How to build a data warehouse from scratch: Step-by-step guide 

Contemporary civilization is essentially a data-driven one. Even an ordinary person has to make decisions relying on tons of information in their work and everyday life. While data is highly important for individuals, for enterprises, it is mission-critical. It is a starting point for any analytics used to streamline business activities, retain customers, shape marketing strategies, etc.  Yet, data handling is still a tough row to hoe for many organizations. Building a data warehouse (DWH) is a second-to-none approach allowing companies to facilitate data storing and processing. In this article, you will learn about DWH benefits, architecture types, and how to build a data warehouse from scratch.

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Data warehouse benefits

Very often, business dossiers of a company are kept in a dozen locations in the cloud and on-premises, for instance, ERP, CRM, or EHR, as well as in multiple paper-format depots. Under such conditions, accessing, retrieving, analyzing, and using data (to say nothing of providing its security) becomes a problem. Luckily, the potential of current cloud services enables enterprises to optimize data handling routines. When you build data warehouse, you pool historical and current electronic records to be stored and managed in one virtual place to be further processed and turned into actionable insights for further business intelligence endeavors. 

Today, organizations can use different types of data warehouses – from small-size information banks (data marts and repositories of unstructured data or data lakes) to classical big-volume relational databases, where information is in the form of easy-to-access tables. Yet, whatever model of implementing a data warehouse you opt for, it is sure to bring your company a number of perks.  

First, you obtain a reliable storage facility that unites all sources of information and rules out any possibility of data deletion. Second, being foolproof in operation and transparent in arrangement, it is simple to use even by non-tech personnel who would find it a breeze to understand its thematic, structural pattern. Third, it functions in real-time mode, enabling 24/7 data tracking, auditing, and vetting, as well as monitoring regulation compliance in the sphere of data protection and security. 

Such weighty data warehouse assets are the derivatives of their peculiar makeup.  

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Data warehouse architecture types

Misled by the name, people with no experience building data warehouses may picture this solution as nothing more than a collection of Excel files housed in a cloud-based server. But this image falls utterly short of the complex and even intricate construction of even the simplest DWH.  

On the front end, it is a representational UI from which users access data, employ special tools to analyze them, and share both data and analysis results with any authorized person. On the backend, beside the warehouse proper as a venue for aggregating, standardizing, and dimensionalizing data, a standard DWH contains three more components.  

The first is multiple data sources (from internal ecosystems to enterprise apps) from which the diverse raw data is drawn. The second is the meta-data module that contains “data about data,” explaining what kinds of data (technical or business) are stored in the DWH and displays their interrelations. The third is a bunch of APIs that enable data integration leveraging the most efficient ELT information handling scheme when data is processed within the warehouse, not before it is fed into it as the now old-fashioned ETL model prescribed. 

Having the same basic elements, data warehouses may manifest different patterns of their internal arrangement, which results in obtaining the DWH architecture of one of the three types.  

One-tier architecture 

This is the simplest organizational mode for a data warehouse building. In it, you just connect your data storage directly to the UI, letting users make queries and get an immediate response. Sounds perfect? Well, not really.  

Issues begin to crop up if the volume of information exceeds 100GB when the query processing speed and accuracy suffer considerably. Besides, analytical capabilities and report complexity under such conditions are substandard, and the flexibility of the entire system is somewhat limited. That is why this model hardly suits large enterprises working with vast amounts of data and advanced queries. 

Two-tier architecture 

As one can easily guess, one more architectural layer here serves as a database extension, coming between the UI and the DWH itself. It consists of several data marts, each containing only domain-specific information, dramatically simplifying query handling. Plus, such an organization enables greater data security since file access can be restricted to specific departments or employees.  

The chief downsides of implementing data warehouse of this type are extra efforts to integrate data marts with the core ecosystem and the necessity to splurge on additional hardware resources. 

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Three-tier architecture 

This model gives depth to data representation and processing by arranging it in the shape of OLAP cubes instead of conventional two-dimensional tables utilized for one- and two-layer DWH structures. The cubes can be configured to access the whole data bank or each mart separately, thus giving the warehouse the utmost flexibility and security. In addition, users can move across dimensions at will, finding any data they need and subjecting it to multi-directional analysis.  

Whatever architecture type you will opt for in data warehouse development, you will need a set of efficient tools to do it.  

Learn more about DICEUS banking data warehouse development services & solutions

Popular technologies to consider for data warehouse creation

Choosing appropriate software for a DWH project ranks among the tips for building a data warehouse from scratch. Why? Because it will impact the data integration capabilities, data security, built-in connectivity with BI and analytic services, and – ultimately – the reliability of the solution you aim to develop. Here is the list of three tools that display the highest customer satisfaction performance and follow the principles of DWH modernization. 

