# turboway/bigdata_analyse

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5,238 stars · 780 forks · Python · MIT

## Links

- GitHub: https://github.com/TurboWay/bigdata_analyse
- awesome-repositories: https://awesome-repositories.com/repository/turboway-bigdata-analyse.md

## Topics

`hql` `python` `sql`

## Description

This project is a collection of big data frameworks and pipelines, including an Apache Hive analysis framework, a behavioral data analytics platform, a predictive analytics engine, and real-time data pipelines. It provides the infrastructure for building Extract, Transform, Load (ETL) workflows to process large datasets for distributed storage and SQL-based analysis.

The system supports diverse analytical implementations, such as a predictive engine using linear regression for value forecasting and a real-time architecture that moves data through message brokers for immediate reporting. It includes specialized capabilities for user behavior analytics, e-commerce performance measurement, and urban transit data analysis.

The codebase covers a broad surface of data engineering and analysis, including data cleansing and transformation, distributed data ingestion, window-based stream processing, and the visualization of results through business intelligence tools. It further enables the calculation of specific business metrics like conversion rates, monetization performance, and user engagement levels.

## Tags

### Part of an Awesome List

- [Big Data Engineering](https://awesome-repositories.com/f/awesome-lists/data/big-data-engineering.md) — Builds end-to-end workflows to clean, transform, and load massive datasets into distributed storage for high-performance querying.
- [Transit Data Analysis](https://awesome-repositories.com/f/awesome-lists/data/transit-data-analysis.md) — Analyzes commuting patterns and transit efficiency to optimize public transportation networks. ([source](https://github.com/TurboWay/bigdata_analyse/blob/main/SZTcard/%E6%B7%B1%E5%9C%B3%E9%80%9A%E5%88%B7%E5%8D%A1%E6%95%B0%E6%8D%AE%E5%88%86%E6%9E%90.md))
- [Charts and Visualization](https://awesome-repositories.com/f/awesome-lists/data/charts-and-visualization.md) — Transforms processed data into visual reports and charts using plotting libraries and BI tools. ([source](https://github.com/turboway/bigdata_analyse#readme))
- [Data Quality](https://awesome-repositories.com/f/awesome-lists/data/data-quality.md) — Ensures data quality in large datasets by removing duplicate records and standardizing timestamp formats. ([source](https://github.com/TurboWay/bigdata_analyse/blob/main/UserBehaviorFromTaobao_Batch/%E7%94%A8%E6%88%B7%E8%A1%8C%E4%B8%BA%E6%95%B0%E6%8D%AE%E5%88%86%E6%9E%90.md))

### Business & Productivity Software

- [User Monetization Metrics](https://awesome-repositories.com/f/business-productivity-software/corporate-revenue-analysis/user-monetization-metrics.md) — Computes key financial metrics including average revenue per user and total spend to assess revenue health. ([source](https://github.com/TurboWay/bigdata_analyse/blob/main/AgeOfBarbarians/%E9%87%8E%E8%9B%AE%E6%97%B6%E4%BB%A3%E6%95%B0%E6%8D%AE%E5%88%86%E6%9E%90.md))
- [E-commerce Performance Analytics](https://awesome-repositories.com/f/business-productivity-software/e-commerce-performance-analytics.md) — Measures conversion rates and ranks product performance to evaluate monetization and sales health.
- [Marketing User Segmentation](https://awesome-repositories.com/f/business-productivity-software/marketing-user-segmentation.md) — Provides a method for scoring users based on purchase recency and frequency to create targeted marketing segments. ([source](https://github.com/TurboWay/bigdata_analyse/blob/main/UserBehaviorFromTaobao_Batch/%E7%94%A8%E6%88%B7%E8%A1%8C%E4%B8%BA%E6%95%B0%E6%8D%AE%E5%88%86%E6%9E%90.md))
- [Business Metric Visualizations](https://awesome-repositories.com/f/business-productivity-software/business-metric-visualizations.md) — Creates visual reports and dashboards to communicate business analysis results and trends.

