RustFS is a production-grade, S3-compatible distributed object storage server implemented in Rust. The core system provides erasure-coded storage with healing, bitrot detection, bucket versioning, object locking, lifecycle management, replication (bucket and site), tiering to remote S3-compatible backends, KMS integration, IAM/OIDC/Keystone authentication, audit logging, event notifications, and an extensive observability layer. It also exposes non-S3 protocols including OpenStack Swift, SFTP, FTPS, and WebDAV, plus an admin/console API and an embedded server library for in-process testing and integration. The repository is a large Cargo workspace of roughly 50 crates, with the main `rustfs` binary orchestrating startup, HTTP/gRPC servers, background services, and protocol servers, while domain subsystems such as `ecstore`, `heal`, `scanner`, `replication`, `notify`, `kms`, `iam`, `obs`, and `targets` are isolated behind trait-based contracts and runtime-boundary modules.
Vaex is a high-performance Python library for lazy out-of-core DataFrames designed to handle datasets larger than memory. It achieves efficiency through memory mapping, zero-copy policies, lazy evaluation, and virtual columns that avoid materializing intermediate results. The system supports HDF5, Apache Arrow, Parquet, CSV, and FITS formats, with accelerated operations via C++ extensions for critical paths like hash-based aggregations and string processing. The architecture is modular, consisting of a core package (vaex-core) and specialized extensions: vaex-viz (visualization), vaex-hdf5 (HDF5 enhancements), vaex-astro (astronomy tools), vaex-ml (machine learning integrations), vaex-jupyter (notebook support), vaex-server (remote access via WebSocket/REST), and vaex-graphql (GraphQL API). Key entry points include the `vaex` CLI tool, `vaex.open()` for data loading, and programmatic APIs for DataFrame operations, expressions, and visualizations. Data flows prioritize out-of-core processing: data remains on disk until needed, computations are lazily executed and cached via fingerprinting, and results are streamed to minimize memory footprint. The codebase demonstrates strong engineering practices with a plugin system for extensibility (expression/dataframe accessors), centralized settings configuration, and a task-based execution engine supporting parallelism and async operations. While primarily a general-purpose data analysis library, it is widely used in data research due to its efficiency with large datasets and domain-specific integrations (e.g., astronomy via vaex-astro). Recent commits indicate active maintenance, low technical drift through adoption of modern dependencies (Apache Arrow, FastAPI), and a comprehensive test suite covering core functionality and extensions.
This repository is a comprehensive educational resource for data engineering, structured as a handbook and bootcamp. It contains curated learning paths (beginner, intermediate, Databricks AI bootcamp) with lecture materials, homework, and sample datasets. The codebase includes teaching examples of core data engineering patterns: dimensional data modeling (cumulative tables, SCD type 2), fact data modeling (array metrics, datelist int), Apache Spark jobs with unit tests, Apache Flink streaming jobs, and analytical SQL patterns (retention, funnel, grouping sets, growth accounting). It also includes a small Flask server for A/B experimentation and Docker Compose setups for local development. While primarily educational, the SQL scripts and job templates are production-quality and reusable across projects. The repository is actively maintained with pinned dependencies and clear documentation, making it highly reproducible for learners.
Databend is an open-source, cloud-native distributed SQL query engine and data warehouse, built in Rust. It provides a full SQL interface for analytical workloads, supporting operations such as SELECT, INSERT, COPY INTO/OUT, MERGE INTO, and complex aggregations with window functions, subqueries, and joins. The architecture follows a storage-compute separation model: a metadata service (Meta) manages schemas, users, and catalogs via a Raft-based consensus layer, while query nodes handle SQL parsing, planning, optimization (Cascades optimizer with rule-based and cost-based passes), and distributed execution over a pipelined framework. Multiple storage engines are supported, including a native columnar Fuse engine optimized for cloud object stores (S3, MinIO, Azure Blob), as well as external formats like Iceberg, Delta Lake, Hive, Paimon, Parquet, and ORC. The system exposes access via MySQL wire protocol, HTTP API, Flight SQL, and gRPC, and includes enterprise features such as data masking, row access policies, materialized views, streams, network policies, and role-based access control.
Fpart is a high-performance C CLI tool that partitions filesystem trees into balanced subsets (called partitions) based on file count, size, or a fixed number of partitions. It uses a bin-packing algorithm to optimize space utilization. The tool supports live mode, where partitions are generated during filesystem crawling and hooks can trigger immediate actions (e.g., starting rsync transfers). Fpsync, a companion shell script, leverages fpart to parallelize data migrations using rsync, cpio, pax, or tar, with support for SSH workers, job queues, resume/replay, and email reports. The codebase is mature, well-structured, and portable across FreeBSD, Linux, Solaris, and macOS. It demonstrates strong C craftsmanship with careful memory management, error handling, and portability workarounds (e.g., embedded fts). The fpsync script is a sophisticated example of shell-based job scheduling and distributed synchronization.
Etl-engine is a Java-based ETL (Extract, Transform, Load) library designed for high-performance data synchronization. It provides a lightweight, extensible framework built around three core components: Node (data sources/destinations/transformations), Pipe (thread-safe buffered queues for data transfer), and Dataflow (orchestrator for pipeline execution). The library supports various data sources including JDBC databases (PostgreSQL, MySQL, Oracle, ClickHouse, DuckDB) and file outputs, with built-in capabilities for batch processing, automatic error handling/retry, and detailed logging. The architecture follows a producer-consumer pattern using Java's concurrency utilities, enabling efficient parallel data processing. Sample implementations demonstrate common ETL scenarios like database-to-database transfers, conditional data routing, and value transformation.