Hight level API in Spark

In Apache Spark, a high-level API refers to the set of more user-friendly and abstracted programming interfaces provided by Spark to simplify the development of Spark applications. These high-level APIs make it easier for developers to work with Spark's distributed computing capabilities while abstracting away some of the complexities of distributed data processing. Here are some of the high-level APIs in Spark:

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1. DataFrame API
2. Spark SQL
3. MLlib (Machine Learning Library)
4. GraphX
5. Structured Streaming
6. SparkR

DataFrame API: The DataFrame API is one of the most popular high-level APIs in Spark. It provides a structured way to work with data in a tabular format, similar to a relational database or a spreadsheet. DataFrames allow you to perform SQL-like operations and transformations on data, and they can represent structured data from various sources, including Parquet, JSON, CSV, and more.

Spark SQL: Spark SQL is a Spark module for structured data processing. It provides a programming interface to work with structured data using SQL, DataFrames, and Datasets. You can run SQL queries on Spark DataFrames, and Spark SQL is tightly integrated with the DataFrame API.

MLlib (Machine Learning Library): MLlib is a high-level machine learning library in Spark. It offers a wide range of machine learning algorithms and tools, making it easier to build and train machine learning models on large datasets.

GraphX: GraphX is a high-level API for graph processing in Spark. It provides abstractions for creating and manipulating graph structures, allowing you to perform graph analytics and computations.

Structured Streaming: Structured Streaming is a high-level stream processing API in Spark. It allows you to process live data streams in a structured and batch-like manner, using the same DataFrame and SQL APIs you use for batch processing. It simplifies the development of real-time data processing applications.

SparkR: SparkR is an R package that provides an R interface to Spark. It allows R users to leverage Spark's distributed processing capabilities from within the R programming language.

These high-level APIs provide abstractions and APIs that are more intuitive and developer-friendly, making it easier to work with Spark for a wide range of data processing tasks, from batch processing to machine learning and streaming. They hide some of the low-level details of distributed computing, making it more accessible to data engineers and data scientists who may not have extensive experience with distributed systems. However, it's important to note that you can also use lower-level APIs in Spark, such as the RDD (Resilient Distributed Dataset) API, for fine-grained control when necessary.