Steps
Step 1
Data Collection
This step conducts big data platform architecture design and environment setup, laying infrastructure foundation for subsequent data processing. Big data platform needs to support massive data storage, computation and analysis, requiring reasonable technical architecture design. Select appropriate big data components, including distributed file systems, computing engines, message queues, databases, etc. Build cluster environment, configure each component, ensure stable system operation. Architecture design needs to consider balance of scalability, reliability, performance and cost.
• Architecture design and technology selection: Lambda/Kappa architecture selection, HDFS/OSS storage, Spark/Flink computing engine, Kafka message queue
• Cluster environment setup: Hadoop cluster deployment, Hive/HBase configuration, Spark installation and tuning, ZooKeeper coordination service
• Platform basic services: data access service, resource scheduling (YARN/K8s), monitoring and alerting (Prometheus/Grafana), permission management
Deliverable: Big data platform solution (architecture design, deployment document, platform environment) | Quality standard: Reasonable architecture, usable environment, coordinated components
Step 2
HDFS Storage Design
This step conducts data collection and data warehouse construction, building complete data pipeline for big data. Data collection is the entry point of big data, requiring integration of multiple data sources to achieve efficient and reliable data transmission. Design data warehouse model, store data in layers (ODS/DWD/DWS/ADS). Implement ETL/ELT processes for data cleaning, transformation, loading. Establish data lineage tracking and data quality monitoring system to ensure credibility and availability of data assets.
• Data collection and access: batch collection (Sqoop/DataX), real-time collection (Flume/Logstash), CDC data synchronization (Debezium/Canal)
• Data warehouse modeling: dimensional modeling (star/snowflake schema), layered design (ODS/DWD/DWS/ADS), metric system design
• Data development and quality: ETL development (Hive SQL/Spark SQL), data quality monitoring, data lineage, metadata management
Deliverable: Data warehouse solution (data pipeline, warehouse model, ETL code, data quality report) | Quality standard: Smooth pipeline, standardized model, trusted data
Step 3
Distributed Computing
This step conducts big data processing and analysis mining, extracting value and insights from data. Use distributed computing engines for massive data processing, including batch processing and stream processing. Conduct statistical analysis, user profiling, recommendation algorithms, prediction models and other data analysis and mining work. Use Spark MLlib for machine learning modeling, use Flink for real-time computing. Optimize data processing performance, solve common problems like data skew and resource tuning.
• Data processing and computing: Spark batch processing, Flink stream processing, SQL optimization, performance tuning, data skew governance
• Data analysis and mining: user profiling, tag system, recommendation algorithms (collaborative filtering/Content-based), A/B testing analysis
• Machine learning application: feature engineering, model training (Spark MLlib), model evaluation, model serving
Deliverable: Data analysis results (processing scripts, analysis report, model results, visualization reports) | Quality standard: Efficient processing, in-depth analysis, valuable results
Step 4
Stream Processing & Visualization
This step conducts data visualization and reporting system construction, presenting analysis results intuitively. Data visualization is a keystep of data value delivery, requiring clear, beautiful and easy-to-use visualization solutions. Select appropriate visualization tools (ECharts/D3.js/Superset), build data dashboards and reporting systems. Design various visualization forms such as dashboards, trend charts, heatmaps, maps, etc. Ensure accurate data, friendly interaction, fast response, supporting business decision-making and data insights.
• Visualization solution design: metric dashboard design, chart selection (line/bar/pie/map/sankey), interaction design, responsive layout
• Tool implementation: BI tools (Superset/Tableau/Power BI), frontend development (ECharts/D3.js), report platform construction
• Data productization: self-service analytics platform, data portal, permission control, report subscription, export function
Deliverable: Data visualization system (dashboard design, frontend code, reporting platform, user manual) | Quality standard: Clear visuals, smooth interaction, accurate data
Step 5
Optimization & Documentation
This step conducts platform operations optimization and project summary, completing final delivery of the big data project. Big data platform requires continuous operations support and performance optimization to ensure stable and efficient system operation. Establish monitoring and alerting mechanism, conduct performance tuning and troubleshooting. Summarize the entire project, sort out technical solutions and implementation experience. Outlook development trends and application prospects of big data technology. Write complete technical documents and project reports,assets knowledge assets.
• Platform operations and optimization: monitoring and alerting system, performance tuning (SQL/resource/parameters), troubleshooting, capacity planning, cost optimization
• Documentation and delivery: architecture document, operations manual, user manual, API document, code repository, data dictionary
• Summary and outlook: project retrospective, technologyassets, lessons learned, technology trend analysis, future improvement directions
Deliverable: Project deliverables (operations system, complete documents, summary report, technologyassets) | Quality standard: Stable platform, complete documentation, in-depth summary