The possibility of plagiarism in this project is not too great, because plagiarism can only be the requirements, and only part of the code can be copied, because there are several different codes in several issues. Let's see what the project talks about?
Comprehensive Project-Module 1-Data Warehouse-day01
01. Pre-knowledge of project development--git version management--gitee code cloud-submit-pull-branch operation.wmv
02. Project background introduction.wmv
03. Project background introduction (2).wmv
04. Project Module 1-Introduction to Data Warehouse-Dictionary Data Construction Requirements.wmv
05. Clarify the concepts of database and data warehouse.wmv
06. Project development project skeleton construction and testing.wmv
07. Project development-geographic location dictionary construction-geohash coding principle and toolkit.wmv
08. Project development-geographic location dictionary construction-code implementation (1).wmv
09. Project development-business district dictionary construction-code implementation.wmv
10. Project development-company internal data-detailed analysis of traffic log.wmv
11. Project development-internal data preprocessing-requirements description.wmv
12. Project development-internal data preprocessing-code skeleton writing.wmv
13. Introduction to AutoNavi Geolocation Service API.wmv
14. Gaode geographic location service api-write demo example.wmv
Comprehensive Project-Module 1-Data Warehouse-day02
01. Implementation of internal traffic log preprocessing code (1).wmv
02. Implementation of internal traffic log preprocessing code (2).wmv
03. Implementation of internal traffic log preprocessing code (3).wmv
04. Customize the schema to let spark automatically parse the json data into dataframe.wmv
05. Explanation of data warehouse modeling thinking-business-demand-theme-layering.wmv
06. Data warehouse ods layer modeling-table building-data loading-detection.wmv
Comprehensive Project-Module 1-Data Warehouse-day03
01.SQL key grammar review and combing.wmv
02. Traffic analysis-dwd_traffic_log table processing generation.wmv
03. Traffic analysis-dwd_traffice_agg_session session level schedule.wmv
04. Traffic analysis-traffic profile dimension report-ads_traffic_summary_cube.wmv
05. User analysis-modeling design-detailed process. Wmv extra: how to copy Tao Ge's CDH virtual machine cluster and network configuration.wmv
Extra: Supplement two small hive skills-multiple insertion-dynamic partition.wmv
Comprehensive Project-Module 1-Data Warehouse-day04
01.Olap data cube multi-dimensional analysis--hive higher-order aggregation function--groupingset--cube.wmv
02.olap data cube multi-dimensional analysis--hive higher-order aggregation function--grouping__id-rollup.wmv
03. User analysis-daily new dws_user_dnu-daily active dws_user_dau-history dws_user_hisu-table development.wmv
04. User analysis-multi-dimensional report on the number of new people in a day-ads_user_dnu_cube.wmv
05. User analysis-daily new daily life plus dimension (week-month-quarter)-automated shell script development.wmv
06. As of today's etl process combing-automated script development.wmv
Comprehensive Project-Module 1-Data Warehouse-day05
01. Errata: List of historical user records -fulljoin-Forget to write conditions.wmv
02. Script development for all tasks as of today (2).wmv
03. Script general scheduling development.wmv
04. User analysis-retention analysis-modeling design.wmv
05. User analysis-retention analysis-retention schedule calculation.wmv
06. User analysis-active zipper table-modeling and calculation process.wmv
07. User analysis-active zipper table-code writing.wmv
Comprehensive Project-Module 1-Data Warehouse-day06
01. User retention analysis-modeling design-operation logic-zipper table calculation logic review.wmv
02. Report development-overall trend report-model design-calculation process combing.wmv
03. Report development-overall trend report-ads_overall_trend development.wmv
04. Report development-user freshness report-ads_user_fresh modeling.wmv
05. Report development-user freshness report-ads_user_fresh development.wmv
06. Report development-user active retention report-ads_user_act_retention.wmv
07. Report development-user active retention report-solution 2-with-as must be written first.wmv
08. Report development-active user composition analysis report (continuous days)-ads_user_act_ingredients.wmv
