December 2025 Summaries

Efficiently running Apache Spark workloads requires meticulous orchestration, which Apache Airflow provides as a leading solution for job scheduling, retries, timeouts, and observability. Onehouse's integration with Airflow enhances this capability, allowing users to orchestrate data pipelines on the Quanton Engine with improved price-performance for ETL tasks. Apache Airflow uses Directed Acyclic Graphs (DAGs) to define task dependencies and offers features like dependency management, scheduling, automation, monitoring, and modularity, making it ideal for production-grade Spark workloads. The integration with Onehouse provides operators and sensors that facilitate cluster management and job execution directly from Airflow DAGs. This setup helps manage various Spark workload types, including ETL pipelines, incremental data processing, ML feature engineering, and data quality checks, by ensuring clusters are provisioned appropriately, dependencies are managed, and failures are addressed automatically. For those migrating from Amazon EMR, Onehouse offers a straightforward transition path with significant cost-performance benefits. Best practices include using sensors for asynchronous operations, setting appropriate timeouts, leveraging XCom templates, and ensuring resource cleanup to optimize orchestration and maintain efficiency.

Efficient query performance in a data lakehouse is significantly influenced by the organization and maintenance of data, as detailed in a guide on Onehouse optimization strategies. The guide emphasizes the importance of storing data with optimal file sizing, sorting, and indexing, particularly using Apache Parquet™, to balance read and write performance, and suggests a file size of 120 MB to minimize I/O operations. Onehouse's Clustering service automatically optimizes file sizes and sorts data to enhance query speed, advocating sorting data by frequently filtered columns and using advanced techniques like Z-Order for multi-dimensional data. Partitioning is recommended for improving file pruning, with a focus on avoiding small files and partition skew, monitored through the Onehouse console. Indexes, such as those in Apache Hudi™, are highlighted for accelerating lookups, while ingestion performance profiles in OneFlow provide options for balancing read and write speeds. The guide advises filtering queries on partition and sort columns, using appropriate data types, and optimizing joins through strategies like broadcasting small tables. It also discusses choosing the right query engines, with Onehouse offering managed engines for various use cases, ensuring that optimized data layout translates to consistently fast query performance.

The performance of cloud object storage platforms like Amazon S3 and Google Cloud Storage (GCS) is significantly influenced by the HTTP protocols they utilize, with S3 relying on HTTP/1.1 and GCS employing HTTP/2. HTTP/1.1's limitations, such as head-of-line blocking and higher latency, lead to inefficiencies and increased costs, as demonstrated by S3 showing up to 15 times higher latency compared to GCS in practical workloads. These inefficiencies arise because HTTP/1.1 lacks the multiplexing and header compression benefits of HTTP/2, resulting in higher TCP overhead and variability in software development kit (SDK) performance. Onehouse addresses these challenges by implementing optimizations like byte-range coalescing and smart concurrency management to improve cost efficiency and performance in data lake operations. The shift from distributed file systems to object storage systems has made HTTP behavior critical in managing data lakes, emphasizing the need for effective protocol management to reduce compute costs and improve throughput. Onehouse's lakehouse platform incorporates these insights to enhance connection management and protocol behavior, ensuring better performance and cost savings for users.

At the AWS re:Invent event, AWS EMR introduced a new runtime version, EMR 7.12, featuring enhanced performance claims for Apache Spark workloads with Apache Iceberg, prompting comparisons with Onehouse's benchmarks on their Spark engine, Quanton™. The blog post highlights the importance of consistent benchmarking conditions, noting that Spark performance is significantly affected by various configurations and settings. Onehouse's testing revealed a 32% improvement in EMR's price/performance over previous versions, yet Onehouse maintained a 2.5x advantage. The blog also critiques EMR's aging infrastructure and support limitations, contrasting it with Onehouse's expertise and advanced capabilities in handling modern lakehouse workloads. Additionally, Onehouse offers a Cost Analyzer tool for Apache Spark™, providing users with detailed job performance insights, and invites interested parties to explore their Quanton pipelines and join their engineering team focused on data infrastructure innovation.

Onehouse Notebooks is a new PySpark Jupyter notebook experience powered by the Onehouse Quanton engine, offering improved price-performance compared to other Apache Spark platforms. It allows users to run interactive PySpark workloads with the benefit of autoscaling clusters within a virtual private cloud, which helps manage costs and infrastructure flexibility. Fully compatible with Apache Spark, Onehouse Notebooks enables users to transition existing PySpark files seamlessly, and the integration with the broader Onehouse platform ensures automatic table optimization and synchronization with any catalog using OneSync. The platform is designed for iterative data engineering, offering the ability to test and validate code cell-by-cell, making it ideal for tasks such as exploring new datasets, prototyping transformations, debugging, and conducting ad-hoc analysis. Users can develop interactively within notebooks and, once ready, operationalize their logic as Apache Spark jobs for production pipelines. To get started, users create a notebook cluster through the Onehouse console, where they can control costs and access their Jupyter notebooks pre-configured with PySpark, facilitating high-performance data lakehouse construction with enhanced cost efficiency.