### Data & Databases

- [Behavioral Analytics](https://awesome-repositories.com/f/data-databases/behavioral-analytics.md) — Tracks activity patterns and engagement metrics to identify growth trends and segment users by value.
- [Conversion Rate Metrics](https://awesome-repositories.com/f/data-databases/conversion-rate-metrics.md) — Measures the transition rate of users between interaction stages such as viewing, adding to cart, and purchasing. ([source](https://github.com/TurboWay/bigdata_analyse/blob/main/UserBehaviorFromTaobao_Batch/%E7%94%A8%E6%88%B7%E8%A1%8C%E4%B8%BA%E6%95%B0%E6%8D%AE%E5%88%86%E6%9E%90.md))
- [User Growth Analysis](https://awesome-repositories.com/f/data-databases/data-acquisition-workflows/acquisition-channel-analysis/user-growth-analysis.md) — Tracks total user growth and registration timing to identify growth peaks and acquisition trends. ([source](https://github.com/TurboWay/bigdata_analyse/blob/main/AgeOfBarbarians/%E9%87%8E%E8%9B%AE%E6%97%B6%E4%BB%A3%E6%95%B0%E6%8D%AE%E5%88%86%E6%9E%90.md))
- [Data Analysis Workflows](https://awesome-repositories.com/f/data-databases/data-analysis-workflows.md) — Provides comprehensive workflows for cleaning, transforming, and querying large datasets to extract business insights. ([source](https://github.com/turboway/bigdata_analyse#readme))
- [ETL Workflows](https://awesome-repositories.com/f/data-databases/data-pipeline-orchestration/etl-workflows.md) — Implements workflows for extracting, transforming, and loading raw JSON and CSV files into structured data warehouses.
- [Distributed SQL Analysis Frameworks](https://awesome-repositories.com/f/data-databases/distributed-sql-analysis-frameworks.md) — Provides a comprehensive system for cleaning and querying large datasets using Hive for distributed storage.
- [Engagement Analytics](https://awesome-repositories.com/f/data-databases/engagement-analytics.md) — Calculates average online time and activity levels to compare behavior between different user segments. ([source](https://github.com/TurboWay/bigdata_analyse/blob/main/AgeOfBarbarians/%E9%87%8E%E8%9B%AE%E6%97%B6%E4%BB%A3%E6%95%B0%E6%8D%AE%E5%88%86%E6%9E%90.md))
- [Product Performance Ranking](https://awesome-repositories.com/f/data-databases/product-performance-ranking.md) — Ranks items and categories by sales and interaction volume to identify top-performing products. ([source](https://github.com/TurboWay/bigdata_analyse/blob/main/UserBehaviorFromTaobao_Batch/%E7%94%A8%E6%88%B7%E8%A1%8C%E4%B8%BA%E6%95%B0%E6%8D%AE%E5%88%86%E6%9E%90.md))
- [Real-Time Data Streaming](https://awesome-repositories.com/f/data-databases/real-time-data-streaming.md) — Implements a streaming architecture that moves live information from sources through processing engines for immediate reporting. ([source](https://github.com/turboway/bigdata_analyse#readme))
- [Traffic Analysis](https://awesome-repositories.com/f/data-databases/traffic-analysis.md) — Calculates total and daily page views and unique visitors to monitor activity trends. ([source](https://github.com/TurboWay/bigdata_analyse/blob/main/UserBehaviorFromTaobao_Batch/%E7%94%A8%E6%88%B7%E8%A1%8C%E4%B8%BA%E6%95%B0%E6%8D%AE%E5%88%86%E6%9E%90.md))
- [User Behavior Analysis](https://awesome-repositories.com/f/data-databases/user-behavior-analysis.md) — Analyzes user activity patterns to determine peak activity hours and weekly distributions of user actions. ([source](https://github.com/TurboWay/bigdata_analyse/blob/main/UserBehaviorFromTaobao_Batch/%E7%94%A8%E6%88%B7%E8%A1%8C%E4%B8%BA%E6%95%B0%E6%8D%AE%E5%88%86%E6%9E%90.md))
- [Commuting Pattern Analysis](https://awesome-repositories.com/f/data-databases/commuting-pattern-analysis.md) — Calculates total trips, expenditure, and peak travel hours to identify public transportation usage trends. ([source](https://github.com/TurboWay/bigdata_analyse/blob/main/SZTcard/%E6%B7%B1%E5%9C%B3%E9%80%9A%E5%88%B7%E5%8D%A1%E6%95%B0%E6%8D%AE%E5%88%86%E6%9E%90.md))
- [Data Format Transformations](https://awesome-repositories.com/f/data-databases/data-format-transformations.md) — Transforms raw JSON formatted source data into cleaned CSV files for downstream analytical processing. ([source](https://github.com/TurboWay/bigdata_analyse/blob/main/SZTcard/%E6%B7%B1%E5%9C%B3%E9%80%9A%E5%88%B7%E5%8D%A1%E6%95%B0%E6%8D%AE%E5%88%86%E6%9E%90.md))
- [Data Warehouse Integrations](https://awesome-repositories.com/f/data-databases/data-warehouse-integrations.md) — Imports processed datasets into distributed storage systems to support large-scale querying and analysis. ([source](https://github.com/TurboWay/bigdata_analyse/blob/main/AmoyJob/2021%E5%8E%A6%E9%97%A8%E6%8B%9B%E8%81%98%E6%95%B0%E6%8D%AE%E5%88%86%E6%9E%90.md))
- [Analysis Dataset Optimization](https://awesome-repositories.com/f/data-databases/dataset-preparation-scripts/analysis-dataset-optimization.md) — Includes processes to merge data files and filter fields to optimize memory usage before loading into databases. ([source](https://github.com/TurboWay/bigdata_analyse/blob/main/AgeOfBarbarians/%E9%87%8E%E8%9B%AE%E6%97%B6%E4%BB%A3%E6%95%B0%E6%8D%AE%E5%88%86%E6%9E%90.md))
- [Time-Window Aggregations](https://awesome-repositories.com/f/data-databases/event-time-processing/time-window-aggregations.md) — Calculates real-time metrics by grouping continuous data streams into discrete time intervals using windowing functions.
- [Hive Data Ingestion](https://awesome-repositories.com/f/data-databases/hive-data-ingestion.md) — Loads raw CSV datasets into Hive tables using defined schemas for large-scale processing. ([source](https://github.com/TurboWay/bigdata_analyse/blob/main/UserBehaviorFromTaobao_Batch/%E7%94%A8%E6%88%B7%E8%A1%8C%E4%B8%BA%E6%95%B0%E6%8D%AE%E5%88%86%E6%9E%90.md))
- [Dataset Cleaning](https://awesome-repositories.com/f/data-databases/large-scale-data-computation/dataset-cleaning.md) — Merges multiple raw data files and cleans datasets before persisting the structured results to a relational database. ([source](https://github.com/TurboWay/bigdata_analyse/blob/main/RentFromDanke/%E7%A7%9F%E6%88%BF%E6%95%B0%E6%8D%AE%E5%88%86%E6%9E%90.md))
- [Relational Database Persistence](https://awesome-repositories.com/f/data-databases/relational-database-persistence.md) — Cleans and merges raw datasets before persisting them in a relational database to maintain structured data integrity.
- [Distributed SQL Loading](https://awesome-repositories.com/f/data-databases/schema-column-mapping/distributed-sql-loading.md) — Imports cleaned CSV files into a distributed SQL engine by mapping source columns to predefined table schemas.
- [Stream Enrichment](https://awesome-repositories.com/f/data-databases/stream-enrichment.md) — Augments real-time event streams by joining them with reference data from external databases to add descriptive metadata.
- [Streaming Metric Analysis](https://awesome-repositories.com/f/data-databases/streaming-metric-analysis.md) — Analyzes real-time data streams using windowing functions to calculate transaction volumes and unique users. ([source](https://github.com/TurboWay/bigdata_analyse/blob/main/UserBehaviorFromTaobao_Stream/%E7%94%A8%E6%88%B7%E8%A1%8C%E4%B8%BA%E6%95%B0%E6%8D%AE%E5%AE%9E%E6%97%B6%E5%88%86%E6%9E%90.md))

