Data Warehouse Models: Data Management Techniques + Tools

Key Benefits of a Cloud Data Warehouse

A cloud data warehouse is housed on a computing platform. It offers a central location for storing vast amounts of data from different sources and enables users to view, analyze, and gain insights using data visualization tools.

Scalability:

Due to the high scalability of cloud-based data warehouses, companies can quickly increase or decrease their processing and storage power in response to shifting demands.
By not having to worry about managing hardware and infrastructure, businesses can more easily handle and process massive amounts of data.

Cost-effectiveness:

Because cloud data warehouses are hosted on cloud computing platforms, companies don't have to spend money on and keep pricy hardware and infrastructure.
Enterprises save money because they only pay for what they actually use.

Data integration:

Cloud data warehouses enable organizations to combine both structured and unstructured data from a variety of sources into a singular repository.
Integrated data creates a unified perspective, making it simpler to analyze and draw conclusions from.

Data analysis:

Cloud-based data warehouses offer advanced analytics features, making it easy for companies to analyze their data and extract insights.

Accessibility:

As long as there is an internet connection, cloud data warehouses make it simple to obtain data from any location.
Businesses can more easily work together on data analysis and share insights across various divisions and locations as a result.

Business intelligence:

Cloud data warehouses are frequently used in collaboration with business intelligence tools, which enable organizations to carry out sophisticated analyses and produce reports and data visualizations.
When using business intelligence tools, trends, anomalies, and insights can be found that can help guide business choices.

Data Lakes:

Data lakes are sizable collections of unstructured, raw data, and they can be used in conjunction with cloud data warehouses.
Businesses can store and handle both structured and unstructured data on a single platform by integrating a data lake and cloud data warehouse.
This makes it simpler for analysts to obtain and analyze data as well as streamline data processing.

Why is data modeling necessary?

Organizing Data: Data modeling organizes data in a structured manner that makes it easy to store, retrieve, and analyze. By defining relationships and constraints between different data elements, it creates a blueprint for how the data should be organized.
Communication: It helps in communicating the data requirements between different stakeholders such as business users, data analysts, and developers. Data models act as a common language that everyone can understand, ensuring that all parties have a clear understanding of what data is needed and how it should be structured.
Maintenance: Data models provide a framework for maintaining the data over time. As the business changes and new data requirements arise, the data model can be updated to reflect those changes, ensuring that the data remains relevant and useful.
Analysis: Data modeling helps in analyzing the data by providing a structure for how data can be combined and manipulated. It helps in identifying relationships between different data elements and allows for more accurate analysis and reporting.

Maintenance and Evolution:

Database maintenance and evolution are made easier by data modeling, which also enables databases to change over time. Data modeling makes it simpler to modify the database schema as the needs of the company change by presenting a clear understanding of the data and its relationships.

Consistency and Standardization:

Data modeling aids in the establishment of uniform data definitions, formats, and connections. This makes it simpler to integrate and share data between systems and departments and helps to guarantee consistency in how data is used and interpreted across a company.

Visualization of Data:

Data visualization can make it simpler to comprehend and explain complicated ideas. Data modeling can assist with data visualization. This involves representing data relationships and hierarchies with diagrams and other visual aids.

Data Integrity and Quality:

Data modeling aids in assuring the high integrity and quality of data. By developing standardized data definitions, formats, and connections, data modeling serves to reduce the risk of data inconsistencies, mistakes, and duplication.

Hierarchical Representation:

Hierarchical relationships are commonly used to represent database data structures like parent-child relationships and tree structures. As a result, data analysis and interpretation are made easier because it is clear how the different data elements connect to one another.

Data Management:

Data modeling is required for effective data management because it organizes and structures data in a way that makes it simpler to manage and maintain. Creating a clear grasp of data relationships, identifying data elements and their attributes, and ensuring data consistency across various systems are all part of this.

