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1. What is Spark and what is its purpose? & Key features Apache Spark is an open-source distributed computing system that specializes in big data processing and analytics. It was developed to address limitations and improve upon the performance of the Hadoop MapReduce framework. Spark provides a fast and general-purpose cluster computing framework for large-scale data processing tasks. - Azure Data Engineer Course Key features and purposes of Apache Spark include: 1.Speed: Spark is known for its speed and performance improvements over traditional MapReduce. It achieves this by utilizing in-memory processing, reducing the need to write intermediate results to disk. This makes Spark well-suited for iterative algorithms and interactive data analysis. - Azure Data Engineer Online Training 2.Ease of Use: Spark provides high-level APIs in languages such as Scala, Java, Python, and R. It also includes a built-in set of higher-level libraries for various tasks, such as Spark SQL for structured data processing, Spark Streaming for real-time data processing, MLlib for machine learning, and GraphX for graph processing. These libraries make it easier for developers to build applications without having to manage low-level details of distributed computing. 3.Flexibility: Spark supports a wide range of data processing tasks, including batch processing, iterative algorithms, interactive queries, and streaming.

2. Its flexibility makes it suitable for diverse use cases, from data warehousing to machine learning. 4.Distributed Computing: Spark distributes data and computations across clusters of machines, enabling horizontal scaling. This allows Spark to handle large datasets that may not fit into the memory of a single machine. - Azure Data Engineer Training Hyderabad 5.Unified Platform: Spark provides a unified platform for various data processing tasks. Instead of using different tools for batch processing, streaming, machine learning, and graph processing, developers can use Spark to cover these use cases within a single framework. 6.Fault Tolerance: Spark achieves fault tolerance through resilient distributed datasets (RDDs), an immutable distributed collection of objects. RDDs automatically recover lost data partitions in case of node failures. - Data Engineer Course in Hyderabad 7.Integration with Hadoop: Spark can run on Hadoop Distributed File System (HDFS) and can be easily integrated with Hadoop ecosystems. This means it can leverage existing Hadoop data and work seamlessly with Hadoop-based tools. Overall, the purpose of Apache Spark is to provide a powerful and versatile platform for large-scale data processing and analytics, with a focus on speed, ease of use, and flexibility. It has gained popularity in the big data community and is widely used for various data processing tasks in industries such as finance, healthcare, retail, and more. - Azure Data Engineer Training Ameerpet Visualpath is the Best Software Online Training Institute in Hyderabad. Avail complete Azure Data Engineer Trainingworldwide. You will get the best course at an affordable cost. Attend Free Demo Call on - +91-9989971070. WhatsApp: https://www.whatsapp.com/catalog/919989971070 Visit https://visualpath.in/azure-data-engineer-online-training.html