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Published on 10 Jun 2024

Understating Big Data Processing: Ultimate Guide of 2024



Updated: 17 Jun 2024

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The term "big data" refers to the massive volumes of structured, semi-structured, and unstructured data that are generated and collected from various sources at an unprecedented speed and scale. The main characteristics that define big data are velocity, volume, value, variety and veracity, what we call the 5 Vs of Big Data. In this article, we will explore all you need to know about Big Data Processing, from types and sources to using GPUs for acceleration. Let’s get started!

Sources of Big Data

To understand what big data is, we would first need to identify where the data originates from.  It could be from numerous sources, including:

  • Social Media: We all know that platforms like Facebook, X, Instagram and LinkedIn generate vast amounts of user-generated data such as posts, comments, likes and shares. So, a major amount of data is produced from social media. 
  • Internet of Things (IoT): The proliferation of connected devices and sensors in various industries, from manufacturing and healthcare to smart cities and transportation, generates continuous streams of data.
  • Transactional Systems: Businesses across sectors like retail, finance, and e-commerce also generate large volumes of transactional data from sales, purchases and financial transactions.
  • Multimedia Sources: Digital media, including images, videos and audio files, contribute significantly to the growth of big data, especially with the rise of platforms like YouTube, Netflix and Spotify that we use on a regular basis.
  • Web Logs and Clickstream Data: Web servers and applications generate vast amounts of log data capturing user interactions, browsing behavior and clickstreams. Again, something that we interact with on a daily basis. 
  • Scientific and Research Data: Experiments, simulations and research in fields like genomics, astronomy and particle physics generate massive datasets that require processing and analysis.

Types of Big Data

So, Big data can be categorised into three main types based on its structure:

  1. Structured Data: This type of data follows a predefined schema or format, making it easily organised and stored in databases and spreadsheets.
  2. Semi-structured Data: This data has some structure but doesn't conform to a strict schema. It can be formatted using markup languages like XML or JSON.
  3. Unstructured Data: This data lacks a predefined structure or schema, making it more challenging to process and analyse. 

Challenges in Big Data Processing 

While big data presents numerous opportunities, processing and analysing these massive and diverse datasets comes with several challenges:

  • Data Quality: This has to be the number one challenge faced in big data processing because of the inaccuracy present in the data from multiple sources. You cannot blindly rely on everything.
  • Scalability: Traditional data processing systems and architectures may not be capable of handling the sheer volume and velocity of big data. You need to ensure your systems can scale to meet the demands.
  • Real-time Processing: Many applications require real-time or near real-time processing of data streams to enable immediate decision-making and responsive actions. Trust me, organisations cannot afford to wait—decisions need to be made instantly.
  • Data Security: With the increasing volume and sensitivity of data, ensuring data security, privacy and compliance with regulations is a major concern. 

Also Read: Top 5 Challenges in Artificial Intelligence

Role of GPUs in Big Data Processing

GPUs help in accelerating big data processing and overcoming the above mentioned challenges associated with handling large volumes of data. Here’s how GPUs benefit data-intensive workloads:

  • GPUs can execute thousands of threads in parallel, making them highly efficient for operations that can be parallelised, such as matrix operations, data transformations, and machine learning algorithms. This parallel processing capability allows GPUs to process large datasets faster than traditional CPU-based systems.
  • GPUs can process large volumes of data at high speeds, significantly reducing processing times compared to traditional CPU-based systems. This is particularly beneficial for applications that require real-time or near real-time processing of data streams.
  • GPUs can perform more computations per watt, making them more energy-efficient for data-intensive workloads compared to CPUs. This translates to lower operational costs and a reduced environmental footprint.
  • GPU-accelerated solutions can be scaled by adding more GPUs or nodes to a cluster for distributed processing of large datasets across multiple resources.

Also Read: The Role of AI in Cloud Computing

Architecture of Big Data Processing 

The integration of GPUs into big data processing architectures and frameworks lead to significant performance improvements and cost savings. The two popular architectural patterns for big data processing are the Lambda and Kappa architectures:

  • Lambda Architecture: This architecture separates data processing into three layers: batch processing, speed (real-time) processing, and a serving layer that combines the results from the other two layers. The batch processing layer handles large volumes of historical data, typically using tools like Apache Hadoop or Apache Spark. The speed layer processes real-time data streams using stream processing engines like Apache Kafka or Apache Flink. The serving layer indexes the output from the batch and speed layers, providing a unified view of the data for querying and analysis.
  • Kappa Architecture: The Kappa architecture simplifies the Lambda architecture by relying solely on stream processing, treating both real-time and historical data as immutable, append-only streams. In this architecture, all data is processed as streams, eliminating the need for separate batch and speed layers. 

