April 2025 Summaries

Cloud data warehouses are increasingly supporting open table formats like Apache Iceberg, Hudi, and Delta Lake, marking a shift from their traditionally closed ecosystems. However, the current support for these formats is limited, inconsistent, and often lacks the true interoperability required for a genuinely open architecture. While data lakes have long embraced open storage formats and decoupled compute and storage to optimize resource management, cloud warehouses have historically favored a tightly integrated approach, which has led to challenges in scaling and vendor lock-in. The introduction of open table formats offers a path to addressing these issues by allowing modularity and flexibility, but the implementations across major platforms like Snowflake, Amazon Redshift, and Google BigQuery still fall short of offering the same capabilities as native tables, particularly in areas such as schema evolution, time travel, and performance optimizations. Despite the positive trend towards openness, cloud warehouses continue to rely on proprietary storage formats, which limits external interoperability and creates new forms of vendor lock-in. For open table formats to become truly beneficial, they must be implemented consistently across platforms and treated with the same importance as native tables, fostering an environment where multi-engine interoperability is a first-class concern.

Streaming engines are critical for processing continuous data streams in real-time or near real-time, serving applications that require low latency, high throughput, and scalability. The blog discusses the intricacies of streaming engines, highlighting key aspects like stateful processing, checkpointing, time semantics, backpressure handling, and delivery guarantee semantics, which are essential for robust fault tolerance and efficient data processing. Apache Flink and Kafka Streams are explored in depth due to their popularity, with Flink offering a high-throughput, low-latency engine ideal for comprehensive stateful operations, while Kafka Streams, integrated with Kafka clusters, is suited for building lightweight, event-driven applications. Spark Structured Streaming is noted for its simplicity and integration within the Apache Spark ecosystem, making it a top choice for rapid development with minimal setup. The piece emphasizes that a single engine may not suffice for all streaming needs, advocating for the use of multiple engines to balance performance and cost as workloads evolve.

Machine learning and data science are rapidly advancing, with new tools like large language models and retrieval-augmented generation pushing the field forward. Python remains the favored language for these disciplines, with libraries such as NumPy, Pandas, and PyTorch handling complex computations. However, as data grows and computations become more intricate, scaling beyond single-node applications is crucial. Distributed compute engines like Apache Spark, Ray, and Dask allow scaling across multiple machines, each offering unique strengths—Spark excels in data pipeline resiliency, Ray in heterogeneous computing with GPUs and CPUs, and Dask in scaling Python code seamlessly. These engines use a master-worker architecture to distribute tasks, with Spark's architecture focusing on fault tolerance and efficiency, Ray's on parallel and heterogeneous computing, and Dask's on scaling Python natively. The choice of engine often depends on specific workload requirements, such as scale, need for GPUs, and data types. Spark is known for its data preprocessing abilities, Ray for ML model training and heterogeneous workloads, and Dask for its Python-native architecture. Working with these engines allows organizations to handle diverse computational workloads efficiently, with each engine providing different integrations and optimizations for data science and machine learning tasks.

The blog post delves into the rapidly evolving analytics landscape, focusing on the architecture and trade-offs of five distributed analytics engines: Apache Spark, PrestoDB, Trino, StarRocks, and ClickHouse. These engines are categorized into General Purpose, Interactive SQL, and Realtime OLAP, each serving distinct use cases, from versatile batch processing to fast ad-hoc querying and high-speed real-time analytics. The analysis covers their architectural designs, scalability, concurrency, storage support, and language capabilities, emphasizing factors such as SIMD support, caching, elastic scaling, concurrency management, and support for various file and table formats. Apache Spark stands out for its broad ecosystem, storage, and language support, while Trino and Presto shine in interactive SQL capabilities. StarRocks and ClickHouse offer high-speed OLAP operations with vectorization. The post underscores the importance of choosing an engine based on technical, business, and strategic needs, promoting a flexible approach to leveraging multiple engines within the Onehouse platform for optimized data management and analytics.

At Onehouse, the focus is on advancing open data infrastructure by introducing Open Engines™, a platform that simplifies deploying open-source compute engines on open data, thereby enhancing flexibility and reducing maintenance costs. This development follows a trend in the industry towards open data formats and lakehouse architectures that decouple data from specific compute engines, offering greater interoperability and innovation. Open Engines addresses the challenges users face with proprietary compute solutions by enabling seamless integration and management of engines like Apache Flink, Trino, and Ray, which cater to various analytics and data science needs. By prioritizing data-centric approaches over engine-first strategies, Onehouse aims to eliminate vendor lock-in, optimize performance, and promote open data architectures as a standard. This initiative not only reduces operational complexity but also encourages the adoption of open-source engines, providing users with more choices and control over their data ecosystems.