How to Build Data Infrastructure: 7 Key Steps & Tips

Sirojiddin Dushaev Lead Data Engineer & Cloud Solutions Architect

Oleksandr Kolosov Technical Lead of Machine Learning

A data infrastructure is the backbone of every modern business. Yet, just having loads of information is not enough on its own. According to Statista, almost 89% of companies said that investing in data and analytics is their top priority. But here’s the catch: only 37% felt that their efforts to improve data quality had actually been effective.

What’s the problem? 

Many companies try to use data that is not organized properly, or does not meet critical business needs: and without a solid foundation, even the best data strategy can fail.

So how do you build a robust data analytics infrastructure that works – one that helps you make smarter decisions and stay ahead of the competition? Let’s break down the process step by step.

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Article Highlights:

• Companies with strong internal data policies are 23 times more likely to outperform their competitors. But here’s the challenge — 77% of businesses struggle to use their data effectively; 
• By prioritizing data projects, companies can focus on initiatives that directly impact revenue, efficiency, and compliance;
• Real-time data processing tools like Apache Kafka can improve customer experiences by helping businesses instantly personalize their offers.

Data Infrastructure Architecture: Types and Components

Imagine that data infrastructure is the foundation of a house. Just like a solid foundation supports a home, a well-structured data infrastructure provides stability and reliability. In this guide, we’ll break down the key components and types in a simple way. 

Deployment Models

Businesses have a wide array of options to build modern data infrastructure, and each has its advantages and trade-offs. First, we need to look at deployment models, which define how and where data is stored and processed. 

On-Premises

This is the traditional type of data warehousing solution where all your data is stored, processed, and managed on physical servers on site in your office or at a dedicated data center. 

• Pros: Full control over security and performance.
• Cons: Greater investment in hardware, IT staff, and maintenance.
• Best for: Companies with high data security requirements and industries with confidential information (such as finance or healthcare).

Cloud

With cloud data infrastructure and analytics, your data is stored and processed on remote servers run by providers like AWS, Google Cloud, or Microsoft Azure.

• Pros: Scalability, cost savings, and remote access.
• Cons: Dependence on an internet connection.
• Best for: Startups, companies looking for cost-effectiveness, and companies that need the flexibility to scale as needed.

Hybrid

The hybrid approach combines on-premises and cloud infrastructures.

• Pros: Balance of control and flexibility (sensitive data remains on the local network while other workloads benefit from the scalability of cloud services);
• Cons: Requires expertise to integrate and maintain both environments.
• Best for: Companies that must balance security and flexibility, migrate to the cloud, or manage diverse workloads.

Data Storage Architectures

Data storage architecture defines how data is structured and integrated within an organization. In other words, the main focus of architecture is design and strategy.

Data Lake Architecture

Data lake architecture supports storage for various data types and enables big data analytics, AI/ML workloads, and real-time processing.

• Pros: Supports various data types, including raw, semi-structured, and structured data. With this flexibility, companies can conduct complex, large-scale analytics and customize their data warehousing solutions. This approach makes data lake architecture ideal for numerous data management needs.
• Cons: Requires effective management to avoid becoming a “data swamp.”
• Best for: Data infrastructure examples with large amounts of diverse data and those working with AI/ML models.

Data Warehouse Architecture

This type of data storage solution is designed for structured data and complex queries, retrieving data through an ETL (Extract, Transform, Load) approach.

• Pros: Offers fast query performance, optimized for reporting and business intelligence.
• Cons: It is less flexible for unstructured data and requires predefined schemas.
• Best for: Enterprises relying on structured data for reporting and decision-making.

Lakehouse Architecture

This type of architecture is a hybrid approach combining the storage capabilities of data lakes with the structured query efficiency of data warehouses. 

• Pros: Creates a balance between structured queries and flexible data storage.
• Cons: Requires complex infrastructure setup and can be costly to maintain.
• Best for: Organizations that need both large-scale analytics and structured queries.

The True Cost of Data Silos & How to Eliminate Them Continue reading

Processing & Integration Architectures

Processing and integration architectures take care of the ways your data moves and is processed.

Event-Driven Data Architecture

This is one of the emerging architectures that enables real-time data processing.

• Pros: Instant data processing and supporting real-time decision-making.
• Cons: Requires high-speed infrastructure and real-time monitoring.
• Best for: Businesses that need immediate information, for example financial institutions and IoT-based services.

