├── DWH ├── Chapter 1.md ├── Chapter 2.md ├── Chapter 3.md ├── Chapter 4.md ├── Chapter 5.md ├── Chapter 6.md ├── Chapter 7.md └── Chapter 8.md ├── LICENSE ├── README.md ├── datalake └── Chapter 1.md └── img ├── dwh 1.png └── dwh 2.png /DWH/Chapter 1.md: -------------------------------------------------------------------------------- 1 | ## Definition 2 | 3 | A Data Warehouse is a **subject-oriented**, **integrated**, **non-volatile**, and **time-variant** collection of data in support of management’s decisions. 4 | - subject-oriented：Data warehouses are all based on a specific theme, and only data related to the theme are needed, and other irrelevant detailed data will be excluded. 5 | - integrated：The data in the data warehouse is obtained through systematic processing, summarization and arrangement on the basis of extracting and cleaning the source business system data. Inconsistencies in the source data must be eliminated to ensure that the information within the data warehouse is a consistent global picture of the entire enterprise. 6 | - time-variant：The data in the data warehouse usually contains historical information. The system records the information of the enterprise from a certain point in the past (such as the time when the data warehouse was started) to the current stage. Through this information, the development process and future trends of the enterprise can be analyzed. Make quantitative analysis and forecasts. 7 | - non-volatile：The data in the data warehouse is mainly used for enterprise decision-making and analysis, and the data operations involved are mainly data queries. Once a certain data enters the data warehouse, it will generally be retained for a long time, that is, there are generally a large number of query operations in the data warehouse. , but there are few modification and deletion operations, usually only regular loading and refreshing are required. -------------------------------------------------------------------------------- /DWH/Chapter 2.md: -------------------------------------------------------------------------------- 1 | ## Top-level design 2 | 3 | ### Basic principles 4 | #### High cohesion and low coupling 5 | - Mainly consider from two perspectives of data business characteristics and access characteristics. 6 | - Design data with similar or related businesses and the same granularity as a logical or physical model. 7 | - Store data that is accessed at the same time with a high probability together and store data that is accessed at the same time with a low probability. 8 | 9 | #### Separation of core model and extension model 10 | - Establish the core model and extended model system. The fields included in the core model support commonly used core services, and the fields included in the extended model support the needs of individualization or a small number of applications. Do not let the fields of the extended model invade the core model too much, so as not to damage the structure of the core model Simplicity and maintainability. 11 | #### Submergence and Singularity of Common Processing Logic 12 | - The lower-level common processing logic should be encapsulated and implemented at the bottom level of data scheduling dependence. Do not expose common processing logic to the application layer, and do not allow common logic to exist in multiple places simultaneously. 13 | #### Balance between Cost and Performance 14 | - Appropriate data redundancy can be used to exchange query and refresh performance, but excessive redundancy and data replication should be avoided. 15 | #### Data Rollback 16 | - The processing logic remains unchanged, and the data results are determined to be unchanged if run multiple times at different times. 17 | #### Consistency, Clear and Understandable Naming 18 | - Fields with the same meaning in different tables must have the same name, and the names defined in the specification must be used. 19 | - Table naming must be clear and consistent, and table names must be easy for users to understand and use. 20 | ### Architecture Planning 21 | #### Architecture Methodology 22 | - TOGAF 23 | #### Construction Planning 24 | - Analyze the Current Situation 25 | - Determine Future Goals 26 | - Evaluate the Gap 27 | - Set Action Goals 28 | - Develop Construction Plans 29 | - Evaluate Costs and Benefits 30 | - Make Decisions、 31 | ### Evaluation Methods 32 | #### Benefit Indicators 33 | - Internal Rate of Return (IRR) and Risk 34 | - Costs: