With the staggering volume of data generated daily, businesses prioritize efficient data analytics techniques to harness its true potential. Big data frameworks are highly capable of processing enormous datasets and uncovering hidden insights. As per recent reports, between 2024 and 2029, the big data revenue value will increase by threefold.
Therefore, it is crucial to train your workforce about the best big data frameworks and enhance their data efficiency skills. Education Nest offers the best corporate training programs that cover the fundamentals and advanced topics of big data analytics.
Hadoop and Spark are the most commonly used technologies used for large data architectures. Their efficient data handling, processing, and analytical capabilities offer compelling benefits. Therefore, these big data tools are extensively used by businesses of all sizes and scales.
So, what are Hadoop and Spark? What is the difference between Hadoop and Spark?
Read on to find out.
What is Hadoop?
Hadoop (or High Availability Distributed Object Oriented Platform) is a Java-based framework that can store and process massive complex data sets parallely. It uses a simple programming model and has an extensible ecosystem that helps with efficient big data processing.
Hadoop Architecture
The Hadoop architecture includes four major modules:
HDFS (Hadoop Distributed File System): It is the primary Hadoop component that helps in distributed data storage. As information is stored across multiple machines, it becomes easier to access and process data.
YARN (Yet Another Resource Negotiator): The YARN framework helps optimize computing resources management and job scheduling. Thus, it ensures efficient cluster utilization.
MapReduce: It is a large-scale data processing model that speeds up parallel data computation. It has two major phases- Map and Reduce. It divides large and complex information sets into smaller subsets for better processing and then aggregates the output for final results.
Hadoop Uses
The common use cases of Hadoop include Batch processing, data warehousing, marketing analytics, risk analysis and management models, model training operations, and more.
What is Spark
Apache Spark is a simple, scalable, and unified big data platform that helps with fast iterative data processing. It is a multi-language engine that uses AI and ML algorithms for faster data processing. Thus, it is a highly efficient and versatile big data platform.
Spark Architecture
The key components of the Spark architecture are:
Spark Core: The core concept of the Spark framework is Resilient Distributed Datasets(RDDs). The Spark core is the foundational structure that holds the APIs that define RDDs, task scheduling, memory management, and other essential I/O functionalities.
Spark SQL: This Spark component helps with structured data processing and access to data sources like Hive, JSON, JDBC, and others. Spark SQL also integrates different frameworks like AI or ML with the Spark ecosystem.
Spark Streaming: This extension helps ingest from different sources, process using complex algorithms, and push out the processed information into live dashboards, databases, or file systems. It is different from traditional streaming systems because of its unified programming model and scalable fault-tolerant processing system.
Spark Uses
Spark use cases include complex data analysis and transformations, data engineering operations, machine learning integrations, interactive computations, and others.
Hadoop vs. Spark: What’s the Difference?
Now that you understand the fundamentals of Hadoop and Spark, here are some key differences between them:
Architecture
Hadoop and Spark architecture are quite different. While Hadoop is batch-oriented only, Spark has core APIs and specialized libraries for real-time processing as well. Spark has a richer ecosystem, too. It can seamlessly integrate with other tools and libraries.
Performance
Hadoop stores information in clusters and relies on disk I/O, which makes it difficult for iterative algorithms. But Spark is faster and more efficient as it supports in-memory processing. It is also more flexible and efficient in terms of scalability. Spark is more user-friendly, too, as it supports multiple languages.
Cost-effectiveness
If you have budget concerns, Hadoop is a better option. It uses disk storage, which is cheaper than the RAM used by Spark. Also, for cloud operations, Hadoop uses regular instances. But, Spark needs memory-optimized instances that spike up the cost.
Hadoop or Spark: What to Choose
Hadoop and Spark are both great technologies for big data frameworks. Therefore, you should first decide your goals, operational requirements, infrastructure capabilities, and other major aspects. Then, you can finalize what will suit your needs.
Besides, you can use them together for different tasks. As a result, you get the best of each tool and produce the best business outcomes. So, make well-informed choices to unlock the true potential of big data.
Also, to harness the true business benefits of Hadoop and Spark, equip your workforce with hands-on learning for better clarity about these concepts. Invest in tailored corporate learning solutions from Education Nest that can be curated as per your specific requirements and deliver more value.
Explore our big data courses, designed comprehensively as per the latest industry requirements. To know more in detail, reach out to us today.
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