The respective architectures of Hadoop and Spark, how these big data frameworks compare in multiple contexts and scenarios that fit best with each solution.
Hadoop and Spark, both developed by the Apache Software Foundation, are widely used open-source frameworks for big data architectures. Each framework contains an extensive ecosystem of open-source technologies that prepare, process, manage and analyze big data sets.
What is Apache Hadoop?
Apache Hadoop is an open-source software utility that allows users to manage big data sets (from gigabytes to petabytes) by enabling a network of computers (or “nodes”) to solve vast and intricate data problems. It is a highly scalable, cost-effective solution that stores and processes structured, semi-structured and unstructured data (e.g., Internet clickstream records, web server logs, IoT sensor data, etc.).
Benefits of the Hadoop framework include the following:
- Data protection amid a hardware failure
- Vast scalability from a single server to thousands of machines
- Real-time analytics for historical analyses and decision-making processes
What is Apache Spark?
Apache Spark — which is also open source — is a data processing engine for big data sets. Like Hadoop, Spark splits up large tasks across different nodes. However, it tends to perform faster than Hadoop and it uses random access memory (RAM) to cache and process data instead of a file system. This enables Spark to handle use cases that Hadoop cannot.
Benefits of the Spark framework include the following:
- A unified engine that supports SQL queries, streaming data, machine learning (ML) and graph processing
- Can be 100x faster than Hadoop for smaller workloads via in-memory processing, disk data storage, etc.
- APIs designed for ease of use when manipulating semi-structured data and transforming data
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