hardware costs, software costs, personnel costs, maintenance costs 35 | - Benefits: data assets, core indicators 36 | - Risk: risk assessment 37 | #### Technical Indicators 38 | ##### Performance and Completeness 39 | - Query performance, loading performance, permission function, monitoring function 40 | - Integration of databases, FTP, cloud services, and support for multiple interfaces 41 | ##### Model 42 | - Data reliability: stability, robustness, and security 43 | - Data reusability 44 | - Data accuracy: consistency 45 | - Response agility 46 | - Model efficiency: response speed, storage space 47 | ##### Usability 48 | - Data warehouses need to be able to connect to common software such as Excel and Tableau. 49 | - Users can flexibly develop and design reports independently. 50 | ##### Scalability 51 | - When data volume and users grow rapidly, the cost and convenience of upgrading hardware. 52 | ##### Reliability 53 | - High availability 54 | - Vendor support 55 | ##### Data Value 56 | - Query frequency (popularity), value coefficient 57 | -------------------------------------------------------------------------------- /DWH/Chapter 3.md: -------------------------------------------------------------------------------- 1 | ## Development preparation 2 | ### policy making 3 | #### Table Naming Convention 4 | | layer | Naming convention | 5 | |-------|---------------------------------------------------| 6 | | stg | stg_{source_db}_{source_table} | 7 | | ods | ods_{source_db}_{source_table} | 8 | | dw | dwd/dws_{domain}_{business_describe}_di/df | 9 | | dm | dm_{domain}_{business_describe}_xd/xm/xy | 10 | | ads | ads_{business_owner}_{business_describe}_xd/xm/xy | 11 | 12 | "di" indicates that the data is incrementally written, and "df" indicates that the data is fully written each time. 13 | 14 | "xd/xm/xy" generally refers to the update cycle, for example, "1d" means the update cycle is one day, "1m" is one month, and "7d" is seven days. 15 | 16 | #### Field Naming Convention 17 | 18 | Generally, a field name should consist of two parts: a prefix and a suffix. The prefix modifies the meaning of the suffix, and there can be multiple prefixes, but only one suffix. The prefix and suffix should be written in camel case with all lowercase letters, with words separated by underscores. Both the prefix and suffix should be written in English words or abbreviations, with the prefix being a verb, adjective, or noun, and the suffix being a noun. Except for specific English abbreviations, full English words should be used as much as possible for field names, and field length should be considered if it is too long. Also, the correct data type should be chosen for different fields, and sensitive fields should be encrypted. 19 | 20 | 21 | #### Storage Convention 22 | 23 | The data in the STG layer is stored in textfile format and retained for 7 days for easy data tracing. 24 | Other layers use orc format for storage. 25 | 26 | #### Development Process Specification 27 | 28 | - Requirement Application: The demand side submits an application description, which describes the business demand scope, data scope, statistical caliber, data quality requirements, etc. according to the document specification. 29 | - Requirement Research: Conduct business research and data analysis on the content of the requirement application. 30 | - Data Exploration: Based on the requirement research and the content of the requirement application, investigate the entities and attribute content, business processes, data processes, etc. in the business demand. Data exploration runs through the entire process of model design, continuously improving the rationality of model design, and ultimately generating a data exploration report. 31 | - Model Design: It includes three parts: conceptual model design, logical model design, and physical model design. 32 | - ETL Design: Based on the physical model, business logic, and data exploration results, write the data extraction and processing process, including field mapping, conversion methods, association conditions, etc. 33 | - Script Development and Testing: It is the specific program implementation of the MAPPING process completed in accordance with the prescribed script writing specifications. 34 | - Online Application: Please submit an online application on JIRA. 35 | - Model Online: Refers to the model versioning and online operation. 36 | - Model Operation: Refers to the maintenance, changes, and optimization of the model after it is online. 