Amazon Redshift 

This solution is an unquestionable leader in the niche by many accounts. It provides built-in cloud data integration with Amazon S3 and federated query capability, enabling the DWH to query and analyze data of any kind, format, or size. Furthermore, the analytical power of the tool is remarkable due to the native integration with a range of AWS services honed for this purpose, such as Amazon EMR, Amazon QuickSight, AWS Lake Formation, and Amazon SageMaker, to name a few. 

Data management characteristics of DWH built with this technology allow for decoupling computer and storage resources as well as for ingesting and transforming data batches and streams with the help of AWS Data Pipeline, AWS Glue, AWS Kinesis Firehose, and AWS Data Migration Services.  

One of the fortes of Amazon Redshift is its focus on data safety and system security. These are safeguarded thanks to built-in disaster recovery and fault tolerance capabilities, multi-factor user authentication, data encryption, granular permission on tables, and a complete roster of compliance requirements it meets (including HIPAA, SOC1, 2, and 3, ISO 27001, PCI DSS Level 1, and more). 

Finally, being a brainchild of AWS, this technology utilizes Amazon Relational Database Service to support PostgreSQL as a powerful database system popular among DWH developers. 

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Azure Synapse Analytics 

Its native integrations embrace Azure Machine Learning, Azure Data Lake Storage, Azure Cognitive Services, Power BI, and others. Native HTAP support is affected via Azure Synapse Link. The tool enables separate scaling for computation and storage, supports big data and streaming data ingestion and processing via leveraging its Azure Stream Analytics and Apache Spark tools, and relies on Azure Data Factory to provide about a hundred native connectors to access both cloud-based and on-premises data sources. Its default security and backup features are on the same high level as is the case with the previous technology.  

Google BigQuery 

It can boast an even bigger number of native data integrations (over 150) as well as multi-cloud analytics support. Thanks to it, you don’t need to copy data either from Azure or AWS to query any information out of these systems. And it goes without saying that it comes with all native integrations across the entire Google Cloud Analytics ecosystem.  

What differentiates this technology from the two previously mentioned ones is separate charges for storage and computing resources and cold/hot data. Moreover, its replicated storage in multiple locations is free of charge! In addition, Google BigQuery’s compliance and security measures closely follow its two counterparts in reliability and sustainability. 

Of course, you can use other development tools, frameworks, and languages (such as Oracle, Bootstrap, Angular, Selenium, etc.) and collaboration software (Jira, Git, Trello, Slack, or others) that you are comfortable with.  

When you have identified the tech stack for your project, the second crucial choice to make if you want to build a data warehouse is to opt for an approach to DWH creation.  

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Approaches to implementing a data warehouse

There are two fundamentally opposite design methods developers onboard for creating a DWH. 

Inmon’s approach 

Also known as a top-down approach, this technique is based on building a centralized data repository as the starting point in DWH development. Separate tables with data related to a specific subject are arranged in groups that are connected with the help of joins. After this stage is accomplished, the information the repository contains is further leveraged in data mart structuring.  

We recommend this method when an organization prioritizes obtaining consistent, accurate, and reliable data to serve as a single source of truth. It enables users to develop data marts promptly since no time is wasted on extracting information from original sources, cleansing it, and performing other preliminary operations with the data. However, the setup and implementation here take much time and effort compared to the second technique. 

Kimball’s approach 

Adopting it, developers move bottom up and create dimensional data marts first. After that, you can unite them into a single enterprise data warehouse. Since the data marts are quick to normalize, developers can proceed much faster than those who prefer Inmon’s approach. On the flip side of it, you will have to repeat ETL actions and watch for a lot of redundant data in tables because each data unit is built from scratch independently.  

Which method should you opt for? The choice is conditioned by the characteristics of a particular project, but in most cases, you can enjoy the best of the two approaches by combining the principles of both. Anyway, the authority of selection lies within the team of experts you hire to implement your DWH project – this is the third vital decision you must make.  

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Team for a data warehouse implementation project

Our high-profile expertise in DWH consulting and creation is a solid ground for us to define the must-have positions you will have to fill to implement a data warehouse project.  

