Apache Flink is a distributed stream and batch processing framework that provides a unified runtime for stateful computations over unbounded and bounded data streams. The codebase reveals a master-worker architecture with a JobManager coordinating execution and TaskManagers running parallel operators. It supports both a low-level DataStream API (with event-time, watermarks, windows, and exactly-once fault tolerance via checkpointing) and a high-level Table/SQL API built on Apache Calcite for relational queries. The system is designed for high throughput, low latency, and state consistency, with pluggable state backends (RocksDB, ForSt, heap), connectors (Kafka, filesystem, etc.), and deployment targets (standalone, YARN, Kubernetes). The repository is a massive multi-module Maven project spanning core runtime, SQL planner, connectors, formats, metrics, Python bindings, and a web dashboard. Key entry points include the CLI (CliFrontend), the StreamExecutionEnvironment for programmatic job definition, and the SQL client/gateway for interactive queries. Data flows through sources (e.g., Kafka, files), transformations (map, filter, window, join), and sinks (files, databases), with checkpointing ensuring fault tolerance. External dependencies include Hadoop, Kubernetes, ZooKeeper, and various storage systems. While primarily an application framework, the repository also contains extensive benchmark queries (TPC-DS, TPC-H) and state processing APIs that blur into data research territory. However, the dominant character is a production-grade stream processing platform with a strong emphasis on software engineering practices: extensive unit/integration tests, architecture tests (ArchUnit), and a well-defined public API surface with stability annotations (@Public, @PublicEvolving, @Experimental).
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.
This repository contains the lab materials for the AWS Data Engineering Immersion Day, a guided workshop demonstrating how to build a data lake and analytics pipeline on AWS using services like AWS Database Migration Service (DMS), AWS Glue, Amazon Athena, Amazon QuickSight, Amazon Redshift Spectrum, and AWS Lake Formation. The repo includes step-by-step PDF lab guides, CloudFormation templates for infrastructure provisioning, sample data loading scripts, and a self-contained anomaly detection example using Apache Flink with Random Cut Forest. While the Flink streaming job is a real application, the primary purpose of the repo is instructional, not a production deployment or a self-contained research project. The data lineage involves ingesting ticketing data from a PostgreSQL database via DMS into S3, transforming with Glue into Parquet, and analyzing with Athena/Redshift. Clickstream data is generated into Kinesis streams and processed in real-time with Kinesis Analytics (Flink) to detect anomalies. The repo is hybrid: it has both application code (Flink Java) and data pipeline orchestration, but the dominant character is a workshop with infrastructure-as-code and documentation.
This is a large, sprawling collection of tutorial and demo projects authored by a single developer (blogger '程序员欣宸'). It covers dozens of technologies including Spring Boot internals, Flink, Kafka, gRPC, JavaCV/OpenCV, Kubernetes controllers, LangChain4j, JUnit 5, and more. Each subdirectory is an independent, self-contained demo typically with a single main class or test. The code is educational: full of Chinese comments, simplified logic, and hardcoded paths. While not a cohesive application, it serves as a rich reference for learning how to use specific frameworks and patterns. The quality is average overall—functional but boilerplate-heavy, with many outdated dependencies and duplicated code across projects.