Microservices-Based Data Architecture

This approach breaks down data services into independent components that offer flexibility and easy integration.

• Pros: The ability to upgrade independently without disrupting the entire system.
• Cons: Requires complex service coordination and robust API management.
• Best for: Companies that require high flexibility and modular data management, such as SaaS providers.

Not sure which option is right for you? CHI Software is here to help you choose! Let's talk!

Key Components of Data Infrastructure

Regardless of the type of infrastructure you choose, certain components are must-have to manage and utilize your data.

Database infrastructure design includes data storage, data processing, and data integration & management components.

Data Storage Solutions

Where is all your business data stored? The right storage solution guarantees availability, security, and scalability. There are different types of storage, including:

• Databases: structured storages for organized data (e.g., SQL databases).
• Data warehouses: large-scale storages optimized for analytics (e.g., Snowflake, Google BigQuery).
• Data lakes: storages for raw, unstructured data that can be processed later (e.g., Amazon S3, Azure Data Lake).

Data Processing Frameworks

Data is useless if you can’t process it efficiently – data processing frameworks and pipelines are the key for turning raw data into insights.

• Batch processing can manage large amounts of data simultaneously (e.g., Apache Hadoop, Spark).
• Stream processing analyzes data in real time as it arrives (e.g., Apache Kafka, Flink).

Data Integration and Management

Businesses often need to organize and combine data from different sources like sales, marketing, and customer interactions to keep it consistent.

• ETL (Extract, Transform, Load) tools help to move and refine data from different sources to a central data storage (e.g., Talend, Apache NiFi).
• APIs and middleware create seamless interaction between different systems.
• Data governance policies and tools provide accuracy, security, and compliance with regulatory requirements.

How Building Data Infrastructure Benefits Your Business

With the right foundation in place, you can turn data into one of your most valuable assets. Let’s break down the benefits.

Building data infrastructure can not only optimize your internal workflows but also improve your relations with clients.

Faster and Smarter Decision-Making

Many businesses often – especially when they’re just starting out – make decisions based on guesswork. But as your business grows, relying on intuition becomes riskier. Imagine having the data at your fingertips to guide every decision you need to make. With a well-built data analytics infrastructure,you can make smarter, more confident choices and avoid the mistakes that guesswork might lead to. 

For example, if you work in the e-commerce industry, your team could leverage data for tracking customer buying habits and increase sales.

Increased Efficiency and Productivity

Without the right data management infrastructure, your team may spend hours searching for information, manually entering data, and correcting errors. A good system automates these processes and frees up time for your employees to focus on more important tasks.

For example, a CRM integrated with your data infrastructure can automatically update customer records, track interactions, and suggest the best time to follow up with your prospects. 

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Scalability for Growth

As your business grows, so does the mountain of data. If your infrastructure isn’t scalable, you’ll eventually hit a wall of poor performance, storage issues, and security risks. But if your company plans wisely from the beginning and creates a scalable data infrastructure strategy, you can expand without worrying about costly system upgrades.

Increased Security 

What can be worse for a business than a data breach? A reliable data infrastructure protects sensitive information through encryption, access control, and regular backups.

Compliance is also important for businesses in regulated industries such as finance or healthcare. A proper database infrastructure design assures you meet legal requirements and avoid fines or reputational damage.

Better Customer Experience

At the end of the day, your customers expect an excellent experience, and that’s exactly what data infrastructure allows you to create. 

For example, if you run a hotel, your data system can track guest preferences – the type of room they prefer, eating habits, or previous stays – allowing you to create a personalized experience. Personalization not only improves customer satisfaction but also increases loyalty.

7 Steps to Build a Data Infrastructure 

Where do you start with data infrastructure development, and how do you build a scalable, secure, and future-proof system? Let’s take a look at seven critical steps.

Data infrastructure and analytics succeed only when you follow a clear step-by-step process.

1. Define Your Data and Analytics Strategy

Before diving into the technical setup, build a clear plan and try to answer the following questions:

• What are the business decisions that you want to improve with data?
• What insights do different teams require in order to make decisions? (Finance, marketing, sales, etc.)
• What regulatory or security requirements do you need to meet?

