoop’s ability to power data-driven decisions at massive scale.

In this blog, you'll explore how Hadoop processes data, key components of its ecosystem, such as HDFS and MapReduce, and the step-by-step workflow that powers scalable big data solutions.

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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 of data processing in Hadoop is essential to optimizing performance and managing large-scale data effectively.

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Now, let’s understand each of the steps involved in data processing in Hadoop:

1. 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.

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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.

2. 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.

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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. 

3. 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.

Here’s how it works:

• 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.

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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.

4. 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.

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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.  

Now,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 in Hadoop, 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.

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Also Read: What is the Future of Hadoop? Top Trends to Watch

Next, let’s look at the key benefits and drawbacks of data processing in Hadoop.

Key Benefits and Limitations of Data Flow in Hadoop

Big data processing in Hadoop offers scalable, cost-effective, and fault-tolerant solutions for managing vast and diverse datasets. Its flexible architecture supports both real-time and batch processing, enabling industries from retail to finance to extract meaningful insights. 

However, effective data processing in Hadoop requires overcoming challenges such as complex deployment, steep learning curves, and substantial resource demands. 

The following table summarizes the key benefits and limitations of data processing in Hadoop:

Also Read: Apache Spark vs Hadoop: Key Differences & Use Cases

Now, let’s look at the common use cases of data processing in Hadoop.

Practical Applications of Data Flow in Hadoop

Hadoop is preferred in data-intensive environments where speed, scalability, and versatility are essential for deriving actionable insights from massive, diverse datasets. Its ability to handle both real-time and batch data flows makes it ideal for sectors like finance, retail, healthcare, and smart infrastructure, where timely decision-making and predictive analytics drive competitive advantage. 

Here are the common use cases:

• Real-Time Fraud Detection in Financial Services: Banks and fintech firms like PayPal and Mastercard use Hadoop to process transaction logs in real time, identifying anomalies and issuing fraud alerts within milliseconds.
• Consumer Behavior Analysis in Retail: Retail giants use Hadoop to analyze millions of purchase records, helping refine product recommendations, inventory planning, and targeted marketing.
• Streaming Data Analytics in Entertainment: Platforms like Netflix rely on Hadoop to assess user streaming habits and content preferences, thereby improving user experience and personalization.
• IoT Data Processing in Smart Cities: Smart cities like Barcelona and Singapore use Hadoop to analyze sensor data from traffic signals, weather systems, and surveillance cameras for urban planning and public safety.
• Healthcare Predictive Modeling: Medical institutions use Hadoop to process electronic health records (EHRs) and genomic data, aiding in early diagnosis and personalized treatment plans.
• Cybersecurity Threat Detection: Security companies leverage Hadoop to monitor network traffic, detect patterns of cyberattacks, and respond to breaches by analyzing historical and real-time data.
• Supply Chain Optimization: Manufacturing and logistics companies analyze large-scale supply chain data with Hadoop to improve demand forecasting, route planning, and vendor performance.

Also Read: 55+ Most Asked Big Data Interview Questions and Answers [ANSWERED + CODE]

Next, let’s look at how upGrad can help you in learning data processing in Hadoop.

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

As businesses scale their data operations, Hadoop remains a backbone for processing massive datasets efficiently. Professionals should gain hands-on expertise in HDFS, MapReduce, YARN, and Hive, focusing on real-world tasks like building data pipelines, optimizing job performance, and integrating with tools like Spark and HBase. 

Mastery of Hadoop’s ecosystem is crucial for roles in data engineering, analytics, and infrastructure design. To achieve this, upGrad offers industry-aligned programs that equip learners with practical Hadoop skills, real-world projects, and expert mentorship. 

Along with the courses covered above, here are some additional courses to complement your learning journey:

• Introduction to Data Analysis using Excel
• Analyzing Patterns in Data and Storytelling
• Basics of Inferential Statistics
• Introduction to Tableau
• Case Study using Tableau, Python and SQL

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References:
https://medium.com/@pratikshinde2210/understanding-how-hadoop-utilizes-parallelism-to-address-the-velocity-problem-83c7a1809ed3