37 | 38 | ### Equipment Selection 39 | - First-generation data warehouses: Oracle, share disk/share everything (RAC limit: 3-15) 40 | - Second-generation data warehouses: MPP/Vertica, share nothing (MPP limit: 200*15T per node) 41 | - Third-generation data warehouses: Hadoop (Hadoop limit: about 5000 for commercial use) 42 | - Fourth-generation data warehouses: Snowflake, cloud-native, storage and computing separation -------------------------------------------------------------------------------- /DWH/Chapter 4.md: -------------------------------------------------------------------------------- 1 | ## Design Process 2 | ![](https://github.com/NorthShip/Data-Engineer-Learning/blob/main/img/dwh%201.png) 3 | 4 | ### Business Research 5 | Researching business processes, exploring data situations, and analyzing requirements. 6 | 7 | ###### Example table 8 | 9 | | Example Company | Trade | Logistics | Marketing | 10 | |-----------------|-------|-----------|-----------| 11 | | Target | | | | 12 | | Needs | | | | 13 | | Data | | | | 14 | | Application | | | | 15 | 16 | ### Division of Data Domain 17 | 18 | ###### Example table 19 | 20 | | Data Domain | Business Process | 21 | |-----------------|------------------| 22 | | Business data | | 23 | | Log data | | 24 | | Management data | | 25 | 26 | ### Defining Bus Matrix 27 | Data Domain, Business Process, Data Granularity, Dimension, Atomic Indicator 28 | ###### Example table 29 | 30 | ![](https://github.com/NorthShip/Data-Engineer-Learning/blob/main/img/dwh%202.png) 31 | 32 | ### Clarifying Statistical Indicators 33 | - Data domain: A collection that abstracts business processes or dimensions for business analysis. Business processes can be summarized as indivisible behavioral events, and indicators can be defined under business processes. A dimension refers to the environment in which a measurement is taken, such as a buyer placing an order, with the buyer being the dimension. To ensure the vitality of the entire system, data domains need to be abstracted and refined, and maintained and updated in the long term, but not changed lightly. When dividing data domains, they should not only cover all current business needs, but also include new business without affecting existing data domains and expanding new data domains. 34 | - Business process: Refers to the business activity events of an enterprise, such as ordering, payment, and refund. A business process is an indivisible behavioral event. In simple terms, a business process is an event in an enterprise's activities. 35 | - Time period: Used to clarify the time range or time point of data statistics, such as the last 30 days, natural week, end of the day, etc. 36 | - Modifier: Refers to the business scenario that limits the abstraction of indicators other than the statistical dimensions. Modifiers belong to a type of modification, such as PC-side, wireless-side, etc. under the access terminal type in the log domain. 37 | - Measurement/Atomic indicator: The atomic indicator has the same meaning as the measurement. It is a measure based on a certain business event behavior, and is an indicator that cannot be further divided in the business definition. It is a noun with a clear business meaning, such as payment amount. 38 | - Dimension: A dimension is the environment of measurement used to reflect a class of business attributes. The collection of such attributes constitutes a dimension, which can also be called an entity object. 39 | - Dimension attribute: A dimension attribute belongs to a dimension. For example, the country name, country ID, and province name in the geographical dimension are all dimension attributes. 40 | - Derived indicator: Derived indicator = Atomic indicator + Business Limitation + Time Period + Granularity Selection 41 | - Total mobile phone category orders by province in the last day = Total order amount + Mobile phone category + Last day + Provincial level 42 | - Derived ratio: Various statistical ratios and proportions. -------------------------------------------------------------------------------- /DWH/Chapter 5.md: -------------------------------------------------------------------------------- 1 | ## Data Warehouse Modeling 2 | ### Modeling Methodology 3 | Real world --> Symbolic world (Conceptual model) --> Data structure world (Logical model) --> Specific database (Physical model) 4 | 5 | - Conceptual model: Abstraction of the real world