Apache Spark vs Confluent Cloud

Apache Spark is a free, open-source engine for large-scale data analytics. It processes batches and real-time streams using Python, SQL, Scala, Java, or R. Spark SQL runs distributed ANSI SQL queries on structured tables and unstructured data such as JSON or images; MLlib provides machine-learning algorithms for local use or cluster scale. The project also describes exploratory analysis on petabyte-scale data without downsampling. Listed ecosystem components include pandas, TensorFlow, PyTorch, scikit-learn, Apache Kafka, Kubernetes, Delta Lake, and Apache Iceberg. Deployment options include standalone, Hadoop YARN, and Kubernetes. Spark runs on Windows and UNIX-like systems, including Linux and macOS, when a supported Java version is available. The 4.2.0 documentation lists Java 17, 21, or 25, Scala 2.13, Python 3.10+, and R 4.0+ (deprecated). Spark Connect separates client applications from clusters for remote access. Security is not enabled by default; Spark supports TLS for RPC connections and encryption of temporary local-disk data. The project is licensed under Apache License, Version 2.0.

Confluent Cloud is a managed data streaming platform built on a serverless Apache Kafka engine. It is designed for real-time streaming, event-driven applications, analytics, and AI/ML workloads. Cluster choices include Basic, Standard, Enterprise, and Freight, as well as Dedicated clusters with pre-provisioned resources. Confluent says its cloud-native engine adjusts cluster infrastructure to workload needs. Cluster Linking replicates, synchronizes, and shares data across regions, public clouds, and organizations. The service includes managed Kafka connectors for databases, data lakes, and warehouses, serverless processing with Apache Flink, and a managed Stream Governance suite. Tableflow can materialize Kafka topics and schemas as Apache Iceberg and Delta Lake tables. Listed security features include RBAC, self-managed encryption keys, client-side field-level encryption, and audit logs. Users can manage clusters and topics through a web UI or CLI; supported client languages include Java, Python, .NET, Go, Node.js, and C/C++. It is available on AWS, Azure, and Google Cloud across more than 100 regions. A free plan is available; paid plans start at $385/mo, and new signups receive a 30-day trial.