Data Warehousing: A Complete Guide for Beginners with PDF Download
Data Warehouse Tutorial for Beginners PDF Free
If you are looking for a comprehensive and easy-to-follow guide on data warehousing, you have come to the right place. In this article, you will learn everything you need to know about data warehousing, from its definition and benefits to its design and implementation. You will also discover how to use data warehousing for data analysis and reporting, business intelligence and data mining, and various real-world applications. By the end of this article, you will have a solid understanding of data warehousing concepts and techniques, and you will be able to download a free PDF version of this tutorial for your reference.
Data Warehouse Tutorial For Beginners Pdf Free turismo dailymotion
Introduction
Data is one of the most valuable assets of any organization. It can provide insights into customer behavior, market trends, operational efficiency, strategic decisions, and more. However, data alone is not enough. You need to store, organize, process, and analyze data in a way that makes sense for your business goals and needs. This is where data warehousing comes in.
What is a data warehouse?
A data warehouse is a centralized repository of integrated data from multiple heterogeneous sources that support analytical reporting, structured and/or ad hoc queries, and decision making. A data warehouse enables you to:
Consolidate data from various sources, such as databases, files, web services, etc.
Transform data into a consistent format and structure that is suitable for analysis.
Store historical data for long-term analysis and comparison.
Provide fast and easy access to data for various users and applications.
Ensure data quality, accuracy, security, and reliability.
A data warehouse is not a replacement for your operational systems, such as transactional databases or online applications. Rather, it is a complementary system that extracts, transforms, and loads (ETL) data from your operational systems into a separate database that is optimized for analytical purposes.
Why use a data warehouse?
A data warehouse can provide many benefits for your organization, such as:
Improved decision making: A data warehouse can help you make better and faster decisions by providing you with relevant, accurate, timely, and consistent information across your organization.
Enhanced business intelligence: A data warehouse can enable you to perform advanced data analysis and reporting using various tools and techniques, such as online analytical processing (OLAP), data mining, dashboards, scorecards, etc.
Increased productivity: A data warehouse can improve your productivity by reducing the workload on your operational systems, simplifying your data access and integration processes, automating your data delivery and refreshment processes, and providing a single source of truth for your data.
Reduced costs: A data warehouse can reduce your costs by eliminating data redundancy and inconsistency, optimizing your data storage and processing resources, and increasing your return on investment (ROI) on your data assets.
Competitive advantage: A data warehouse can give you a competitive edge by enabling you to discover new opportunities, identify and solve problems, monitor and improve performance, and respond to changing market conditions.
Data warehouse architecture
A data warehouse architecture is a framework that defines the components, processes, and technologies that are involved in building and maintaining a data warehouse. There are different types of data warehouse architectures, such as:
Single-tier architecture: This is the simplest form of data warehouse architecture, where data is directly loaded from the source systems into the data warehouse without any intermediate stages.
Two-tier architecture: This is the most common form of data warehouse architecture, where data is first extracted from the source systems and then transformed and loaded into the data warehouse.
Three-tier architecture: This is the most complex and robust form of data warehouse architecture, where data is first extracted from the source systems, then transformed and loaded into a staging area, and then finally loaded into the data warehouse.
The following diagram shows an example of a three-tier data warehouse architecture:
Source Systems Staging Area Data Warehouse --- --- --- Database 1 ETL Process Data Marts Database 2 OLAP Server File 1 Data Mining Tools File 2 Reporting Tools Web Service 1 Dashboards Web Service 2 Users Data warehouse concepts
Before we dive into the details of data warehouse design and implementation, let us review some of the key concepts and terms that are related to data warehousing, such as:
Data warehouse schema: A data warehouse schema is a logical structure that defines how the data is organized and stored in the data warehouse. There are different types of data warehouse schemas, such as star schema, snowflake schema, etc.
Data mart: A data mart is a subset of a data warehouse that focuses on a specific subject area or business function, such as sales, marketing, finance, etc. A data mart can be independent or dependent on the data warehouse.
Data lake: A data lake is a large repository of raw and unstructured data that can be stored and processed using various technologies, such as Hadoop, Spark, etc. A data lake can complement or replace a data warehouse depending on the use case and requirements.
Metadata: Metadata is data about data. It describes the characteristics, properties, and relationships of the data in the data warehouse. Metadata can be classified into technical metadata, business metadata, and operational metadata.
Data quality: Data quality is the degree to which the data in the data warehouse meets the expectations and requirements of the users and applications. Data quality can be measured by various dimensions, such as accuracy, completeness, consistency, timeliness, validity, etc.
Data Warehouse Design
Now that we have a basic understanding of what a data warehouse is and why we need it, let us see how we can design a data warehouse that meets our business needs and objectives. Data warehouse design involves two main steps: data modeling and dimensional modeling.