Data can be managed more effectively by designing a data model that reflects business procedures and rules, increasing data accuracy, consistency, and reliability.

Reduction of Redundancy:

Data redundancy can be reduced through data modeling, which can lead to more effective data storage and retrieval. Storage space can be saved and query speed can be enhanced by identifying and removing redundant data elements.

Query Performance:

Data retrieval and storing are optimized through data modeling, which significantly speeds up queries. The time it takes to retrieve data can be decreased by creating a data model that is customized to the requirements of the company.

Enhance query speed, this entails building indexes, streamlining table structures, and partitioning data. A well-designed data model can lessen the number of tables that must be joined in a query, reducing the amount of data that must be scanned and enhancing speed.

Data philosophies

Two data warehouse architectures, Kimball and Inmon, show various methods for creating and developing data warehouses. The main variations between the two are as follows:

1. Kimball Method (Ralph Kimball):

Focuses on creating data marts that are tailored for particular user groups or business operations.
Utilizes a method of dimensional modeling to arrange data into a star or snowflake structure.
Emphasizes flexibility and simplicity in design, emphasizing business customers' usability.
Advocates for building the data warehouse incrementally and iteratively, with a focus on rapidly generating business value.
Entails the use of ETL (Extract, Transform, Load) procedures to convert data coming from the source systems into the data warehouse.

2. Inmon Method (Bill Inmon):

Builds a central data warehouse that combines data from various source systems as its main goal.
Utilizes a normalized modeling method to reduce duplication and guarantee data consistency.
Focuses on ensuring data accuracy and consistency throughout the organization while putting a strong emphasis on data quality and governance.
Promotes the creation of the data warehouse using a comprehensive and strategic strategy, with an emphasis on long-term planning and scalability.
It entails loading data into the data warehouse using ELT (Extract, Load, Transform) procedures, with transformations taking place inside the warehouse.

Types of Data Model Architectures

To arrange and format data in a way that makes it simpler to comprehend and use, data models, are used. The following examples highlight the distinguishing features and benefits of the three main types of data models:

Physical data model

The primary focus of the physical data model is the actual implementation of the data. It includes details on the file organization, access controls, and data storing format.

To improve database efficiency and make sure that data is preserved as effectively as possible, this type of model is used. tangible data models also include metadata that describes the data's tangible characteristics, such as field lengths, data types, and indexes.

Logical data model

A logical data model is a particular kind of data model that concentrates on the relationships between business entities. It contains information about entities, connections, and data attributes.

This kind of model is employed to guarantee data accuracy and consistency and to give various business units or departments a shared grasp of the data.

Logical data models also include metadata such as data definitions, business principles, and data quality specifications that describe the meaning and context of the data.

Conceptual data model

An organization's data is represented at a high level in a conceptual data model, which gives a general overview of the data components, relationships, and business rules at play. It is a condensed view of the information that emphasizes business ideas and how they relate to one another rather than technical details.

A conceptual data model's main objective is to give stakeholders a shared grasp of the data requirements. When it comes to the data being used and how it is related, it helps to ensure that everyone engaged in a project is on the same page.

Conceptual data models frequently use straightforward language and diagrams that are basic enough for non-technical stakeholders to understand.

Dimensional Modeling vs. Relational Models

Both relational models and dimensional models are used to create databases, but they organize data differently.
The relational database model, which arranges data into tables with rows and columns, is the foundation for relational models. Foreign keys that refer to primary keys in other tables describe the connections between them.
This method is frequently applied in transactional systems, where real-time data collection and handling are the main objectives.
On the other hand, databases that are best for reporting and research are designed using dimensional modeling. It arranges data into a star or snowflake schema, which is made up of a main fact table and additional dimension tables.
The dimension tables offer descriptive information about the data, while the fact table includes numerical data that is analyzed. Systems for data warehousing and corporate intelligence employ this strategy.
Due to the fact that the data is pre-aggregated and stored in a denormalized format, one of the main benefits of dimensional modeling is its capacity to offer quick query performance for analytical queries. Complex analyses involving numerous dimensions, including time, geography, product, and customer, are now simpler to carry out.
Contrarily, relational models work better in real-time, transactional systems where data is continuously changing and updating.