### Software Engineering & Architecture

- [Distributed File Systems](https://awesome-repositories.com/f/software-engineering-architecture/distributed-systems/distributed-data-management/distributed-storage-clusters/distributed-file-systems.md) — Implements distributed file system storage to enable high-performance querying and parallel processing of large-scale datasets.
- [Distributed Data Loaders](https://awesome-repositories.com/f/software-engineering-architecture/distributed-systems/distributed-data-management/distributed-storage-clusters/distributed-file-systems/distributed-data-loaders.md) — Provides a process for uploading cleaned files to a distributed file system and loading them into a SQL engine. ([source](https://github.com/TurboWay/bigdata_analyse/blob/main/SZTcard/%E6%B7%B1%E5%9C%B3%E9%80%9A%E5%88%B7%E5%8D%A1%E6%95%B0%E6%8D%AE%E5%88%86%E6%9E%90.md))

### Artificial Intelligence & ML

- [Data Cleansing](https://awesome-repositories.com/f/artificial-intelligence-ml/dataset-preparation-utilities/data-cleansing.md) — Provides capabilities for removing duplicates and filling missing values to prepare raw data for analysis. ([source](https://github.com/TurboWay/bigdata_analyse/blob/main/AmoyJob/2021%E5%8E%A6%E9%97%A8%E6%8B%9B%E8%81%98%E6%95%B0%E6%8D%AE%E5%88%86%E6%9E%90.md))
- [Linear Regression](https://awesome-repositories.com/f/artificial-intelligence-ml/linear-regression.md) — Utilizes linear regression modeling to forecast numerical outcomes based on historical data patterns.
- [Linear Regression Models](https://awesome-repositories.com/f/artificial-intelligence-ml/linear-regression-models.md) — Uses linear regression models to forecast numerical outcomes based on specific input dimensions. ([source](https://github.com/TurboWay/bigdata_analyse/blob/main/AmoyJob/2021%E5%8E%A6%E9%97%A8%E6%8B%9B%E8%81%98%E6%95%B0%E6%8D%AE%E5%88%86%E6%9E%90.md))
- [Prediction Engines](https://awesome-repositories.com/f/artificial-intelligence-ml/model-predictions/prediction-engines.md) — Provides an architectural engine that uses linear regression and value models to forecast numerical outcomes.

### Networking & Communication

- [Message Broker Producers](https://awesome-repositories.com/f/networking-communication/message-broker-producers.md) — Simulates real-time event flows by writing data from files into a message broker for downstream consumption. ([source](https://github.com/TurboWay/bigdata_analyse/blob/main/UserBehaviorFromTaobao_Stream/%E7%94%A8%E6%88%B7%E8%A1%8C%E4%B8%BA%E6%95%B0%E6%8D%AE%E5%AE%9E%E6%97%B6%E5%88%86%E6%9E%90.md))