Data modeling
Data modeling is the process of defining the structure and relationships of the data in the data warehouse. Data modeling can be performed at different levels of abstraction, such as:
Conceptual level: This is the highest level of abstraction that describes the overall scope and purpose of the data warehouse. It identifies the main entities and concepts that are relevant to the business domain and their relationships.
Logical level: This is the intermediate level of abstraction that describes the detailed structure and attributes of the entities and concepts in the conceptual level. It also defines the constraints and rules that govern the data.
Physical level: This is the lowest level of abstraction that describes how the logical level is implemented in terms of tables, columns, indexes, partitions, etc. It also defines the physical storage and performance aspects of the data.
The following diagram shows an example of a conceptual level data model for a sales-related subject area:
Customer Order Product Category --- --- --- --- Customer ID Order ID Product ID Category ID Customer Name Order Date Product Name Category Name Customer Address Order Amount Product Price Dimensional modeling
Dimensional modeling is the process of designing the data warehouse schema using a star or snowflake structure. Dimensional modeling can be performed at different levels of granularity, such as:
Enterprise level: This is the highest level of granularity that describes the entire data warehouse as a single star or snowflake schema. It contains all the dimensions and facts that are relevant to the whole organization.
Business process level: This is the intermediate level of granularity that describes a specific business process or function as a star or snowflake schema. It contains only the dimensions and facts that are relevant to that business process or function.
Individual level: This is the lowest level of granularity that describes a single dimension or fact table as a star or snowflake schema. It contains only the attributes and measures that are relevant to that dimension or fact table.
The following diagram shows an example of a business process level dimensional model for a sales-related subject area:
Fact Table: Sales Dimension Table: Customer Dimension Table: Product Dimension Table: Time --- --- --- --- Order ID (PK) Customer ID (PK) Product ID (PK) Time ID (PK) Order Date (FK) Customer Name Product Name Year Customer ID (FK) Customer Address Product Price Quarter Product ID (FK) Customer Phone Product Category (FK) Month Order Amount Day Weekday Data Warehouse Implementation
After we have designed our data warehouse schema, we need to implement it using various tools and technologies. Data warehouse implementation involves three main steps: ETL process, ETL tools, and data quality and data cleansing.
ETL process
ETL stands for extract, transform, and load. It is the process of moving data from the source systems to the data warehouse. ETL process can be divided into three phases:
Extract phase: This is the phase where data is extracted from the source systems using various methods, such as full extraction, incremental extraction, delta extraction, etc.
Transform phase: This is the phase where data is transformed into a consistent format and structure that is suitable for loading into the data warehouse. Transformation can include various operations, such as filtering, sorting, aggregating, joining, splitting, validating, etc.
Load phase: This is the phase where data is loaded into the data warehouse using various methods, such as bulk loading, incremental loading, trickle loading, etc.
The following diagram shows an example of an ETL process:
Source Systems Extract Phase Transform Phase Load Phase Data Warehouse --- --- --- --- --- Database 1 Full Extraction Filtering Bulk Loading Fact Table: Sales Database 2 Incremental Extraction Sorting Incremental Loading Dimension Table: Customer File 1 Delta Extraction Aggregating Trickle Loading Dimension Table: Product Source Systems Extract Phase Transform Phase Load Phase Data Warehouse --- --- --- --- --- Database 1 Full Extraction Filtering Bulk Loading Fact Table: Sales Database 2 Incremental Extraction Sorting Incremental Loading Dimension Table: Customer File 1 Delta Extraction Aggregating Trickle Loading Dimension Table: Product File 2 Joining Dimension Table: Category Web Service 1 Splitting Dimension Table: Time Web Service 2 Validating ETL tools
ETL tools are software applications that help automate and simplify the ETL process. ETL tools can provide various features and functionalities, such as:
Data extraction: ETL tools can connect to various source systems and extract data using various protocols and formats, such as SQL, XML, JSON, CSV, etc.
Data transformation: ETL tools can perform various data transformation operations using various methods and languages, such as graphical user interface (GUI), scripting, SQL, etc.
Data loading: ETL tools can load data into various target systems using various methods and modes, such as batch, real-time, parallel, etc.
Data quality: ETL tools can ensure data quality by performing various checks and validations, such as data profiling, data cleansing, data auditing, data reconciliation, etc.
Data integration: ETL tools can integrate data from various sources and targets using various techniques and standards, such as data mapping, data lineage, data governance, etc.
Data management: ETL tools can manage data throughout the data warehouse lifecycle using various functions and capabilities, such as data scheduling, data monitoring, data security, data backup and recovery, etc.