OLTP vs. OLAP

Online analytical processing, also referred to as OLAP, and online transaction processing, also known as OLTP, are two separate types of database processing with different purposes.
Analytical processing, on the other hand, is used to access, analyze, and aggregate data for reporting and decision-making. OLAP databases are designed to support complex queries across large datasets in addition to being optimized for quick query speed.
On the other hand, analytical processing, or OLAP, is used to access, analyze, and aggregate data for reporting and decision-making. In addition to being optimized for quick query performance, OLAP databases are made to support complex queries across big datasets.
OLAP applications include data warehousing, business intelligence platforms, and financial research tools.

	OLTP	OLAP
Purpose	Transactional processing	Analytical processing.
Data structure	Normalized data structures are common for OLTP databases, with data arranged in tables and relations.	OLAP databases usually have denormalized data structures with dimensionally organized data.
Query complexity	Are primarily concerned with retrieving and updating individual data.	Are complex because they combine data from various aspects.
Performance	Designed for fast query performance	Optimized for quick data entry and retrieval.

Data Warehousing Models

Several layers of data modeling techniques are usually used in data warehousing models to help organize and manage massive amounts of data. The following examples emphasize the primary characteristics and functionality of each of the three major layers:

Staging layer

The staging layer, which acts as a temporary storage place for data that has been extracted from source systems, is a crucial element of data warehousing models. The staging layer's main goal is to ensure that the data is accurate, full, and consistent as it is being prepared for integration into the data warehouse.

The intermediate layer

The intermediate layer, which is the second tier in a data warehousing architecture, is used to keep data in a relational database. This stage is also referred to as the operational data store (ODS) layer in data warehousing models.

Its primary function is to keep data in a form that is suitable for analysis and querying. Normalization techniques are typically used in the construction of this layer to ensure data consistency and reduce data redundancy.

The intermediate layer may also contain metadata, which is used to describe the data in the data warehouse and to provide information about data lineage, data quality, and data connections. Its main purpose is to store data in a format that is ideal for querying and analysis.

Data mart layer

The data mart layer, which is the third and final layer in a data warehousing model, is used to store data marts created to serve end users' analytical needs. Data marts are typically constructed in accordance with organizational structures or functions, and they are designed for specific analytical duties like data mining or reporting.

The purpose of a specific type of data warehousing model known as a "data vault" is to keep historical data and provide a thorough overview of the data over time.

Normalization Schemas

The process of normalizing data in a database aims to enhance consistency and reduce redundancy. Although normalization is a crucial component of database architecture, it is not always the most effective strategy for data warehousing.

Denormalization is actually frequently needed in data warehousing to support complex reporting and analysis and improve query speed, and other goals. In light of this, the following are a few typical normalization schemas that could be applied in data warehousing models:

1. First Normal Form (1NF):

In this schema, each table must have a primary key, and each attribute must have only one value. All relational databases, including data warehouses, must have this structure.

2. Second Normal Form (2NF):

This schema mandates that all non-key attributes be reliant on the primary key. In other words, there shouldn't be any partial dependencies, where a primary key is only partially reliant on an attribute.

3. Third Normal Form (3NF):

In this schema, each non-key attribute must be independent of every other non-key attribute and only be reliant on the primary key. This lessens duplication and enhances data integrity.

4. Boyce-Codd Normal Form (BCNF):

Unlike 3NF, Boyce-Codd Normal Form (BCNF) demands that all dependencies be based on candidate keys, not just the primary key. This can assist in removing some of the anomalies that can develop during data storage.

