13.3 - Apache Spark

Apache Spark is a computing framework that's designed to handle large amounts of data more quickly than traditional systems, like Hadoop’s MapReduce. It does this by processing data in RAM (memory), which is much faster than writing intermediate results to disk. This key feature allows it to handle big data processing tasks efficiently.

Apache Spark is made up of several core components, each designed to handle specific tasks:
- Spark Core is the foundational engine that manages the underlying functionalities like job scheduling and memory management. It provides APIs for RDDs (Resilient Distributed Datasets), which help manage distributed data.
- Spark SQL enables users to run SQL queries against their data, making it easier to work with structured data while utilizing RDDs and DataFrames.
- Spark Streaming focuses on real-time data processing, allowing applications to handle live data streams.
- MLlib is a library that provides various machine learning algorithms, which can scale effectively with large datasets.
- GraphX allows for graph-related computations and analysis, such as social connections and pathways in a network.

RDDs, or Resilient Distributed Datasets, are the fundamental data structure in Spark. They are collections of objects that are distributed across a cluster and are immutable, meaning once created, they cannot be changed. This immutability ensures reliability and fault tolerance.
On the other hand, DataFrames are a more structured way to store data compared to RDDs. They resemble tables in a database, complete with named columns, allowing for easier data manipulation and query operations. DataFrames also benefit from Spark's optimization features.

The Spark execution model consists of a Driver Program that distributes tasks to a Cluster Manager. The Cluster Manager coordinates the available resources across the cluster and assigns these tasks to Executors, which run the computations.
Spark optimizes the execution process using a Directed Acyclic Graph (DAG) scheduler. A DAG represents the sequence of computations and their dependencies, allowing Spark to efficiently manage how tasks are executed.
Finally, Spark employs 'lazy evaluation,' meaning it won’t execute operations until necessary. This allows Spark to optimize the logical execution plan before carrying out actions, improving performance.

Apache Spark offers several advantages that make it appealing for big data processing. Its in-memory processing capability allows data to be processed much faster compared to systems that rely on disk storage. Spark supports both batch and stream processing, providing flexibility depending on the application's needs. Additionally, Spark offers comprehensive APIs in multiple programming languages like Python, Scala, Java, and R, making it accessible to a broad range of developers. Moreover, its design is particularly suited for iterative tasks such as those found in machine learning, where multiple passes of data may be needed.

While Apache Spark offers many advantages, it also has some limitations. One major drawback is that Spark typically uses more memory than Hadoop due to its in-memory processing approach. This can lead to higher costs associated with memory usage, particularly in large clusters. Furthermore, achieving optimal performance may require tuning and configuration of the cluster settings, which can be complex. Lastly, there is limited built-in support for data governance, meaning users may need to implement additional solutions to manage data security and compliance effectively.