Comprehensive Project-Module 1-Data Warehouse-day07
01. Report statistics-user interval distribution statistics-ads_user_interval-spark task realization.wmv
02. Report statistics-user interval distribution statistics-ads_user_interval-sql implementation.wmv
03. Event analysis theme-background introduction to event log data acquisition.wmv
04. Event analysis theme-detailed concept of conversion rate (funnel model)-demand analysis.wmv
05. Event analysis theme-DWD layer modeling etl-dwd_event_detail.wmv
06. Event Analysis-Event Overview Report-ads_event_overall.wmv
Extra: Detailed explanation of mapreduce-shuffle ring buffer.wmv
Extra: Detailed explanation of yarn's three resource scheduling strategies.wmv
Comprehensive Project-Module 1-Data Warehouse-day08
01. Access path analysis-dwd layer path analysis schedule-dwd_routes_detail.wmv
02. Access path analysis-ads layer path analysis report-ads_routes_rpts.wmv
03. Analysis of business path conversion rate-modeling-calculation thinking design.wmv
04. Business path conversion funnel analysis-code implementation-ads_routes_step_detail.wmv
05. Advertising effect analysis theme-DWS and ADS layer modeling design.wmv
06. Advertising effect analysis-ads layer report-advertising overview report-ads_ad_overall development and implementation.wmv
07. Pull new activity effect analysis report.wmv
08. Development and realization of the effect analysis of preferential activities.wmv
Comprehensive Project-Module 1-Data Warehouse-day09
01. Data migration tool sqoop-installation-import mysql to hdfs.wmv
02. Data migration tool sqoop-import mysql to hdfs-specify conditions-incremental import-free query.wmv
03. Data migration tool-sqoop- import mysql data to hive.wmv
04. Data migration tool-sqoop-export data to mysql.wmv
05. Business data analysis-data migration-user_info import script development.wmv
06. Data migration-script development-sales analysis-modeling design.wmv
07. Order analysis-turnover analysis report-ads_order_amt_cube.wmv
08. Order analysis--GMV multi-dimensional analysis report.wmv
09. Order analysis-category analysis report.wmv
Comprehensive Project-Module 2 - User Portrait - Day01
- Analysis of Big Data Applications in Various Industries
- User portrait project background introduction-label system analysis
- User Portrait Project--Data Introduction--DSP Business Department Data
- User portrait project-data introduction-company internal data-DSP business department data
- User portrait project-data introduction-cloud operator traffic data
- Analysis of the overall process of user portrait project development
- Introduction to the core concepts of graph computing-graph-point-edge-directed-ring-degree-connected subgraph-point edge data structure
- An introduction to graph computing-finding connected subgraphs
- An Introduction to Graph Computing--Find Connected Subgraphs (2)
Comprehensive Project-Module 2 - User Portrait - Day02
- Graph computing entry case contact 2
- Project development-id mapping dictionary-issue requirements-calculation process analysis
- Project development-id mapping dictionary construction (T day initial construction)
- Project development-id mapping dictionary construction (T+1 day)(1)
Comprehensive Project-Module 2 - User Portrait - Day03
- id-mapping code implementation (2)-group id adjustment
- Id-mapping code implementation (3)-transform into calculation of real data
- dsp data preprocessing development (1)
- Analysis of the overall structure of the integrated project (1)
- User portrait-dsp log preprocessing-code implementation
- User portrait-dsp extra-kpi report statistics
- User portrait-dsp extra-kpi report statistics (sql implementation version)-dataframe write mysql
Comprehensive Project-Module 2 - User Portrait - Day04
- User portrait-doit traffic log preprocessing
- User portrait-doit traffic log preprocessing (2)
- User portrait-cmcc traffic log processing-crawler background introduction
- Introduction to crawlers-jsoup function introduction-JD outdoor category crawler examples (1)
- Getting started with crawlers-JD outdoor category crawling development (2)
Comprehensive Project-Module 2 - User Portrait - Day05
- User portrait-preprocessing-cmcc traffic log preprocessing
- User Portrait-Tag Extraction-Tag Structure Review-Tag Programming Model Design