5. Fourth Normal Form (4NF):

The database must be cleared of all multi-valued dependencies before using this structure. This may not always be required in data warehousing, but it can help to reduce redundancy and enhance data consistency.

Star Schema Dimensional Model

A star-shaped fact table in the center, encircled by several dimension tables, is the organization of data in a star-shaped database schema, which is used in data warehousing.

Because of their renowned ease of use, effectiveness, and adaptability, star schema models are common in business intelligence and analytics apps. Additionally, they are prepared for OLAP (Online Analytical Processing) operations, which enable quick data analysis and data slicing and dicing.

Fact tables

A fact table is a central table in a star schema dimensional model that holds the numerical information or facts associated with a business activity, such as sales, inventory, or production. A collection of measures or metrics that can be aggregated and analyzed by dimensions are typically contained in fact tables, which are typically big, wide tables.

In a star schema, fact tables are used to store numerical data at a particular degree of granularity and are linked to dimension tables using surrogate keys. To ensure that the fact table design satisfies the requirements of the business, it should be carefully planned and tested as it is essential to the efficiency and usability of the data warehouse.

The degree of detail or data aggregation in a fact table is referred to as granularity. For instance, a sales fact table might include measurements on a daily, weekly, monthly, or annual basis, based on the needs of the business. The amount of information needed for analysis and reporting determines how granular a fact table should be.

To guarantee uniqueness and enhance query performance, fact tables may contain surrogate keys, which are artificial keys or identifiers. These keys are created automatically by the database management system and are frequently numeric or alphanumeric.

Dimension tables

A star schema dimensional model's dimension tables have description attributes that give the measures in the fact table meaning. The fact table's material is grouped, filtered, and aggregated using these attributes.

The fact table's equivalent foreign key would be linked to each of these dimension tables' primary keys, enabling quick data aggregation and analysis by various dimensions. For instance, it would be simple to perform a query on total sales by product type, customer segment, or time frame.

Pros:

Fast query performance is made possible by star schema dimensional models because they require fewer joins and data aggregations.
Because star schema includes the aggregation of facts at a particular level of granularity, the data aggregation process is quick and easy.
Because star schema models are built to support complicated queries and reporting requirements, they are better suited for data analytics.
The star schema's uncomplicated form makes it simple to comprehend and apply. It is simple to browse and analyze because it is built on a concise, clear structure of dimensions and fact tables.
By removing redundant data and keeping only the necessary data in the database, star schema dimensional models assist in reducing data redundancy.

Cons:

Star schema dimensional models may experience problems with data security if the data is not adequately validated and normalized.
Star schema dimensional models are not the best choice for transactional systems because they were created for analytical reasons rather than real-time transaction processing.
Star schema models may need extra work to model complicated data because they are not well-suited for dealing with complex datasets.
Star schema dimensional models require a lot of work to modify and are not very flexible when it comes to managing changes in the data structure.
Star schema dimensional models struggle to keep historical data effectively, and maintaining historical data may take more work.

Snowflake Schema Dimensional Model

An expansion of the star schema is the snowflake schema, a kind of dimensional model used in data warehousing. Contrary to the star schema, some of the dimension tables are normalized into extra sub-dimension tables. It organizes data into a central fact table surrounded by numerous dimension tables.

The snowflake schema gets its name from the form it assumes when diagrammed, which resembles a snowflake. A snowflake design creates a hierarchical structure by normalizing dimension tables by slicing them into numerous related tables. A primary key in each of these sub-dimension tables would connect to a matching foreign key in the parent dimension table.

Data denormalization

To minimize the number of joins necessary to obtain data, data denormalization in a snowflake schema dimensional model entails combining or duplicating data from various tables. By streamlining the data model and lessening the complexity of joins, it can enhance query speed.

It may, however, also lead to data duplication and increased storage needs. Making summary tables, which have aggregated data that can be accessed rapidly without the need for joins to the fact table, is one popular method of data denormalization in a snowflake schema.

