Apache Hadoop vs Apache Spark

Architecture:

- Hadoop: Hadoop's architecture is centered around its two main components - the Hadoop Distributed File System (HDFS) and the MapReduce programming model. HDFS is a distributed file system designed to hold very large amounts of data (terabytes or even petabytes), and provide high-throughput access to this information. Files are stored in a redundant fashion across multiple machines to ensure their durability to failure and high availability to very parallel applications. MapReduce is a computational model that allows for massive scalability across hundreds or thousands of servers in a Hadoop cluster. It provides a way to process these large datasets in a distributed and parallel manner.

- Spark: Spark's architecture is based on the concept of distributed task dispatching and fault recovery. It uses a master/worker architecture where the driver program runs the main function and creates a SparkContext. The SparkContext can then be used to create Resilient Distributed Datasets (RDDs) on the worker nodes. RDDs are a fundamental data structure of Spark which are an immutable distributed collection of objects. Each dataset in RDD is divided into logical partitions, which may be computed on different nodes of the cluster.

Processing Speed:

- Hadoop: Hadoop uses batch processing — a non-interactive method where all the data is collected and processed in one batch. This means that the data is read from the disk, processed, and then written back to the disk, which makes it a slower system for data processing. It's ideal for long-running, high-latency jobs, but not for interactive analysis or real-time data processing.

- Spark: Spark is known for its in-memory processing capabilities, which can significantly increase the speed of data processing compared to Hadoop. It caches intermediate data in memory rather than storing it on disk, which is ideal for iterative algorithms and interactive data mining tasks. This makes Spark faster for complex applications. Spark uses the concept of Directed Acyclic Graph (DAG) for executing tasks, which allows it to optimize the execution plan. Hadoop's MapReduce model, on the other hand, executes tasks in a linear sequence, which can lead to sub-optimal performance.

Ease of Use:

- Hadoop: Hadoop is typically programmed using Java, which can be complex and difficult for beginners. It also uses MapReduce, a programming paradigm that requires developers to write two separate scripts (map and reduce) for data processing tasks.

- Spark: Spark provides high-level APIs in Java, Scala, Python and R, and an interactive shell in Scala and Python. This makes it easier to develop applications in Spark. It also supports SQL queries, streaming data, machine learning and graph data processing, which are used through separate libraries that are part of the Spark project.

Fault Tolerance:

- Hadoop: Hadoop's HDFS is designed to be highly fault-tolerant. Data is automatically replicated across multiple nodes to ensure that it is not lost if a single node fails. This redundancy ensures high availability and reliability of data.

- Spark: Spark provides fault-tolerance through the abstraction of Resilient Distributed Datasets (RDDs). RDDs are designed to handle the failure of any worker node in the cluster, ensuring that the loss of a worker node does not result in data loss.

Security:

- Hadoop: Hadoop provides robust security features including Kerberos authentication, access control lists (ACLs) and data encryption. It also integrates with enterprise-level security systems.

- Spark: Spark's security features are relatively less mature compared to Hadoop. It supports authentication via shared secret (password authentication). However, it relies on the underlying Hadoop cluster for user authentication and data-level security.

Cost:

- Hadoop: Hadoop is designed to run on commodity hardware, making it a cost-effective solution for processing large amounts of data. It can handle terabytes and even petabytes of data on thousands of nodes.

- Spark: Spark requires substantial amounts of RAM to run in-memory, making it a more expensive solution compared to Hadoop. However, the speed and ease of use often justify the additional cost.

Data Processing:

- Hadoop: Hadoop is designed for high-throughput batch processing. It's best suited for large-scale data-intensive applications like data mining and data warehousing where data is processed in large blocks or batches.

- Spark: Spark supports both batch processing and real-time data processing. It excels in processing live data streams and delivering results in near real-time.

Machine Learning:

- Hadoop: Hadoop supports machine learning through Apache Mahout, a library for scalable machine learning algorithms. However, Mahout requires deep knowledge of the underlying algorithms and is not as user-friendly as other machine learning tools.

- Spark: Spark comes with MLlib, a built-in library for machine learning that provides various algorithms for classification, regression, clustering, and collaborative filtering. It also supports distributed computing, making it highly scalable.

I hope you have found this helpful. Thank you for reading.