into a symbolic system (ER diagram: Entity, Attribute, Relationship) 6 | - Logical model: Abstraction of the symbolic system into a data structure (Relational model: Table | Hierarchical model: Document | Network model: Graph) 7 | - Physical model: Implementation of the logical model 8 | 9 | ### Data Warehouse Modeling Methods 10 | - Normalized modeling: ER diagram 11 | - Dimensional modeling: Star schema, Snowflake schema 12 | - Data Vault: It emphasizes the establishment of an auditable foundation data layer, which emphasizes the historical, traceable, and atomic nature of data, without requiring excessive consistency processing and integration of data; at the same time, it organizes enterprise data into a structured manner based on the theme concept and introduces further occurrence processing to optimize the model to cope with the scalability of the contingency system, Hub, Link, Satellite. 13 | ### Hierarchy and Theme 14 | #### Reasons for hierarchy 15 | 16 | - Decompose tasks 17 | - Encapsulate reuse 18 | - Isolate coupling 19 | - Trace data lineage 20 | 21 | #### Data buffer layer (ODS) 22 | Incremental storage: Incremental storage on a daily basis, with business date as partition, each partition stores daily incremental business data 23 | Full storage: Full storage on a daily basis, with business date as partition, each partition stores full snapshot 24 | Zipper storage: The zipper storage records all change data with a daily granularity by adding two timestamp fields (start_dt and end_dt), and the partition field is usually these two timestamp fields. 25 | 26 | #### Data Warehouse layer (DW) 27 | Detailed granularity fact layer (DWD): Divided into topics, the finest granularity (business perspective) 28 | Common granularity summary layer (DWS): Aggregated by time granularity (metric perspective) 29 | 30 | #### Data application layer (APP) 31 | Specific customized data applications 32 | 33 | #### Common Theme Model 34 | - FS-LDM 35 | - Broker Modeling Method IBR:The abstract model uses the "Identity-Behavior-Relevance" (IBR) design method to design. 36 | The identity (Identity) is the core, and all kinds of market behaviors (Behavior) are extended, and the relevant relationships (Relevance) are summarized. -------------------------------------------------------------------------------- /DWH/Chapter 6.md: -------------------------------------------------------------------------------- 1 | ## ETL 2 | ### Extraction 3 | #### 1.Data exploration system 4 | 5 | #### 2.Change data capture 6 | ##### Based on query 7 | ###### Timestamp 8 | - Absolute historical data table with timestamp and self-incrementing column:Directly insert based on timestamp or sequence value as marker position. 9 | - With operation time:Maintain the update field to determine new data. 10 | ###### Snapshot 11 | - Full comparison of featureless data tables:Based on unique constraints data comparison, if it does not exist, insert it. If it exists, compare whether the data in the key column is equal. If not, modify it. 12 | ##### Based on triggers 13 | ##### Based on log parsing 14 | 15 | #### 3.Extraction system 16 | - Load raw data all at once. 17 | - Solution for task failure. 18 | 19 | ### Cleansing 20 | #### Deduplication system 21 | ##### Handling stock duplicate data 22 | - using rowid (may need to use window functions to generate), group by, distinct to deduplicate in SQL. 23 | ##### Handling incremental duplication issues 24 | - Maintain the key column to the historical record table, and then compare the incremental data. Or use Merge to update historical data to prevent duplicates. Unique constraints can be used to ensure data is not duplicated. 25 | 26 | ### Data release 27 | 28 | #### Fact table loading 29 | | Features | Transaction Facts | Cycle Snapshot Facts | Cumulative Snapshot Facts | 30 | |------------------------|-------------------------------|-------------------------|---------------------------------------------------| 31 | | **Time/Period** | time | period | multiple points in time with short time spans | 32 | | **Granularity** | one transaction fact per line | one time period per row | one business cycle per line | 33 | | **Fact table loading** | add | add | add and modification | 34 | | **Fact table update** | not update | not update | update when new events | 35 | | **Time Dimension** | business date | end of period | multiple business process completion dates | 36 | | **Fact** | trading activity | performance over time | limit performance across multiple business phases | 37 | 38 | 39 | #### Slowly changing dimensions 40 | - T1: Do not record historical data, new data overrides old data. 41 | - T2: Set an effective FLAG value (can also use a zipper table, with the latest time). 