Data duplication into another database is another method of data denormalization. Denormalizing data can lead to data redundancy and increased storage needs, so it's essential to proceed with caution.

It's crucial to take into account the trade-offs between query speed and storage needs, as well as to guarantee that the denormalized data is still compatible with the original data.

Extended dimension tables

Extended dimension tables are extra tables made in a snowflake schema dimensional model to further normalize or extend the data contained in a dimension table. These tables enable more thorough and complex data analysis because they are linked to the initial dimension table via primary and foreign keys.

Consider a client dimension table that includes the customer ID, name, and address. An extended dimension chart that includes extra details like the buyer's purchasing history or demographic data could be made. A primary key-foreign key relationship would be used to link this extended dimension table to the initial customer dimension table.

Using extended dimension tables in a snowflake schema has several advantages, including increased flexibility, better data organization, improved query speed, and improved data quality.

Pros of Snowflake Schema

Snowflake schema enhances query speed by lowering the quantity of joins necessary to retrieve data. It removes the redundant data from the dimension tables, making the tables smaller and the queries quicker.
Less data redundancy means that the Snowflake schema is simpler to manage than other schemas.
By removing redundant data, the Snowflake schema makes better use of available storage capacity.
The Snowflake schema offers greater freedom in defining complicated relationships between dimensions, allowing for a more accurate representation of the data.

Cons of Snowflake Schema

Snowflake schema may be more complex than a star schema, which makes it more challenging to design and manage.
Increased disk I/O may be experienced as a result of the snowflake schema because the database engine may need to access multiple tables to obtain data.
Because more tables and joins are needed to obtain data in a snowflake schema, queries there may be more complicated.
Compared to other schemas, the snowflake schema may need more storage because it uses extra tables to represent the complex relationships between dimensions.

7 Helpful Data Warehouse Modeling Tools

A data warehouse can be designed and built with the aid of several useful modeling tools for data warehouses. Seven such tools are listed below:

1. Portable

For teams working with long-tail data sources, Portable is the finest data integration tool. Over 300 unusual data sources can be connected to Portable's ETL tool.

Upon request, the Portable team will develop and manage unique connectors with turnaround times of as little as a few hours.

Key features:

Seven days a week, twenty-four hours a day, direct help is offered.
A large catalog of long-tail data connectors that are available to use right away.
Custom data source connections can be built on demand, are maintained and are free of charge.

Pricing:

There are no restrictions on volume, connectors, or destinations for manual data processing under Portable's free plan.
The monthly flat fee for automated data transfers at Portable is $200.
Please contact sales for information on business requirements and SLAs.

2. SqlDBM

SqlDBM is a database development platform that allows businesses to make databases online without having to write any code. It enables developers to concentrate on the database architecture rather than the syntax.

Users can import SQL scripts to create database models automatically. Users can also use SqlDBM to automatically create a database model with powerful and effective visualizations from their existing DB/DW.

Key features:

Team Collaboration.
Forward Engineering.
Autocomplete Data Type.
Dark and Light themes.
Reverse Engineering.
Subject Areas.

Pricing:

SqlDBM offers four different plan options: free, single unlimited, team unlimited, and students and teachers.

Plans for singles start at $15 per month.
The monthly fee for the team unlimited package is $45 per month.
Please call the sales team for student and teacher rates.

3. DbSchema

DbSchema is a universal database design tool for schema management, schema documentation, team design, and deployment across numerous databases.

Key features:

Data Importer
Schema Synchronization
Automation Scripts
Reverse Engineer the schema by Connecting to the Database
Diagrams for MongoDB and Schema validation

Pricing:

DbSchema is available in two editions: Free Community and paid Pro.
The Pro plan begins at $ 98 for educational purposes and goes up to $ 294 for commercial purposes.

4. LucidChart

Lucidchart is a web-based diagramming application that allows you to create diagrams quickly and easily. Draw flowcharts, org charts, wireframes, UML, thought maps, and more in no time!

