Data is the oil of the 21st century
Gain deep insights into products, customer behaviour, and business processes
Upgrading your data solution
We’ll help you to upgrade legacy data processing technology to optimize costs and improve system scalability.
AI/ML enabling existing product
Innovating your data processing approach with AI/ML technologies to meet your evolving business needs with automation and speed.
Scaling your business safely
With the right data engineering tools, you can effortlessly handle growing data volumes and user demands. Your business can expand its data infrastructure without sacrificing performance or stability.
Optimizing costs
By streamlining workflows and eliminating inefficiencies, your organization can reduce infrastructure expenses and maximize the value of your datasets.
Powering your business decisions
Your business can iterate over metrics and reporting much faster. Let us assist you in upgrading or building your data analytics stack.
Fuel businesses with data solutions across industries
Data services we provide
Data engineering consulting services
- Data quality assessment
- Data strategy development
- Data infrastructure refinement
- Evaluation of data governance and security procedures
Data architecture services
- Elicitation of functional and non-functional requirements
- Data structure development based on business needs
- Selection of an optimal architectural framework
ETL/ELT services
- Custom ETL workflow development
- Scheduling and orchestration of ETL jobs
- ETL performance optimization
- Data cleaning, validation, and enrichment
Data pipeline services
- Custom data pipeline development
- Scheduling and orchestration of pipeline jobs
- Real-time data streaming pipelines
- CI/CD for data pipelines
- Pipeline monitoring and troubleshooting
- Pipeline integration with BI tools
- Pipeline scaling for big data
Data storage services
- Data warehousing
- Data lake design, implementation, and management
- Data mart creation and management
On-premises to cloud data migration
- Migration to Google Cloud
- Migration to AWS
- Migration to Azure
Data integration services
- API integration
- Legacy system integration
- Real-time data streaming integration
Data modeling services
- Data warehouse modeling
- Multi-dimensional/OLAP modeling
- Document/NoSQL data modeling
- Model optimization and refinement
- Model documentation and metadata management
Database services
- Database design and modeling
- Database monitoring, optimization, and maintenance
- Data migration between databases
- On-premises or cloud database deployment
- Data backup and recovery procedures
From idea to MVP execution within 24 hours
Project stages and flow
Our priority is developing a data engineering solution that will boost your business, no matter when it integrates — pre or post product release
Technology we use
Cloud Platforms
Open Source Tools
Data Integration Tools
Data Storage and Analytics Solutions
BI Tools
Machine Learning Libraries
Data Science Tools
Downloads
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Learn how to build a high-performing outsourcing team with KITRUM’s guide
Businesses get over an 11% profitability boost from their investments in data and analytics
Empower decision-making with clear analytics
Our achievements
Tell us about the challenges you've faced with
AWS vs GCP 2026. Comparative Guide: CDN, pricing, and biggest drawbacks
Microsoft Azure vs GCP vs AWS Comparison Guide 2025
Multicloud vs. Hybrid Cloud: Which Strategy Fits Your Business Needs?
Client-Server Architecture. Advantages and Disadvantages of the Network Computing Model
Data Ecosystems: How to Simplify the Delivery of Complex Data Management Infrastructure
Is Microservice Architecture Still a Trend in 2026?
IT Infrastructure Costs Optimization: Is Migrating to the Cloud Necessary?
5 Questions to Answer Before Data Migration
Store, Organize and Analyze: How Big Data Can Empower Your IoT app
How to Create a Big Data Developer Team?
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Migration Strategies: What to Consider When Migrating From Azure to AWS
Green Cloud Computing: How Does Moving to Cloud Help Sustainability?
Three IoT Architecture Layers in IoT Systems & Their Security Threats
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In-Depth Exploration of the Steps in an ELT Process
Unpacking the Power of Docker Containers
Other services
FAQ
What is data engineering?
Data engineering is about crafting and maintaining systems that handle massive amounts of data, making it usable for analysis and decision-making.
What is a data engineering example?
Sure! Think of building a system that gathers data from different sources, processes it into a standard format, and stores it in a way that makes it easy to analyze later. That’s data engineering in action.
What is data engineering in AI?
In AI, data engineering focuses on preparing data for machine learning models. It involves cleaning up messy data, organizing it into useful formats, and ensuring it’s ready for training smart algorithms.
What are data management solutions?
Data management solutions are tools and setups that help companies manage their data smoothly. They include databases, data warehouses, and systems that keep everything organized, safe, and compliant.
