Hadoop : Concept of Parallelism

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Lets begin with brief introduction about me…..

Aloha, This is Ankit Shukla and I am working as SDET Automation/functional Engineer in Regulatory Reporting Domain. In this article we will discuss about How Hadoop uses parallelism and how its useful for banking industry.

Before deep dive into concept of parallelism ,lets talk about need of Hadoop and why we should go for it ?

As we are in the era called Digital Era ,where keeping data and using data efficiently is very generic need . In the World of data we have multiple kinds of data available ,it can be video, image, streaming data, etc.

What is Hadoop Ecosystem and why we need it while we have other storage solutions such as Hard disk and pen drive ?

The Hadoop Ecosystem is a set of open-source tools and frameworks designed to facilitate the storage, processing, and analysis of large volumes of data on distributed clusters. It was created to address the challenges posed by big data — datasets that are too large, too complex, or too rapidly changing for traditional data processing systems to handle efficiently.

Key Components of the Hadoop Ecosystem:

1. Hadoop Distributed File System (HDFS): A distributed storage system designed to store vast amounts of data across a cluster of machines.
2. MapReduce: A programming model and processing engine for parallel and distributed data processing on large data sets.
3. YARN (Yet Another Resource Negotiator): A resource management layer responsible for managing and scheduling resources in a Hadoop cluster.
4. Apache Hive: A data warehousing and SQL-like query language system for Hadoop that facilitates easy data summarization, ad-hoc querying, and analysis.
5. Apache Pig: A high-level platform and scripting language built on top of Hadoop for processing and analyzing large datasets.
6. Apache HBase: A distributed, scalable, and NoSQL database that provides real-time read and write access to large datasets.
7. Apache Spark: A fast and general-purpose cluster-computing framework that provides in-memory data processing for large-scale data processing and analytics.
8. Apache Kafka: A distributed event streaming platform used for building real-time data pipelines and streaming applications.
9. Apache Sqoop: A tool for efficiently transferring bulk data between Hadoop and structured data stores such as relational databases.
10. Apache Flume: A distributed, reliable, and available service for efficiently collecting, aggregating, and moving large amounts of log data.
11. Apache Oozie: A workflow scheduler for managing Hadoop jobs, allowing users to specify a series of jobs and their dependencies.

Why the Hadoop Ecosystem?

While traditional storage solutions like hard disks and pen drives are suitable for everyday data storage needs, Hadoop is important as one of the primary tools to store and process huge amounts of data quickly. It does this by using a distributed computing model which enables the fast processing of data that can be rapidly scaled by adding computing nodes. . Here’s why the Hadoop Ecosystem is necessary:

1. Scalability: Hadoop is designed to scale horizontally, meaning it can handle growing data volumes by adding more nodes to the cluster. Traditional storage solutions may not provide the same level of scalability.
2. Distributed Computing: The Hadoop Ecosystem enables distributed computing, allowing the processing of large datasets across multiple nodes simultaneously. This distributed approach enhances performance and processing speed.
3. Fault Tolerance: Hadoop’s distributed nature ensures fault tolerance. If a node fails, the processing can continue on other nodes without losing data or compromising the entire system.
4. Cost-Effectiveness: Hadoop uses commodity hardware, making it a cost-effective solution for storing and processing massive amounts of data. Traditional storage solutions might involve expensive, high-end storage systems.
5. Parallel Processing: The Hadoop Ecosystem, especially MapReduce, allows for parallel processing of data, improving the speed of data analysis. Traditional storage solutions may not have built-in mechanisms for efficient parallel processing.
6. Diverse Data Processing: Hadoop can handle diverse data types, including structured, semi-structured, and unstructured data. Traditional storage solutions might not be as versatile in managing various data formats.
7. Data Redundancy and Replication: Hadoop’s HDFS stores data redundantly across the cluster, ensuring data reliability and availability. This redundancy and replication are crucial for handling hardware failures or data corruption.
8. Real-Time Processing: Components like Apache Spark and Apache Kafka in the Hadoop Ecosystem provide capabilities for real-time data processing, which may be challenging with traditional storage solutions.