If you can clearly define your priorities, you will prevent wasted time and resources and build an infrastructure that serves your business goals. You can also identify short-term and long-term priorities to scale your infrastructure efficiently.

2. Prioritize Your Data Projects

In case your team does not have clear priorities, your company may waste time on projects that don’t bring real value.

How do you prioritize? First, try to focus on initiatives that directly impact revenue, efficiency, and compliance. 

Don’t forget that some projects will require more effort due to legacy systems or integration complexity – so consider both factors when creating your priority list. As an option, you can use a prioritization matrix:

• High-value, easy-to-implement projects → Start immediately.
• High-value, complex projects → Plan for the long term.
• Low-value, easy-to-execute projects → Execute only when resources allow.
• Low-value, complex projects → Avoid or reprioritize.

We highly recommend focusing on your specific needs when developing a data infrastructure strategy.

3. Choose the Right Environment

Choosing an environment that meets your needs can be a daunting task, so we’ve compiled a checklist to help you set benchmarks and show you the options that will meet your goals. Start by answering the following questions:

Do you process sensitive data that needs to stay on-site (e.g., financial, medical, government data)?

• Yes: An on-premises infrastructure gives you complete control over security, compliance, and infrastructure.
• No: Consider cloud or hybrid options.

Do you need real-time data processing for tasks like fraud detection or IoT application notifications?

• Yes: Cloud solutions with an event-driven architecture (e.g., AWS Kinesis, Apache Kafka) can efficiently handle real-time workloads.
• No: Local data stores may be sufficient.

What is your budget for infrastructure setup and maintenance?

• Limited budget: Cloud-based data storage options usually offer a pay-as-you-go payment model.
• If higher initial investment is possible: On-premises infrastructure provides long-term control but requires higher setup and maintenance costs.
• If a balance of costs is needed: A hybrid model allows you to optimize costs by keeping critical data on-premises while using the cloud to scale.

Does your infrastructure need to integrate with multiple external systems (e.g., CRM, ERP, BI)?

• Yes: Cloud or hybrid solutions offer better integration with external tools via APIs and built-in connectors.
• No: On-premises solutions work if your operations are primarily internal.

Does your business rely on real-time data processing (e.g., fraud detection, IoT, instant analytics)?

• Yes: Event-driven data architecture processes data instantly as events occur, making it essential for real-time applications.
• No: Other architectures may be more suitable for batch processing and historical analysis.

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4. Create a Scalable Data Model

The data model defines how information is structured and organized, and how easily you can access and analyze it. Moreover, without a robust model, you may be running the risk of inconsistent data, duplicate records, and poor reporting accuracy.

Best practices for data modeling include:

• Using a relational database model, which provides structured and linked data (e.g., MySQL, PostgreSQL);
• Optimizing performance with the help of OLTP (real-time transaction processing) or OLAP (integrated analytics and reporting) based on your business needs;
• Developing models that can handle increasing data volume without performance challenges;
• Using a data warehouse (e.g., Snowflake, BigQuery, or Redshift) to centralize data from multiple sources.

5. Document the Data Lineage

The data lineage plays a pivotal role in IT infrastructure analytics because it tracks the flow of data from its origin to the final reports, and: 

• Gives you precise information about where data comes from and how it has been processed;
• Helps IT teams track errors and inconsistencies and significantly reduces setup time;
• Is a mandatory element for regulations such as GDPR and HIPAA.

6. Evaluate and Optimize Performance

To keep your infrastructure running at the highest level, you need to keep a close eye on it, and here’s what you should look for during general checks:

• Data storage: Is your database effectively structured? If you’re not sure, use indexing and partitioning to make queries run faster;
• Query performance: Are reports running fast enough? If you are not satisfied with the speed, then optimize SQL queries and consider caching;
• ETL processing: Are you refreshing data too often? Use incremental data loading to reduce processing time.

7. Implement Data Governance and Security

A data governance program is the element which guarantees that data is accurate, secure, and only available to the right people.

Pay your attention to:

• Setting up access control and assigning roles according to each user’s responsibilities;
• Ensuring that all records follow standardized formats (e.g., correct date/time formats, unique customer identifiers);
• Maintaining records according to industry regulations;
• Regular monitoring and logging data activities to detect suspicious behavior.

Need help creating and implementing your data infrastructure? Our team will guide you through every step of the process!

