Azure Data Engineer

Apache Spark, renowned for its prowess in distributed computing, introduces Spark SQL as a powerful module dedicated to structured data processing. Spark SQL seamlessly integrates relational data querying with Spark's functional programming paradigm, offering a unified platform for diverse and large-scale data processing.  - AzureData Engineer Course Key Features: 1.    Unified Data Processing:  Spark SQL bridges the gap between structured and semi-structured data processing. It provides a unified interface, allowing users to execute queries on various data formats, including Parquet, JSON, and Hive. 2.     Hive Compatibility:  Boasting complete compatibility with Apache Hive, Spark SQL facilitates users familiar with Hive to run queries directly within the Spark environment. This compatibility ensures a smooth transition and coexistence with existing Hive data and metadata.  - Azure Data Engineer Online Training 3.      DataFrame API:  At the core of Spark SQL is the DataFrame API

Apache Spark is an open-source distributed computing system that provides a fast and general-purpose cluster-computing framework for big data processing and analytics. It was developed to overcome the limitations of the Hadoop MapReduce model by offering a more versatile and efficient platform for large-scale data processing. 1.     Resilient Distributed Datasets (RDDs): RDD is the fundamental data structure in Spark. The collection of things it represents is distributed and immutable, allowing for parallel processing. RDDs can be created from existing data in Hadoop Distributed File System (HDFS), local file systems, or other data sources. - Azure Data Engineer Online Training 2.   Spark Core: The core engine of Spark provides the basic functionality for distributed task scheduling, memory management, and fault recovery. It also includes the RDD API for data manipulation and transformation. 3.      Spark SQL: Spark SQL enables the integration of structured data processing with