Key features:

Make a UML sequence model out of text markup.
Create an organization chart from a CSV file.
Import Amazon Web Services architecture for network diagrams.
Import ERD database, tables, and schemas.
In Google Sheets, connect diagram shapes to real data.

Pricing:

Lucidchart has four account types: free, basic, professional, and team.

Individual plans start at $7.95 per month.
The team plan begins at $6.67 per month.
For Enterprise rates, please contact Lucid Software.

5. Idera ER/Studio

IDERA ER/Studio Data Architect is a powerful data modeling tool that enables companies to build a business-driven corporate data architecture. With round-trip database support, data architects can simply reverse-engineer, analyze, and optimize existing databases from different platforms.

Its purpose is to assist companies in improving data quality, streamlining data integration, and ensuring data governance.

Key features:

Reverse Engineering
Data Lineage
Data Modeling

Pricing:

The ER/Studio Data Architect plan is priced per person at $1,470.40.
The ER/Studio Business Architect plan is priced per person at $920.00.
Contact the sales staff for more information on the ER/Studio Data Architect Professional plan.
Contact the sales staff for more information on the ER/Studio Enterprise staff Edition plan.

6. ArchiMate

ArchiMate is a design language developed by the Open Group. It graphically maps business connections using clear and consistent terminology. ArchiMate can be used to visualize an organization's organizational framework, including its systems, procedures, information, and data flows.

Key features:

Support for ArchiMate 3.1 models.
It is free to use.
Cross-platform.
Expandable using plugins.

Pricing:

It is free to use as it's open-source.

7. MySQL Workbench

It is a visual database design and modeling tool for creating, editing, and managing MySQL databases. MySQL Workbench supports visual schema design, SQL development and execution, database administration and management, and data transfer and synchronization.

It also allows for plugins to provide other capabilities, such as reverse engineering and ERD production from an existing database.

Key features:

Database Connection & Instance Management.
SQL Editor.
Data modeling.
Database administration.
Performance monitoring.
Database migration.

Pricing:

MySQL Workbench is freely accessible thanks to the GPL v2 open-source agreement. It can be obtained from the MySQL website and installed using package managers on a number of OS platforms.
Additionally, the Oracle Store sells a paid subscription to MySQL Workbench Plus, a commercial version with more features and assistance.

Modern Data Warehouse Techniques

Many important steps and processes are usually involved in modern data warehouse techniques. Some of the main methods used are highlighted in the following points:

1. Catalog your data sources

Find and list every source of data for your company, including both structured and unstructured data, information from different divisions or business operations, and information from external sources. This procedure is essential to ensuring that your data warehouse is complete and accurately represents the data in your company.

2. Visualize a data warehouse design

Create a data warehouse that can efficiently handle, store, and analyze all of the data you have gathered after you have categorized your data sources. Typically, during this process, a visual depiction of the tables, columns, relationships, and data types found in the data warehouse architecture and schema is produced.

3. Set up ETL data pipelines

ETL, or extract, transform, and load is a crucial method for adding data from different sources to your data warehouse. Data must be extracted from each source, transformed to fit the data warehouse's framework, and then loaded into the warehouse in order to accomplish this.

In a process known as extract, load, and transform(ETL), data is sometimes put into the warehouse first, then transformed.

4. Adapt to your business processes

A modern data warehouse should be created to support your business's particular needs, including data processing, reporting, and decision-making. To support particular business processes and workflows, this may entail modifying the schema of your data warehouse, your data pipelines, or your data processing tools.

5. Automate data management

It's critical to automate as many data management duties as you can in order to preserve the accuracy and integrity of your data warehouse. Data cleansing, data validation, and data profiling may be part of this, in addition to tracking and alerting for problems like poor data quality or failed ETL pipelines.

Automation can lessen the burden on your data management team while ensuring that your data warehouse stays up to date.