Compare Apache DataFusion vs. Apache Pulsar in 2025

Apache Pulsar

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Description

Apache DataFusion is a versatile and efficient query engine crafted in Rust, leveraging Apache Arrow for its in-memory data representation. It caters to developers engaged in creating data-focused systems, including databases, data frames, machine learning models, and real-time streaming applications. With its SQL and DataFrame APIs, DataFusion features a vectorized, multi-threaded execution engine that processes data streams efficiently and supports various partitioned data sources. It is compatible with several native formats such as CSV, Parquet, JSON, and Avro, and facilitates smooth integration with popular object storage solutions like AWS S3, Azure Blob Storage, and Google Cloud Storage. The architecture includes a robust query planner and an advanced optimizer that boasts capabilities such as expression coercion, simplification, and optimizations that consider distribution and sorting, along with automatic reordering of joins. Furthermore, DataFusion allows for extensive customization, enabling developers to incorporate user-defined scalar, aggregate, and window functions along with custom data sources and query languages, making it a powerful tool for diverse data processing needs. This adaptability ensures that developers can tailor the engine to fit their unique use cases effectively.

Description

Apache Pulsar is a cutting-edge, distributed platform for messaging and streaming that was initially developed at Yahoo! and has since become a prominent project under the Apache Software Foundation. It boasts straightforward deployment, a lightweight computing process, and APIs that are user-friendly, eliminating the necessity of managing your own stream processing engine. For over five years, it has been utilized in Yahoo!'s production environment, handling millions of messages each second across a vast array of topics. Designed from the outset to function as a multi-tenant system, it offers features like isolation, authentication, authorization, and quotas to ensure secure operations. Additionally, Pulsar provides configurable data replication across various geographic regions, ensuring data resilience. Its message storage relies on Apache BookKeeper, facilitating robust performance, while maintaining IO-level separation between read and write operations. Furthermore, a RESTful admin API is available for effective provisioning, administration, and monitoring tasks, enhancing operational efficiency. This combination of features makes Apache Pulsar an invaluable tool for organizations seeking scalable and reliable messaging solutions.