- User portrait-label extraction-analysis of label calculation strategy process
- User portrait-DSP label extraction-label score statistics
- User portrait-DSP label extraction-label gathering by person (1)
- User portrait-DSP label extraction-labels gathered by gid (1)
- User Portrait-DOIT Label Extraction-Duoyi Label-Data Warehouse Statistics
- User portrait-DOIT tag extraction-Duoyi tag-log data extraction
- User Portrait-DOIT Label Extraction-Duoyi Label-Data Warehouse Report Data Extraction
Comprehensive Project-Module 2 - User Portrait - Day06
- User portrait-cmcc label extraction
- User portrait-multi-source label aggregation and merging-multi-layer map assembly
- User portrait-multi-source tag aggregation merge-tag bean merge-bean to json
- User Portrait-Two-day Label Attenuation Merger-Requirements Description-Process Design
- User portrait-two-day label attenuation merge-code implementation-label jsonization
Recommendation algorithm
Comprehensive Project-Module 3 - Recommendation Algorithm - Day01
- Introduction to Recommendation System-Popularity Recommendation-Portrait Recommendation-Algorithm Recommendation
- Introduction to machine learning algorithms--knn classification-kmeans clustering-supervised learning-unsupervised learning-semi-supervised learning
- The core foundation of machine learning algorithms-feature vector model (sparse vector-dense vector)
- The core foundation of machine learning algorithm-actual case of item vectorization (1)
- CB recommendation-recommendation algorithm based on content similarity-to achieve the overall process architecture
- NLP algorithm model-TF-IDF feature value calculation-text vectorization
- NLP algorithm model-TF-IDF text vectorization actual combat
- Classification algorithm--Naive Bayesian teaching ideas and formula derivation
- Classification algorithm-Naive Bayes-model training and prediction code implementation
- Classification algorithm-Naive Bayes-model training and prediction code implementation
- Project combat-Naive Bayes classification of comment data sets
Comprehensive Project-Module 3 - Recommendation Algorithm - Day02
- Process review based on content similarity recommendation calculation
- Recommendation based on content similarity-code implementation (1)
- Recommendation based on content similarity-code implementation (2)
- Recommendation based on content similarity-code implementation (3)
- Collaborative filtering recommendation algorithm-principle of algorithm idea
- Collaborative filtering algorithm-algorithm code implementation-result display
- Model label calculation-Churn rate label-Naive Bayes application-vector normalization
Comprehensive Project-Module 4 - Flink Real-Time Computing- Day01
- Flink knowledge review
- Flink restart strategy
- Flink restarts strategy test
- Flink integrates KafkaSource
- Flink integrates KafkaSource to achieve Exactly-Once
- Flink integrates RedisSink
- Customize MysqlSink
Comprehensive Project-Module 4 - Flink Real-Time Computing- Day02
- Flink content review
- Submit Flink tasks in the cluster
- Flink's StandAlone execution process
- Detailed explanation of FlinkOnYarn execution process
- Flink's stage division principle
- Flink recovers data from checkpoint
- Project engineering initialization
- FlinkUtils tool class package
Comprehensive Project-Module 4 - Flink Real-Time Computing- Day03
- review
- Real-time computing business architecture
- Real-time computing business architecture upgrade
- Nginx installation
- Installation of OpenResty
- Log collection server
- Talk about Nginx data collection into Kafka
- Log collection data test
- Data real-time ETL
- Flow measurement output
- Realization of flow measurement output
- Customize RedisSink
- Multi-dimensional statistics of participation times
Comprehensive Project-Module 4 - Flink Real-Time Computing- Day04
- Knowledge review
- Real-time project structure combing
- Introduction to canal
- Installation and use of canal
- Order data analysis requirements
- Flink statistics order data
- Flink window delay join
Comprehensive Project-Module 4 - Flink Real-Time Computing- Day05
- Flink obtains the lost data of the window through the flow measurement output
- Left join and get delayed data
- Flink two streams join
- Realized in order and schedule
- Project knowledge point review
- ProtoBuffer combined with Flink optimization