1. Project manager. This is the person ultimately responsible for the success of the job. (S)he determines the scope of the project, its deliverables and draws the project plan, where resourcing, timeframes, and budget estimation are the key components. When the plan is being put into execution, the PM manages resource coordination, performs project status tracking, controls progress and communication within the team, deals with pipeline bottlenecks – in a word, supervises all day-to-day activities and project tasks.  
2. Business analyst. First, this specialist pinpoints business requirements set by the customer and makes sure the tech crew is aware of them. (S)he also conducts documented interviews and assists data experts in data modeling, data mapping, and other related activities. 
3. Data modeler. On the basis of a preliminary detailed data analysis, (s)he designs the architecture of the DWH as a whole and each of its elements (including data staging, storage, models, and more) in particular, plus documents the overall scope and its components. Then, this person advises on the tech stack to choose and controls the full-cycle process of architecture development and implementation. 
4. Data warehouse database administrator. The DBA transforms logical models into the table format, provides the operational support of the database, and ensures data integrity and accessibility by fine-tuning database performance. Also, this specialist is responsible for creating a data recovery and backup strategy.  
5. ETL developer. (S)he deals with planning, developing, and setting up the extraction, transformation, and loading pipelines. 
6. QA engineer. His/her overarching goal is to ensure the proper functioning of the DWH and the accuracy of the data it contains. With this aim in view, (s)he creates a testing strategy, detects potential errors, eliminates them, and executes various tests on the finished solution. 
7. In addition to these core roles (several of which may be performed by one person, by the way), you can reinforce the team with a data steward, a DWH trainer, a solution architect, a DevOps engineer, and any other expert that can add value to the project implementation and facilitate its progress. 

Now that all preparatory and planning stages are over, you can proceed to the DWH project implementation. 

5 steps to build a data warehouse

The multiple completed DWH projects enabled DICEUS to provide a universal development strategy for creating data warehouses.  

Step 1. Identifying business requirements and objectives 

You can’t successfully develop any software solution unless you know exactly what outcome the customer expects. In the case of DWH creation, business requirements define many aspects – from the kind of data it will contain to the frequency of its usage. To understand it, we interview users and discover business goals, core processes, major issues the company faces, KPIs it steers by, types of data analysis performed by users, etc.  

An essential part of the interview is the communication with the customer’s IT department, during which we figure out technical details of the software the company utilizes (like operational systems it uses, available tools to retrieve and analyze information, insights that are typically generated, and so on).  

Step 2. Conceptualization and tech stack selection 

Having all the necessary information at our fingertips, we adopt an architectural approach to creating a DWH and select the optimal combination of tools to implement it. The choice of the tech stack is influenced by the current environment, expected strategic technological directions, competencies of the in-house IT team, deployment type (cloud, on-premises, or hybrid), data nature and volume, number of users, data security requirements, and more. 

Step 3. DWH environment design 

To create it, we apply various modeling techniques (for instance, a normalized schema or a star schema) used to organize the customer’s data into entities (real-world objects) with logical relationships between them and attributes (characteristics of these objects). Then, these logical models are transformed into database structures (tables, columns, foreign key constraints, etc.). After such modeling operations are completed, we define the source-to-target dataflow, fill the DWH with aggregated data, and control the entire process closely.  

At this stage, taking thought for data access and usage policies is vital, and setting up the metadata catalog and establishing business glossaries is critical. 

Step 4. DWH implementation and launch 

The implementation itself consists of several procedures. To begin with, we configure and customize the onboarded technologies (such as the DWH platform, data transformation tools, data security solutions, etc.). Secondly, ETL pipelines are developed, and data security measures are introduced. Then, all these components are integrated with the current data infrastructure, to which data from other sources is migrated afterward.   

When these processes are over, we verify data legibility, accuracy, consistency, and security, test DWH’s performance, and ensure users have access to the new system and can handle it efficiently. The latter is achieved via customized training and detailed support documentation. 

Step 5. After-launch maintenance and support  

As a high-profile software vendor, DICEUS extends support and maintenance services to all our clients, even if they commission nothing more than a simple mobile app. In the case of such a serious solution as an enterprise data warehouse, support and maintenance are a part of the package deal. After the DWH is put into operation, we keep in touch with its owners to measure its performance metrics, fathom user satisfaction, and ensure the system’s long-term robust functioning and sustainable growth.  

As you see, choosing the DWH architecture, selecting development tools, onboarding a certain approach, and implementing the project is a no-joke task that should be entrusted to professionals. Seasoned mavens of DICEUS have sufficient competence and experience to tackle such a project and deliver a high-end solution that will impress you with seamless functioning, sure to add value to your business, whatever industry you specialize in.  

Key takeaways

In the highly competitive business environment, the master of information is the master of the situation. However, many companies fail to properly use the plethora of business, product, and client data they have at their disposal. Such dossiers can be efficiently handled if they are pooled together and cleanly structured within a data warehouse. To ensure its seamless performance, you should recruit qualified experts who will map out its architecture, adopt a proper approach to its building, harness adequate software tools, and meticulously follow the DWH implementation roadmap.  

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