There are many ETL tools available in the market, both free and paid. Some of the popular ETL tools are:
Informatica PowerCenter
IBM DataStage
Microsoft SQL Server Integration Services (SSIS)
Oracle Data Integrator (ODI)
SAP Data Services
Talend Open Studio
Pentaho Data Integration
AWS Glue
Azure Data Factory
Google Cloud Dataflow
Data quality and data cleansing
Data quality and data cleansing are two important aspects of data warehouse implementation that ensure the reliability and usability of the data in the data warehouse. Data quality refers to the degree to which the data in the data warehouse meets the expectations and requirements of the users and applications. Data cleansing refers to the process of identifying and correcting errors and inconsistencies in the data in the data warehouse.
Data quality can be measured by various dimensions, such as:
Accuracy: The extent to which the data in the data warehouse is correct and free from errors.
Completeness: The extent to which the data in the data warehouse is sufficient and covers all the relevant aspects.
Consistency: The extent to which the data in the data warehouse is coherent and compatible across different sources and targets.
Timeliness: The extent to which the data in the data warehouse is up-to-date and reflects the current state of affairs.
Validity: The extent to which the data in the data warehouse conforms to the predefined rules and standards.
Data quality can be measured by various dimensions, such as:
Accuracy: The extent to which the data in the data warehouse is correct and free from errors.
Completeness: The extent to which the data in the data warehouse is sufficient and covers all the relevant aspects.
Consistency: The extent to which the data in the data warehouse is coherent and compatible across different sources and targets.
Timeliness: The extent to which the data in the data warehouse is up-to-date and reflects the current state of affairs.
Validity: The extent to which the data in the data warehouse conforms to the predefined rules and standards.
Uniqueness: The extent to which the data in the data warehouse is distinct and free from duplicates.
Data cleansing can be performed by various methods, such as:
Data profiling: This is the process of analyzing and assessing the quality of the data in the source systems and identifying potential issues and anomalies.
Data standardization: This is the process of transforming and formatting the data in the source systems into a consistent and uniform structure and style.
Data matching: This is the process of identifying and linking records that refer to the same entity or concept across different sources and targets.
Data deduplication: This is the process of removing duplicate records from the data in the source systems or the data warehouse.
Data enrichment: This is the process of adding or enhancing missing or incomplete data from external sources or derived values.
Data verification: This is the process of validating and confirming the accuracy and completeness of the data in the source systems or the data warehouse.
Data Warehouse Usage
Once we have implemented our data warehouse, we can use it for various purposes, such as data analysis and reporting, business intelligence and data mining, and various real-world applications. Data warehouse usage involves two main concepts: OLAP and OLTP.
OLAP and OLTP
OLAP stands for online analytical processing. It is a technique that enables users to perform complex and multidimensional analysis on large volumes of historical data in the data warehouse. OLAP can provide various features and functionalities, such as:
Data slicing: This is the process of selecting a subset of data from a larger data set based on certain criteria or filters.
Data dicing: This is the process of selecting a smaller data cube from a larger data cube based on multiple criteria or filters.
Data drilling: This is the process of navigating through different levels of granularity or detail in a data cube. Drilling can be performed in various ways, such as drilling down, drilling up, drilling across, etc.
OLAP stands for online analytical processing. It is a technique that enables users to perform complex and multidimensional analysis on large volumes of historical data in the data warehouse. OLAP can provide various features and functionalities, such as:
Data slicing: This is the process of selecting a subset of data from a larger data set based on certain criteria or filters.
Data dicing: This is the process of selecting a smaller data cube from a larger data cube based on multiple criteria or filters.
Data drilling: This is the process of navigating through different levels of granularity or detail in a data cube. Drilling can be performed in various ways, such as drilling down, drilling up, drilling across, etc.
Data pivoting: This is the process of rotating or changing the orientation or perspective of a data cube to view it from different angles or dimensions.
Data aggregation: This is the process of summarizing or grouping data in a data cube based on certain measures or functions, such as sum, average, count, etc.
Data calculation: This is the process of performing arithmetic or logical operations on data in a data cube to derive new values or measures.
OLTP stands for online transaction processing. It is a technique that enables users to perform simple and routine transactions on current and operational data in the source systems. OLTP can provide various features and functionalities, such as:
Data insertion: This is the process of adding new records or rows to a table in a database.
Data deletion: This is the process of removing existing records or rows from a table in a database.
Data update: This is the process of modifying existing records or rows in a table in a database.
Data retrieval: This is the process of fetching existing records or rows from a table in a database based on certain criteria or queries.
Data locking: This is the process of preventing concurrent access or modification of data in a table in a database by other users or transactions.
Data backup: This is the process of creating a copy or replica of data in a table in a database for recovery purposes.
The following table summarizes some of the key differences between OLAP and OLTP:
OLAP OLTP --- --- Analytical processing Transaction processing Historical data Current data Data warehouse Source systems Complex and multidimensional queries Simple and routine queries Low frequency and high volume High frequency and low volu