Lesson 1 Flashcards
What is Apache Spark?
Apache Spark is an open-source big data platform primarily used for data science tasks, capable of processing various data types, including structured and unstructured data, efficiently.
What distinguishes Databricks from Apache Spark?
Databricks is a commercial product built by the developers of Apache Spark, offering a complete development environment with proprietary enhancements for collaboration and ease of use, while Apache Spark itself is an open-source project providing powerful data processing capabilities.
Describe the concept of scaling in big data processing.
Scaling in big data processing involves distributing computational tasks across multiple machines, known as “scale out,” as opposed to traditional single-machine processing (scale up). This approach allows for efficient handling of large volumes of data by leveraging the combined resources of multiple machines.
What is the Hadoop project, and how has it evolved?
The Hadoop project initially focused on providing batch-oriented processing through its core component, MapReduce. Over time, it expanded to include various services and tools beyond MapReduce, forming the Apache Hadoop project, which encompasses a broad range of functionalities for big data processing.
How does Apache Spark improve upon traditional big data processing approaches?
Apache Spark improves upon traditional approaches like Hadoop MapReduce by utilizing in-memory processing, which significantly reduces data transfer between disk and memory, resulting in much faster processing speeds. Additionally, Spark offers a more flexible and accessible programming interface, making it easier for developers to interact with and leverage its capabilities.
What role does distributed file storage play in big data platforms like Hadoop?
Distributed file storage systems, such as Hadoop Distributed File System (HDFS), play a crucial role in big data platforms by efficiently storing and managing large volumes of data across multiple nodes in a cluster. These systems enable parallel processing of data by providing a unified view of distributed data across the cluster.
How does Apache Spark differ from traditional Hadoop MapReduce?
Apache Spark offers significant performance improvements over Hadoop MapReduce by primarily utilizing in-memory processing, reducing the need for data to be written to disk between processing stages. This results in faster data processing and analysis compared to the disk-based processing approach of MapReduce.
What is the significance of the YARN component in the Hadoop ecosystem?
YARN (Yet Another Resource Negotiator) is a key component of the Hadoop ecosystem responsible for managing and allocating cluster resources efficiently. It allows multiple data processing engines to run on the same Hadoop cluster, enabling better resource utilization and improved cluster management.
How does Databricks enhance collaboration for data science teams?
Databricks provides a complete development environment optimized for collaboration among data science teams. It offers proprietary enhancements such as interactive notebooks, real-time collaboration features, and streamlined workflows, enabling teams to work more efficiently and effectively on data science projects.
What are some challenges associated with traditional Hadoop MapReduce for data processing?
Traditional Hadoop MapReduce poses challenges such as complex programming models (often requiring Java), disk-based processing leading to slower performance, and limited support for real-time data processing requirements. Additionally, managing and deploying MapReduce jobs can be cumbersome and resource-intensive.
How does Apache Spark address the limitations of traditional Hadoop MapReduce?
Apache Spark addresses the limitations of Hadoop MapReduce by providing a more flexible programming model with support for multiple languages (e.g., Scala, Python), in-memory processing for faster performance, and built-in libraries for various data processing tasks such as SQL queries, machine learning, and streaming analytics. This makes Spark more suitable for a wide range of use cases, including batch processing, interactive analytics, and real-time data processing.
What is the core functionality of the Hadoop Distributed File System (HDFS)?
The Hadoop Distributed File System (HDFS) provides a distributed storage system for storing large volumes of data across multiple machines in a Hadoop cluster. It enables data replication, fault tolerance, and high throughput for data processing tasks.
How does Apache Spark improve upon the traditional MapReduce model for data processing?
Apache Spark improves upon the traditional MapReduce model by offering a more versatile and efficient processing framework. Spark performs in-memory computations, reducing the need for data serialization and disk I/O, resulting in faster data processing speeds. Additionally, Spark provides a rich set of APIs for various data processing tasks, including batch processing, interactive queries, machine learning, and streaming analytics.
What role does the Cluster Manager play in Apache Spark?
The Cluster Manager in Apache Spark is responsible for coordinating resources and managing the execution of Spark applications across the cluster. It allocates resources such as memory and CPU cores to individual tasks and monitors their progress to ensure efficient utilization of cluster resources.
What are some advantages of using Databricks over traditional Apache Spark deployments?
Databricks offers several advantages over traditional Apache Spark deployments, including a unified and collaborative workspace for data science teams, built-in support for interactive notebooks, simplified cluster management, and integration with cloud services for seamless deployment and scalability.
