Spark Processor

📄️ Spark Processor - An Overview

When working with large amounts of data, traditional tools and scripts (like Python with pandas, or simple SQL queries on a single database) often struggle. They either run very slowly or fail because they cannot handle data beyond the capacity of one computer.

📄️ Spark Processor: Code and Output Screens

The Spark Processor in Syntasa provides a flexible interface to perform data transformations using different programming languages and manage output datasets effectively. It has two key screens — Code and Output — that together form the core of Spaark-based data processing within an application workflow.

📄️ Spark Processor for Python - An Overview

The Spark Processor in Syntasa serves as a powerful component for transforming, enriching, and analyzing large-scale data within data pipelines. When written in Python, it combines the flexibility of the Python programming language with the distributed processing power of Apache Spark. This makes it possible to handle complex data transformations, apply machine learning models, and orchestrate end-to-end analytics workflows efficiently. With Spark Processors written in Python, users can directly utilize familiar Python libraries and functions to operate on distributed datasets, enabling rapid experimentation and streamlined data engineering.

📄️ Understanding Conda Environment for Spark Processor (Python)

Starting from version 8.0, Syntasa introduced the option to run Spark Processors inside a Conda environment. Conda is a package and environment manager that allows you to create isolated environments with specific Python versions and dependencies. This feature provides greater flexibility and consistency compared to traditional execution, where all Spark processors had to rely on the default Python version or the runtime settings.

📄️ Handling Partitioned and Non Partitioned Data in Spark Processor using Python

When working with data pipelines in Syntasa, the Spark Processor is one of the most important components for data transformation and output generation. In Syntasa, a Spark Processor acts as the engine that connects to your data source (Event Store, GCS, S3, or Azure storage), applies transformations, and writes the results to the desired output. When dealing with data pipelines, one important concept is whether your data is partitioned or non-partitioned.

📄️ Handling Python Libraries for Spark Processor

Python libraries are the building blocks that enable data scientists and engineers to perform complex operations efficiently. For example, a data scientist working on an e-commerce application may rely on Pandas for customer purchase analysis, Scikit-learn for building recommendation models, or Google Cloud Storage SDK to pull customer event logs directly from cloud storage. Without these libraries, building data pipelines or machine learning workflows in Spark Processors would be cumbersome and time-consuming. Hence, proper library management is a critical part of working in the Syntasa environment.

📄️ Spark Processor for Scala - An Overview

Scala is the native language of Apache Spark — the foundation upon which Spark was originally developed. In the Syntasa platform, when you choose to write your Spark Processor in Scala, you’re using Spark in its most direct and efficient form. Scala gives you the closest integration with Spark’s distributed data processing engine, enabling faster execution, better optimization, and type safety during development.

📄️ Handling Partitioned and Non Partitioned Data in Spark Processor using Scala

When developing data pipelines with Spark processor using the Scala language, Scala serves as a native language for Spark, offering the most direct and optimized way to work with large-scale distributed data. The Spark Processor connects to various input sources—such as Event Stores, GCS, S3, or Azure storage—reads the data as Spark DataFrames, applies transformations using native Scala Spark APIs, and writes the processed output back to an Event Store.

📄️ Handling Scala Libraries in Spark Processor

When working with Spark Processors in Syntasa, you may need to extend your Scala code using external libraries. These libraries can help perform advanced operations such as JSON parsing, complex string transformations, or custom analytics. Unlike Python, Scala libraries cannot be installed directly from the Spark Processor UI. Instead, they are managed through Spark runtime configurations. This ensures that all executors in the Spark cluster have access to the same dependencies at runtime.