How CHI Software Can Help You Succeed

Data-driven companies are 23 times more likely to outperform their competitors, but we have to keep reality in mind: 77% of companies struggle to use their data effectively. The reason for this is an unreliable company data strategy.

Data engineering is one of the key expertise areas of our AI/ML department.

At CHI Software, we create customized data infrastructures adapted to your business needs. That’s what sets us apart as data engineering company:

• We have more than six years of excellence in data engineering, with more than 30 successful data projects with measurable benefits such as 40% cost savings and 20% faster operations;
• 70% of our data engineers are senior-level professionals;
• CHI Software’s team is certified in Google Cloud, AWS, Azure, and Oracle;
• Our solutions deliver measurable results, from reduced infrastructure costs to 2x faster query performance.

What Do We Develop and How Does It Work?

CHI Software helps companies collect data from various sources and combine it into a structured system. Our expertise includes:

• Databases: relational (PostgreSQL, MySQL, MS SQL) and NoSQL (MongoDB, DynamoDB).
• APIs and web services: REST, GraphQL, SOAP (CRM, ERP, financial services).
• File storage: CSV, JSON, Parquet, XML in cloud platforms (AWS S3, Google Cloud Storage, Azure Blob).
• Real-time data streams: IoT devices, sensors, and digital behavioral tracking.

Proven Success Stories in Big Data Development

The best way to demonstrate our professionalism in providing big data development services is to point to our clients’ results.

From Fragmented Insights to 99% Accuracy

The world’s leading performance marketing company in the online gambling and financial sector urgently needed to improve their business intelligence. The company constantly faced high operational costs, especially due to using Azure Analysis Services cubes, and struggled with fragmented data from multiple marketing platforms such as Google Analytics and Voonix, making it difficult to get accurate and timely information; thus impacting the entire company. 

Smart data management and improved data infrastructure were crucial for our client’s business intelligence.

The CHI Software development team reduced infrastructure costs by switching from these expensive cubes to materialized views in Azure Synapse. In addition, our team collected all marketing data in one centralized data warehouse and used Azure DataBricks to accelerate analytics. As a result, the company received:

• 2x faster data processing;
• 99% data accuracy;
• 5x increase in data scalability;
• 15% boost in marketing ROI.

Do you want to achieve similar results for your business? We know how to make it happen! Share your goals with our team

Data Migration & Faster Reports

Similarly, a leading mobility and technology company needed to streamline its data infrastructure for better reporting and decision-making. Their data was scattered across multiple systems, and this was slowing down their ability to collaborate and make timely decisions. 

Building data infrastructure helped our client double the speed of report generation.

• As a solution, CHI Software engineers centralized the key business reports into one data warehouse and automated the data integration process with Airflo. 
• We also used Hive and Spark EMR to speed up query execution and OpenMetadata to improve data management and increase data accuracy. 
• Finally, we updated Superset’s dashboards to provide real-time visibility into business metrics. 

With the help of CHI Software, the company:

• Completed a 100% successful data migration with no outages;
• Improved query performance and accelerated report generation by twofold;
• Reduced manual work with data, increasing operational efficiency.

Conclusion 

When you have a well-structured data foundation, you can easily turn raw data into insights to increase efficiency, and stay ahead in a competitive market. But it’s worth remembering that building a robust infrastructure isn’t just about choosing the right tools, it’s about creating a system that truly supports your business goals.

CHI Software, as a company with extensive experience, understands that no two companies are alike and can tailor solutions to your unique needs. Are you ready to change the way your business works with data? Let’s build something great together.

About the author

Sirojiddin Dushaev Lead Data Engineer & Cloud Solutions Architect

Sirojiddin is a seasoned Data Engineer and Cloud Specialist who’s worked across different industries and all major cloud platforms. Always keeping up with the latest IT trends, he’s passionate about building efficient and scalable data solutions. With a solid background in pre-sales and project leadership, he knows how to make data work for business.

Oleksandr Kolosov Technical Lead of Machine Learning

Oleksandr holds a Ph.D. in Probability Theory and Math Statistics and has a strong background as both a professor and engineer. He's worked with leading services like AWS and Azure, bringing expertise in machine learning, databases, and web applications. With skills in Python, .NET, JavaScript, and more, he's well-versed in building and optimizing tech solutions.