Business Software for Apache Arrow

Apache DataFusion is an extensible, high-performance query engine written in Rust that utilizes Apache Arrow as its in-memory format. Designed for developers building data-centric systems such as databases, data frames, machine learning, and streaming applications, DataFusion offers SQL and DataFrame APIs, a vectorized, multi-threaded, streaming execution engine, and support for partitioned data sources. It natively supports formats like CSV, Parquet, JSON, and Avro, and allows for seamless integration with object stores including AWS S3, Azure Blob Storage, and Google Cloud Storage. The engine features a comprehensive query planner, a state-of-the-art optimizer with capabilities like expression coercion and simplification, projection and filter pushdown, sort and distribution-aware optimizations, and automatic join reordering. DataFusion is highly customizable, enabling the addition of user-defined scalar, aggregate, and window functions, custom data sources, query languages, etc.

Powerful data engineering workflows, without the infrastructure headaches. Complex streaming, scheduling, and data backfill pipelines, are all defined in simple, composable Python. Make ETL a thing of the past, fetch all of your data in real-time, no matter how complex. Incorporate deep learning and LLMs into decisions alongside structured business data. Make better predictions with fresher data, don’t pay vendors to pre-fetch data you don’t use, and query data just in time for online predictions. Experiment in Jupyter, then deploy to production. Prevent train-serve skew and create new data workflows in milliseconds. Instantly monitor all of your data workflows in real-time; track usage, and data quality effortlessly. Know everything you computed and data replay anything. Integrate with the tools you already use and deploy to your own infrastructure. Decide and enforce withdrawal limits with custom hold times.

XTDB is an immutable SQL database designed to simplify application development and ensure data compliance. It automatically preserves data history, enabling comprehensive time-travel queries. Users can perform as-of queries and audits using SQL commands. XTDB is trusted by various companies to transform dynamic and temporal applications. It is easy to get started with via HTTP, plain SQL, or various programming languages, requiring only a client driver or Curl. Users can effortlessly insert data immutably, query it across time, and execute complex joins. Risk systems benefit directly from bitemporal modeling. Valid time can be used to correlate out-of-order trade data whilst making compliance easy. Exposing data across an organization is a challenge when things are changing all the time. XTDB simplifies data exchange and can power advanced temporal analysis. Modeling future pricing, tax, and discount changes requires extensive temporal queries.

Data is used in every aspect of a processing plant or facility, it is underlying most operational processes, most business decisions, and most environmental events. Failures are often attributed to this same data, in terms of operator error, bad sensors, safety or environmental events, or poor analytics. This is where APERIO can alleviate these problems. Data integrity is a key element of Industry 4.0; the foundation upon which more advanced applications, such as predictive models, process optimization, and custom AI tools are developed. APERIO DataWise is the industry-leading provider of reliable, trusted data. Automate the quality of your PI data or digital twins continuously and at scale. Ensure validated data across the enterprise to improve asset reliability. Empower the operator to make better decisions. Detect threats made to operational data to ensure operational resilience. Accurately monitor & report sustainability metrics.

Light up the black box and pip install 3LC to gain the clarity you need to make meaningful changes to your models in moments. Remove the guesswork from your model training and iterate fast. Collect per-sample metrics and visualize them in your browser. Analyze your training and eliminate issues in your dataset. Model-guided, interactive data debugging and enhancements. Find important or inefficient samples. Understand what samples work and where your model struggles. Improve your model in different ways by weighting your data. Make sparse, non-destructive edits to individual samples or in a batch. Maintain a lineage of all changes and restore any previous revisions. Dive deeper than standard experiment trackers with per-sample per epoch metrics and data tracking. Aggregate metrics by sample features, rather than just epoch, to spot hidden trends. Tie each training run to a specific dataset revision for full reproducibility.

Daft is a framework for ETL, analytics and ML/AI at scale. Its familiar Python dataframe API is built to outperform Spark in performance and ease of use. Daft plugs directly into your ML/AI stack through efficient zero-copy integrations with essential Python libraries such as Pytorch and Ray. It also allows requesting GPUs as a resource for running models. Daft runs locally with a lightweight multithreaded backend. When your local machine is no longer sufficient, it scales seamlessly to run out-of-core on a distributed cluster. Daft can handle User-Defined Functions (UDFs) in columns, allowing you to apply complex expressions and operations to Python objects with the full flexibility required for ML/AI. Daft runs locally with a lightweight multithreaded backend. When your local machine is no longer sufficient, it scales seamlessly to run out-of-core on a distributed cluster.