Spark SQL code development using Local desktop conda environment and Jupyter Notebook

Spark SQL is a component of Apache Spark that provides a programming interface for structured and semi-structured data using SQL (Structured Query Language).

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Spark SQL is essential in data engineering because it provides a unified and efficient way to work with data, seamlessly integrating SQL capabilities with the distributed processing power of Apache Spark. It simplifies development, improves performance, and enhances compatibility with existing data infrastructure and tools. Spark SQL allows users to seamlessly integrate SQL queries with Spark programs. This unification simplifies the development process by enabling data engineers to work with structured data using SQL and at the same time leverage the power of Spark for distributed data processing.

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Another key advantage of ease of use. SQL is a widely-used language for working with structured data. Spark SQL makes it easier for data engineers who are familiar with SQL to interact with and manipulate data in Spark.

Spark SQL seamlessly integrates with Apache Hive, a data warehouse infrastructure built on top of Hadoop. This integration allows Spark to query data stored in Hive, making it a valuable tool for organizations with existing Hive deployments.

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Spark SQL typically runs on distributed computing platforms like Apache Spark clusters. However, it can run in local mode for small-scale testing and development. if server or platform is not available for development or proof of concept, local desktop based conda environment & Juptyer notebook can be made to work with Spark SQL.

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However, proof of concept involving large volume of data processing using Spark SQL can be challenging from local desktop. Application involving large volume of data require specific set up and configuration of Spark cluster. This includes dealing with considerations such as resource allocation, cluster mode (local, standalone, or cluster), and connectivity with various data storage systems.

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In a distributed environment, performance can be a critical factor. Access to a server or platform enables assessment of performance and opportunities for optimization. These activities can be performed on local desktop based conda environment. local desktop based conda environment is suitable for exploration of Spark SQL syntax, and features.

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Following are steps to install python spark packages for learning and proof of concept development:

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Step 1: Create a new Conda Environment

Name of environment can be anything. In this illustration, coda environment is being created with the name Spark

Execute following on conda prompt

 conda create –name Spark        

Click y to complete the installation

Step 2:? Activate newly created coda environment

Name of environment can be anything, in this case name of environment is spark

Execute following on conda prompt to activate the newly created conda environment

activate spark        

Step 3: Install all the needed python packages

Install pip by executing following on conda prompt

conda install pip        

Click y to complete the installation

Step 4: Install jupyter notebook

execute following on conda prompt.

pip install jupyter notebook        

Step 5: Install pyspark package

Execute following on conda prompt

pip install pyspark        

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?It might take some time to complete.

Step 6: After all, above installation, jupyter notebook can be started

Now code can be written in Jupyter Notebook to work with Spark SQL

jupyter notebook with sample code and sample data for illustration can be found at : https://github.com/vivekvision/SparkSQL_LocalDesktop_JupyterNotebook