A Comprehensive Guide To Data Warehouse Design & Development

For any organization striving to stay competitive, data and analytics have evolved into indispensable resources. Decision-makers use reports, dashboards, and analytics tools to monitor the performance of their businesses and get insights from data, which frequently originates from a variety of sources.  

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Nikhil Khandelwal VP Engineering icon
A Comprehensive Guide On Data Warehouse Design & Development

These insights are driven by data warehousing, which enables companies to effectively store cleaned and converted data from various sources in a central repository and distribute accurate information across the organization. 

 

Table of Contents 

  1. Introduction 
  2. What is data warehouse? 
  3. Types of data warehouses 
  4. Understanding data warehouse architecture 
  5. Building a data warehouse from scratch 
  6. Why businesses need an Enterprise Data Warehouse? 
  7. The future of data warehouse 
  8. How VLink can help you build a highly efficient data warehouse system? 

 

A survey from Gartner analyzed that digital transformation initiatives for modern data management is attracting and involving almost 72% of data and analytics leaders from enterprises across the globe. However, hiring data leaders isn’t the only way to digitally transform and become a data-powered organization. 

 

Data WareHouse Solutions

 

You can become a data-driven firm by implementing data warehousing solutions and technologies, making the employees focus on data-driven decisions. There is so much to come along with these advances for enterprises. Data is moving around, and it needs to be contained in an automated environment to help businesses succeed. 

So, before we move further with this discussion, let’s begin with the basics and core understanding of data warehouse design and development. 

 

What is data warehouse? 

 

In a data warehouse, which serves as a central repository, an organization can keep large amounts of data gathered from many systems and sources. These sources can be: 

  • Relational & transactional databases 
  • Enterprise resource planning (ERP) systems 
  • SaaS tools 
  • Customer relationship management (CRM) systems 

In essence, data warehouses hold all the vital information organizations require to conduct analyses and get the priceless business insights reflected in that data. The ultimate resource for business intelligence (BI) tasks like trends analysis and better organizational decision-making is the data warehouse.

 

Data Warehousing & Its Characteristics 

 

To support business intelligence and advanced data analysis, data warehousing facilitates a data management process that centralizes and consolidates huge amounts of data from various sources. In a more subtle way, it’s a transformative process to convert raw data into resourceful information and make it available for nurturing complex decisions within minimal timeframes. 

Here are the characteristics of data warehousing: 

  • Subject Oriented 

A data warehouse is subject-oriented since it offers data on a particular problem, or a business need rather than on how an organization is currently operating. These problems might be focused on inventory, marketing, storage, etc. 

Modern data warehousing focuses on accurate data analysis and modelling for decision-making. It also offers a clear and concise description of the topic by omitting information that would be counterproductive to the decision-making process. 

 

  • Integrated 

Integration in a data warehouse refers to creating a common measurement scale from several databases for all related data. A data warehouse is constructed by merging data from numerous sources, such a mainframe, relational databases, flat files, etc.  

Such a program helps in driving the most accurate data analysis along with complete consistency in coding specifications, naming conventions, and characteristic measures. 

 

  • Time-variant 

The time horizon for the data warehouse is relatively broad when compared to operating systems. A data warehouse stores data that has been accumulated over time and offers historical data. It has a time component, either overtly or covertly. 

The record key system includes a spot where temporal variation may be seen in data from the data warehouse. There should be a temporal component in each main key that is part of the DW, either implicitly or explicitly. like the day, month, week, etc. 

 

  • Non-volatile 

Additionally, the data warehouse is non-volatile and that’s why old data won't get lost when there are new entries. Data is only periodically updated and is read-only. It aids in comprehending what happened and when it happened as well as in the analysis of historical data. 

There is no requirement for the transaction process, recovery, or competitiveness control measures. Activities like removing, updating, and inserting that are carried out in an operational application context are omitted in the data warehouse environment. 

By providing superior performance, limitless scale, quicker time to market, and creative data processing capabilities at a fraction of the cost of conventional, on-prem systems, new cloud-based data warehouses are upending the game. 

 

Types of data warehouse designs 

 

Depending on how their duties are carried out, many types of data warehouses are possible. The Following are examples of the three functional categories of data warehouses: 

  • Data Mart 

Dedicated to a particular enterprise, a Data Mart is a subset of a data warehouse solution. It may be identified as a collection of distinct data warehouses, each geared towards carrying out a certain organizational task. These provide distinct, solitary operations that may apply to a certain business line or possibly a distinct department that runs autonomously. 

A data mart can help you cut out unnecessary data sets so you can concentrate on important information and gather insights more quickly and effectively than you would in the whole data warehouse. 

