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Spark Architecture

For companies that have integrated big data into their standard operations, processing speed becomes a determining factor. The increasingly voluminous waves of data can challenge the compute abilities of many applications.

Apache Spark is an open-source cluster computing engine that enables real-time data processing. It's built to handle disparate data sources and programming styles.

Spark's real-time processing framework can handle the enormous volume of data that various businesses capture daily. Spark enhances the data computing of all industries that leverage big data.

Additionally, Spark architecture is user-friendly because it integrates easily with other libraries. Spark is natively supported in Amazon EMR.

Operating within AWS, Spark is easily compatible and integrated.

SPARK ARCHITECTURE BASICS

The Spark ecosystem includes a combination of proprietary Spark products, such as Spark SQL, Spark Streaming and Spark MLlib, and various libraries that support SQL, Python, Java and other languages. This design positions Spark to integrate with a variety of workflows.

Spark architecture is dependent upon a Resilient Distributed Dataset (RDD). RDDs are the foundation of Spark applications.

The data within an RDD is divided into chunks, and it is immutable.

HOW DOES SPARK WORK?

Spark distributes data across the cluster and process the data concurrently. Spark uses master/agent architecture, and the driver communicates with executors. The relationship between the driver (master) and the executors (agents) defines the functionality.

ADVANTAGES OF SPARK ARCHITECTURE

Data scientists rely on Spark for its versatility and processing capabilities. Quite simply, its speed sets it apart from all competitors. Spark code can also be written in Java, Python, R and Scala.

Spark architecture also allows it to be deployed in a variety of ways, and data ingestion and extraction is not complicated. In addition, Spark fosters data through the intricate ETL pipeline.

Spark architecture provides for a scalable and versatile processing system that meets complex big data needs. It can be leveraged even further when integrated with existing data platforms.

Numerous big data courses include Spark as part of the curriculum.

SNOWFLAKE AND SPARK

Snowflake, the cloud data platform, is designed to connect with Spark. The Snowflake Connector for Spark brings Snowflake into the Apache Spark ecosystem, enabling Spark to read data from and write data to Snowflake. 

The connector provides Snowflake access to the Spark ecosystem as a fully-managed and governed repository for all data types, including JSON, Avro, CSV, XML, and more. The connector also allows for strong integration use cases, including complex ETL and machine learning