Big Data Processing Technologies

Several big data processing technologies handle the large datasets including:

  • Apache Hadoop: A distributed computing framework for storing and processing large datasets across clusters of commodity hardware.
  • Apache Spark: An open-source, distributed computing framework for processing large datasets with in-memory processing capabilities.
  • RAPIDS: A suite of open-source libraries and APIs built on CUDA for executing end-to-end data science and analytics pipelines entirely on GPUs.
  • Dask: A flexible parallel computing library for scaling out analytics across multiple GPUs and CPUs.

Batch Processing vs Stream Processing

Big data processing can be broadly categorised into two types: batch processing and stream processing.

  1. Batch Processing: It involves processing large datasets in batches or chunks, typically on a scheduled or periodic basis. This approach is suitable for workloads that don't require real-time processing, such as historical data analysis and reporting.
  2. Stream Processing: It involves processing data as it arrives in real-time for immediate analysis and decision-making. This approach is crucial for applications that require real-time insights, such as fraud detection, IoT data processing, and social media monitoring.

Also Read: Static vs. Continuous Batching for Large Language Models

Data Ingestion and ETL

Data ingestion is the process of collecting and importing data from various sources into a centralised system or data lake for further processing and analysis. The ETL (Extract, Transform, Load) process is a crucial step in data ingestion, where data is extracted from various sources, transformed into a common format, and loaded into a data storage system. 

Data Storage and Management

Distributed file systems and NoSQL databases are commonly used for storing and managing large volumes of data in big data environments. RAPIDS and Dask can integrate with these storage systems for efficient data partitioning, replication and parallel processing across multiple GPUs and nodes.

  • Distributed File Systems: Platforms like Apache Hadoop Distributed File System (HDFS) and Amason S3 are designed to store and manage large datasets across distributed clusters of commodity hardware.
  • NoSQL Databases: Non-relational databases like Apache Cassandra, MongoDB, and HBase are optimised for handling large volumes of unstructured and semi-structured data with high scalability and availability.

Data Processing Techniques

Several data processing techniques have been developed to handle big data workloads:

  • MapReduce: A programming model for processing and generating large datasets with a parallel, distributed algorithm on a cluster.
  • Spark RDDs: Resilient Distributed Datasets (RDDs) in Apache Spark are fault-tolerant, immutable collections of records that can be processed in parallel across a cluster.

Data Analysis and Visualisation

Once data has been processed, it needs to be analysed and visualised to extract insights and make informed decisions. Frameworks like RAPIDS and Dask integrate with GPU-accelerated visualisation libraries like Datashader and CuPy. This enables faster rendering and visualisation of large datasets.

Use Cases of Big Data Processing 

GPU-accelerated big data processing has numerous real-world applications across various industries:

  • Finance: Real-time fraud detection, risk analysis, algorithmic trading, and portfolio optimisation.
  • Healthcare: Genomic data analysis, medical imaging, predictive modeling for disease diagnosis and treatment, and patient data analytics.
  • E-commerce: Personalised recommendations, customer behavior analysis, supply chain optimisation, and demand forecasting.
  • Internet of Things (IoT): Real-time processing and analysis of sensor data from connected devices in various domains like manufacturing, transportation, and smart cities.
  • Scientific Research: Simulations, modeling, and analysis of large-scale scientific data in fields like genomics, particle physics, and astronomy.
  • Cybersecurity: Real-time threat detection, network traffic analysis, and security log analysis.
  • Media and Entertainment: Content recommendation systems, user behavior analysis, and video/audio processing.


We all know that the volume and complexity of data will only grow in the coming years, so the need for efficient and scalable solutions becomes imperative. Hence, leveraging the massively parallel processing capabilities of GPUs, organisations can process and analyse large datasets at a massive speed. From accelerating machine learning and deep learning models to streamlining data processing pipelines and real-time analytics, GPU acceleration is leading the big data ecosystem.

At Hyperstack, we understand your concerns regarding big data processing. So, we offer powerful solutions including the NVIDIA A100, NVIDIA RTX A4000 and NVIDIA A6000, specifically designed for big data workloads. With our solutions, you can excel in data processing time for big data analytics, which is vital in finance, healthcare, and scientific research. 

Ready to get started? Sign up for a free Hyperstack account today and experience the big data acceleration!


What are the 5 Vs of Big Data?

The 5 Vs of Big Data are Velocity, Volume, Value, Variety and Veracity. 

What are the main sources of Big Data?

Big data originates from various sources, including social media platforms, Internet of Things (IoT) devices, transactional systems, multimedia sources, web logs, and scientific research data.

How do GPUs improve big data processing?

GPUs accelerate big data processing by executing thousands of threads in parallel, enabling faster data processing, real-time analytics, and greater energy efficiency compared to traditional CPU-based systems.

What are some common big data processing frameworks?

Popular big data processing frameworks include Apache Hadoop, Apache Spark, RAPIDS, and Dask. These tools help manage and analyse large datasets efficiently.

What is the best GPU for big data workloads?

For data science workloads, we recommend using the NVIDIA A100, NVIDIA RTX A4000, or NVIDIA A6000 GPUs. These GPUs provide excellent performance, scalability, and efficiency for handling large-scale data processing tasks.

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