 

  • Enterprise Data Warehouse 

Data warehouse platforms store significant amounts of data within predetermined limits, which are essentially clusters of databases. Businesses may store data in logical ways, classify it, and then utilize the categorized data to generate analytics and insights inside the company. 

Companies can collect data from even unconnected sources using enterprise data warehouses, increase the quality of the data, and transform it into formats that are beneficial for the business. 

 

  • Operational Data Store 

A central database known as an operational data store contains details on all pertinent and recent datasets from various transactional systems. It gathers information from many production systems, loosely combines it, and adds temporal variance and non-volatility elements. 

As a result, organizations can simply aggregate data from several sources into a single location for reporting and analytical purposes while maintaining the data's original structure. 

 

Types of data warehouse designs

 

A cloud-based data warehouse is being established. There are still many huge organizations that employ on-premises data warehouse systems. However, modern cloud-based solutions enable businesses to build a data warehouse in days, with no upfront expenditure, and with far improved scalability, storage, and query performance, whereas old data warehouse development was often a million-dollar undertaking. 

 

  • On-premises data warehouses provide superior security, governance, and latency (the amount of time that elapses between the acquisition of data and its release to the public). On-premises data warehouses, on the other hand, are sometimes difficult to maintain and less flexible when it comes to growing to meet expanding demands. 

 

  • Cloud data warehouses, on the other hand, are far more flexible since they handle varying computation and storage needs. They are also considerably simpler to utilize because their cloud platform manages them completely. Additionally, because most cloud data warehouses employ a pay-as-you-go billing approach, their price is often clearer. 

 

Whether you deploy a customized data warehouse solution or require building it from scratch, there is always a part of development that needs to be upgraded with the latest cloud platforms. 

 

Data Warehouse Architecture 

 

There are three tiers in the data warehouse architecture, namely: 

  • The front-end client is on the top tier, which enables teams to show the latest results using reporting tools.  
  • The middle tier provides accessibility to the data analysis through analytical engine in the data warehouse. 
  • The end-tier or bottom tier is where all the data is stored securely, which is also named as database server. 

 

Data Warehouse Architechture

 

The architecture of a data warehouse depends on the specific needs of the organization. Some of the common architectures include: 

 

  • Basic: The metadata, summary data, and raw data for this architecture are all kept in one central repository. On one end, data sources load the repository, while on the other, end users access it for analysis, reporting, and data mining.  
  • Easy with a staging area: To make data preparation simple, many data warehouses provide a staging space for data before it enters the warehouse. Prior to being stored, operational data needs to be purified and converted.   
  • Hub & spoke: Organizations may adapt their data warehouses to accommodate different business lines by including data marketplaces between the central repository and end users. The data is sent to the relevant data mart whenever it is ready for use. 
  • Sandbox: A private, secure space that enables businesses to swiftly experiment with new datasets and methods of data analysis in an "offline" mode without having to adhere to the formal rules or protocols of the data warehouse.

Real-time data is transformed into essential information via the data warehouse architecture, which makes it accessible to users so they can act quickly to affect change. It is a combination of technology and elements that aids in the strategic use of data. 

We have explored the data warehousing definition, concepts, and architecture. Now the next part is to know the complete process in data warehouse design, development, integration & deployment. 

 

Building a data warehouse from scratch 

 

The process of creating a data warehouse requires thorough investigation and an exact plan, both of which can only be carried out by professionals. The phases are, however, not so difficult and can be followed as mentioned below. 

 

Phase 1: Discovery & goal alignment 

Create a thorough inventory of your functional and non-functional business requirements after determining your company's needs, since this will influence the data warehousing solutions you choose. You may do this by: 

  • Identifying the tactical and strategic business goals you want to achieve with the data warehouse development project. 
  • Setting project priorities based on the company's, departments', and business users' needs. 
  • Examining the present technical architecture, applications, etc., of the firm. doing an initial study of the data source (data type, structure, volume, sensitivity, etc.).  

It is recommended that you should take the professional’s advices and support to have an accurate definition of your business requirements. 

 

Phase 2: Establishing development environments 

 

Data warehouses typically have three primary physical environments — development, testing, and Development, testing, and production are the three main physical settings that make up a data warehouse. This match accepted best practices for software development, and each of your three environments is located on a different physical server. These environments will do the following: 

 

  • Before implementing changes in the production environment, test them first. 
  • Run tests that need a specific server to be used for execution against random samples of data from the production environment. 
  • Increased workloads for team members and servers should make hiring easier. 
  • Track and manage issues in a flexible manner while maintaining data integrity. 
  • Maintain breakpoints to avoid server stuttering. 

The pace of data warehouse design and development can be impacted by resource sharing between production, testing, and development! The resource requirements for testing, development, and production environments will vary, and performance issues will arise if all tasks are placed on one server. 