42 | - T3: Add historical fields, Current and Previous. 43 | - T4: Build a micro dimension using intervals to store values. 44 | - T5: Micro dimensions encapsulate fast changes, record historical data (encapsulate the changing part and query as needed). -------------------------------------------------------------------------------- /DWH/Chapter 7.md: -------------------------------------------------------------------------------- 1 | ## OLAP 2 | ### Definition 3 | OLAP (Online Analytical Processing) is a process of extracting a subset of detailed data from a data warehouse, aggregating it as necessary, and storing it in an OLAP server for analysis by front-end tools. 4 | 5 | ### Classification 6 | 7 | - ROLAP (Relational OLAP) stores multidimensional data for analysis in a relational database, and selectively defines a set of real views for storage in the relational database according to application needs. 8 | - MOLAP (Multidimensional OLAP) physically stores multidimensional data required for OLAP analysis in the form of multidimensional arrays, forming a "cube" structure. 9 | - Currently, no OLAP system can meet the query needs of all scenarios. The fundamental reason is that no system can achieve perfection in terms of data volume, performance, and flexibility at the same time. Each system needs to make trade-offs between these three aspects during design. 10 | 11 | ### Common OLAP engines 12 | - MapReduce-based: Hive 13 | - Cross-data-source in-memory: Presto, Spark SQL 14 | - Pre-aggregation: Kylin 15 | - Real-time ingestion: Druid (consumes data from Kafka queue) 16 | - Single-table performance: ClickHouse -------------------------------------------------------------------------------- /DWH/Chapter 8.md: -------------------------------------------------------------------------------- 1 | ## Optimization Solutions 2 | ### Data Collection 3 | - Collection scheduling strategy 4 | - Data arrival time 5 | - Data quality monitoring 6 | 7 | ### Storage 8 | - Hot and cold data tiering: Store data in lower-cost storage media and geographic locations based on value. 9 | - Data compression 10 | 11 | ### Calculation 12 | - SQL audit optimization 13 | 14 | ### Model 15 | - Sinking of common logic 16 | - Model value assessment 17 | 18 | ### Metrics 19 | - Governance of metrics system -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | Apache License 2 | Version 2.0, January 2004 3 | http://www.apache.org/licenses/ 4 | 5 | TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 6 | 7 | 1. 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Real-time analysis is achieved through big data analytics methods such as in-memory computing and message queues. 9 | 10 | #### Shift from structured to multimodal data 11 | Combining structured and unstructured data using natural language processing, speech recognition, and image recognition technologies. 12 | 13 | #### Shift from isolated systems to data integration 14 | Integrating data storage from multiple heterogeneous systems to create a unified view. 15 | 16 | ### Key points 17 | - Storage of raw data, support for multimodal data, and a unified external access interface. 18 | - According to the definition of data lakes in the paper, HDFS is itself a data lake. 19 | - Hudi, Iceberg, and Delta Lake are middleware for organizing data between storage and computing engines, providing higher real-time capabilities and transaction support for streaming and batch computing on HDFS data. 20 | 21 | ### Lakehouse 22 | - Solving the problems of data silos and improving real-time performance in MPP and Hadoop systems. 23 | - Key components: Metadata Gateway, Presto. -------------------------------------------------------------------------------- /img/dwh 1.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/NorthShip/Data-Engineer-Learning/5d445cc5ac3e54ca831e3832880bd55996ed62cc/img/dwh 1.png -------------------------------------------------------------------------------- /img/dwh 2.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/NorthShip/Data-Engineer-Learning/5d445cc5ac3e54ca831e3832880bd55996ed62cc/img/dwh 2.png --------------------------------------------------------------------------------