Concept of Parallelism:

• Hadoop is designed to process large datasets by distributing them across multiple nodes in a cluster. This is achieved through the concept of parallelism, where data is divided into smaller chunks, known as data blocks, and processed concurrently on different nodes.
• The parallel processing capability of Hadoop enhances the overall speed and efficiency of data processing tasks.
• Proof: The MapReduce programming model, which is a core component of Hadoop, divides data processing into two phases — the Map phase and the Reduce phase. The Map phase processes data in parallel across multiple nodes, enabling the simultaneous execution of tasks on different parts of the dataset. This parallelism is a key factor in addressing the Velocity problem associated with the high speed at which data is generated.

2. Upload Split Data:

• In Hadoop’s distributed file system, HDFS (Hadoop Distributed File System), large files are split into smaller blocks (typically 128 MB or 256 MB in size). These blocks are then distributed across the nodes in the Hadoop cluster. When it comes to uploading data, this split data is distributed in parallel, allowing for efficient and speedy data transfers.
• Proof: HDFS divides large files into fixed-size blocks, and these blocks are distributed across the cluster. When data is uploaded or written to HDFS, it is distributed across multiple nodes simultaneously, taking advantage of parallelism. This parallel upload process is crucial for handling large volumes of data efficiently.

3. Fulfilling Velocity Problem:

• The Velocity problem in the context of big data refers to the rapid generation and accumulation of data. Hadoop addresses this challenge by leveraging parallel processing and distributed storage, allowing it to efficiently handle and process vast amounts of data at high speeds.
• Proof: Hadoop’s parallel processing capabilities enable it to distribute the computational load across multiple nodes, allowing for the concurrent processing of data.
• This not only accelerates data processing but also ensures that the system can keep up with the high velocity at which data is being generated, fulfilling the requirements of the Velocity dimension in big data.

Scenario: Regulatory Reporting in the Banking Industry

Challenge:

A large multinational bank is faced with the complex task of regulatory reporting to comply with various financial regulations imposed by national and international authorities.

These regulations require the bank to submit accurate and timely reports on a range of financial activities, including risk exposure, capital adequacy, liquidity, and transaction details.

Hadoop Implementation:

To address the challenges associated with regulatory reporting, the bank decides to implement Hadoop as part of its data processing and reporting infrastructure.

The goal is to leverage Hadoop’s capabilities to efficiently handle the large volumes of financial data, ensure data accuracy, and meet regulatory deadlines.

Example Steps:

1. Data Integration:

• The bank ingests diverse data sets from multiple sources, including transactional databases, trading platforms, and customer accounts, into the Hadoop ecosystem.
• Hadoop’s ability to handle structured and unstructured data is crucial, as regulatory reporting often involves a variety of data formats.

Data Quality and Validation:

• Hadoop processes and validates the data against predefined rules and regulations. This step ensures that the data is accurate, complete, and conforms to regulatory standards. Hadoop’s parallel processing capabilities enable the bank to perform extensive data quality checks efficiently.

Data Transformation:

The data is transformed and aggregated as required for specific regulatory reports.

Hadoop’s MapReduce or Spark-based processing allows the bank to perform complex computations, aggregations, and transformations on large datasets, ensuring the generation of accurate and comprehensive reports.

Parallel Processing for Speed:

Hadoop’s distributed architecture allows the bank to process data in parallel across a cluster of machines.

This parallel processing significantly speeds up the generation of regulatory reports, enabling the bank to meet tight reporting deadlines imposed by regulators.

Scalability for Growing Data Volumes:

As the volume of financial transactions and regulatory requirements increases, the bank can scale its Hadoop cluster horizontally by adding more nodes.

This scalability ensures that the reporting system can handle growing data volumes without sacrificing performance.

Data Lineage and Auditing:

Hadoop’s capabilities for data lineage and auditing provide transparency into the entire data processing pipeline.

This is crucial for regulatory compliance, as it allows the bank to trace the origin of data and demonstrate the accuracy and integrity of the reported information.

Benefits:

• Compliance Assurance: Hadoop’s ability to process and validate large volumes of data ensures that the bank complies with regulatory requirements, avoiding penalties and legal repercussions.
• Efficiency: Parallel processing and scalability enable the bank to generate regulatory reports efficiently, even as data volumes increase.
• Flexibility: Hadoop’s flexibility allows the bank to adapt its reporting processes to evolving regulatory frameworks without significant overhauls of the existing infrastructure.
• Cost-Effective Scaling: By leveraging commodity hardware and open-source Hadoop technologies, the bank can scale its infrastructure cost-effectively to meet the demands of growing regulatory requirements.

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