You can choose to run more than these three environments, and some business users choose to add additional environments for specific business needs. 

 

Phase 3: Determining & setting up data sources 

 

You should specify all accessible data sources to ensure that only correct and pertinent data is loaded into your data warehouse. As certain data may be contained in a few storage systems, it's also crucial to identify the systems of record to prevent loading unneeded information into a data warehouse.   

  • To make sure you integrate relevant data sources, you need to analyze their type of data contained volume and its structure (data models, if any). 
  • Sensitivity of data in terms of safety, and approach for accessing it. 
  • Missing or poor data quality, data cleansing possibilities in the sources. 
  • Data identity to check if there is missing data or lacks quality while meeting business requirements. 
  • Intervals in updating the data. 
  • Relationships with other sources of data 

 

The process to visualize all these sources and align data distribution will be required to complete these phases. And this is called data modelling.  

The three most popular data models for warehouses are: 

  • Snowflake schema 
  • Star schema 
  • Galaxy schema 

To direct your warehouse's overall data architecture, you need to pick and create a data model. The model you select will affect how your data warehouse and data marts are organized, which in turn affects how you use data warehouse tools and query that data. 

 

Phase 4: Selecting & implementing Extract, Transform, Load (ETL) plan 

 

Data is extracted from its source, modified to meet reporting needs, and then loaded into a data warehouse using extract, transform, and load (ETL) technology. The process is as follows: 

  • Data is extracted from a source system, put in a staging area. 
  • Then transform it into the most suitable format for data analytics. Additionally, you eliminate any redundant data or discrepancies that might complicate analysis. 
  • Then the final data is loaded into the data warehouse securely before getting it through the integrated BI tools. 

Because ETL handles the majority of the in-between work, using the wrong technology or creating a bad ETL process will ruin your warehouse as a whole. You need to be able to establish straightforward, consistent, and reproducible data pipelines between all of your current architecture and your new warehouse in addition to having the best possible speeds, high availability, and visualization. 

Today's technologies, however, use a different methodology called Extract, Load, and Transform (ELT). It is a game changer since it enables analysts to immediately query and analyze large amounts of data without having to set up a time-consuming ETL procedure. The following comparison will assist you in selecting the best option for your data warehousing plan. 

 

 

ETL 

VS 

ELT 

Extracts data from an integrated source, transforms it using a secondary processor and loads it. 

Working 

Extracts data, loads it, and transforms it within the same system. 

Usage of multiple servers makes it costly. 

Cost Effectiveness 

Simplified processes help in cutting expenses. 

Data transfer after transforming it requires is highly time taken. 

Processing Speed 

Comparatively faster as processes take place within the same system. 

Compliances & regulations are maintained with pre-loaded transformation. 

Security & Privacy 

Requires additional data security while loading raw data. 

Maintaining multiple processors is hectic. 

Flexibility 

Maintaining few processes makes it flexible. 

 

Phase 5: Implementing Online Analytical Processing (OLAP) Cube 

 

Large data sets may be accessed quickly, consistently, and interactively using OLAP servers, which enable complicated searches on them. To make querying and reporting easier, every data warehouse either includes an OLAP server or collaborates with one. 

In a data warehouse design, OLAP cubes perform the following operations: 

  • Roll up 

Climbing a concept hierarchy for a dimension can be used to aggregate data on a data cube. Another name for this is dimension reduction. 

  • Drill-down 

Rolling up is the opposite of drilling down. This is accomplished by lowering the notion hierarchy or adding a new dimension. 

  • Slice & dice 

Slicing creates a new sub-cube by choosing one dimension from a cube. Dicing, on the other hand, chooses two or more dimensions and produces a new sub-cube. 

  • Pivot 

Pivot operation is also known as rotation, which offers another visual of data through rotation of its axes. 

OLAP offers quick and effective access to massive amounts of data, and hence these cubes are an essential part of data warehouse design. This allows users to make defensible business decisions based on insights gained from the data. 

 

Phase 6: Building visualization & query optimization 

 

Front-end visualization is required so that users can quickly comprehend and make use of the outcomes of data searches. It guarantees that customers may quickly and simply construct reports and choose report criteria. Users may need reports to be supplied securely through a browser, sent as an attachment in an email, or kept as a spreadsheet on a network. 

There are several capabilities to establish user groups and roles, visualize the data, and provide access rights, among other things. It should be possible to edit reports in the data warehouse without affecting the underlying data. 

Another crucial aspect that helps in data warehouse designing is Query Optimization. Let’s see how it works: 

  • Build different environments for development and testing. 
  • Drive tuning for better performance on the ETL. 
  • Streamline report delivery processes and queries execution. 
  • Continuous tuning of the environments and test them. 
  • Once the queries passed successfully, product environments will update the status. 
  • To prevent breaks in between development or testing, a rollback process will be placed. 

Creating a data warehouse with the intention of providing quick and effective access to data for decision-making is one of the main objectives. Data architects must plan the data warehouse schema and indexing based on the kinds of queries users will be executing throughout the design phase. 

 

Phase 7: Launch & maintenance 

 

When your warehouse is ready to go live, you should begin considering use cases, education, and training. Most of the time, it will take a week or two (at least at scale) before your end customers notice any functionality from that warehouse. But before the deployment is finished, they should get proper training in its utilization. 

The final phase will be completed by the following actions: 

  • Implementing user-friendly interfaces: This entails installing user interfaces that are clear and simple to use and that enable people to meaningfully engage with the data. 
  • Deploying the data warehouse: This includes introducing it to its target audience and ensuring a seamless rollout procedure. 
  • Training users: It includes educating users on how to utilize the data warehouse and offering them support while they do so. 
  • Testing & refining: This involves performing user testing to make sure that the data warehouse satisfies its users' demands and making any required adjustments. 

Your data warehouse setup's ability to meet your company's needs can be greatly influenced by its data warehousing and design functions. Your use case will determine the sorts of Data Warehouse you select, but the design characteristics can be particular to the features that are more frequently used or can increase the system's robustness and efficiency for analytics. 

 

Why businesses need an Enterprise Data Warehouse? 

 

Organizations looking to analyze vast volumes of data and get value from it have access to a number of advantages thanks to data warehouses. Most crucial of them are described below: 

  • Ensures consistent data quality 

Using the ETL (extract, transform, load) method, a data warehousing operation enhances the quality and consistency of data arriving from various sources. Data integration procedures are used to eliminate duplicate entries, transform all data into a standardized format, and update data during the transformation step.   

 

  • Combines data from multiple sources 

Through their operations, several departments generate fresh data, and even within one department, data may be spread over several platforms. In order to keep this data united, you require central storage because both circumstances preclude a consolidated view from the location where decisions are made. 

 

You can integrate the data from all of those business operations and make it easily available for analysis and reporting by using a data warehouse.  

 

  • Pulls down data silos 

Businesses run the danger of creating data silos, or data systems where different departments store data and source their information, as a result of the democratization of technology and a significant dependence on cloud technologies. 

 

By routinely transferring data from several sources to a single repository, which teams can access directly to obtain the essential information, data warehousing may avoid unpleasant scenarios.  

 

  • Provides historical intelligence 

You will need to look at how the numbers have evolved over time and utilize those insights to develop more educated projections in order to make data-driven business decisions. 

 

Teams can get the essential information with just a few simple queries because data warehouses' ability to preserve historical data over far wider time periods than individual apps.  

 

  • Improves data security 

Your company data no longer depends on the status of certain platforms once it is within your data warehouse. Your data is unaffected if a vendor or service provider decides to alter their policy or stop offering their services for whatever reason. 

 

The future of data warehouse 

 

The way that data is organized, stored, and analyzed has drastically changed in recent years. The challenge from new cloud-based data warehouse solutions has already been acknowledged by data warehouse suppliers, including publicly listed firms like Amazon and Google as well as upstarts like Panoply. 

Cloud-based data warehouses brings renowned methods for information extraction from data and analysis. They enable data warehousing for both small and medium-sized firms as well as large, well-funded corporations. 

In addition, new technology is revolutionizing the data warehouse by speeding up regular processes and lowering the repetitive, human labor required for each stage of the lifespan of a data warehouse.  The advanced data warehouse solutions will offer new ways to: 

  • Categorize and optimize datasets 
  • Analyze and define data sources  
  • Create effective data models 
  • Define ETL or ELT pipelines on-the-go 
  • Streamline executions & deployments 

In general, next-generation data warehouses make maintenance and update simpler. They significantly aid the business team by enhancing the reliability and consistency of decision support infrastructures and by promptly responding to shifting business needs. 

 

How VLink can help you build a highly efficient data warehouse system? 

With several advantages and uses in various sectors, data warehousing is an essential component of business intelligence. However, to maximize the capabilities of your data warehouse, you need a method for adding new sources and loading data without repeatedly opening tickets with data engineers.  

By providing a code-free method of transferring data from several sources to a single platform like Snowflake, VLink addresses this issue by effectively automating the data flow so that even non-technical users can do data transfers.  

You may use our Data Warehouse solutions of your choice to add an automated component to the design with even more powerful capability. Without needing to repeatedly create the code, they are entirely automated and secure. These solutions enable you to not only export and load data but also change and enhance it because they are integrated with AI and BI technologies. 

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