Whatever your business does, data is its lifeblood. But without the right systems in place, data can become a liability instead of an asset. Data lake consulting by Intellias organizes your raw, previously unusable data into a trustworthy resource your business can actually use. We integrate with your existing systems, clean up messy data, design custom data workflows that fix quality issues, and architect a scalable repository.
At Intellias, we offer more than enterprise data lake engineering services. We deliver data lakehouse solutions that bring all your organization’s data together and categorize it to support real-time analytics, predictive modeling, and AI-driven automation.
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Unite your scattered data in one place so analysts can work on what they need efficiently.
Build systems that grow with your business without performance issues as data volumes increase.
Meet compliance requirements with secure data management protocols tailored to industry standards.
Turn complex datasets into clear answers using purpose-built business intelligence and analytics tools.
Technical by nature, data lakehouse solutions serve a business purpose: consolidating and organizing all company data for easier access. We use an iterative development method, focus on scalability, and align with your goals to solve practical issues.
Identify and link data lakehouse services to company targets and analytical use cases by mapping relevant data sources to ensure they support strategic objectives.
Develop a modular architecture with layers for data ingestion, storage, processing, and consumption, emphasizing scalability, schema-on-read flexibility, and raw data fidelity.
Use AWS, Azure, or GCP to establish a robust infrastructure, build ETL/ELT pipelines for data ingestion and transformation, and implement metadata management for data discovery and oversight.
Implement role-based access control and automate data quality checks to maintain security and integrity while also ensuring compliance with regulatory requirements.
Integrate your data lakehouse with analytics platforms and machine learning environments to gain deeper insights and improve structured querying.
Monitor performance and resource use to identify bottlenecks, optimize workflows and support through audits, team training and feedback, ensuring friction-free adaptability.
Our cloud data lake engineering services focus on creating scalable data lake and data lakehouse solutions that make working with raw data easier — without compromising on technical standards. Intellias data lakehouse experts and data lake consultants will develop a data ecosystem that’s flexible for analysis and strengthens data governance for your teams.
Ingest raw structured, semi-structured, and unstructured data immediately, applying schema only when needed for analysis.
Handle petabyte-scale growth at a fraction of traditional data warehouse costs using distributed computing frameworks.
Process streaming data as it arrives so time-sensitive tasks like fraud detection or dynamic pricing can be carried out in real time.
Provide the granular, raw data that machine learning algorithms require for pattern discovery and predictive modeling.
Create a single source of truth that’s accessible to engineering, analytics, and business teams with appropriate governance.
Keep information in its native format to stay compatible with new technologies and unexpected future use cases.
Implement strong metadata management and governance to keep your datasets organized without losing flexibility.
Eugene Berko
Director of Technology Practices
Director of Technology Practices at Intellias with 13+ years of experience in software engineering, management, and consulting. The area of Yevhen’s responsibility is the company’s technology offering in the fields of Data & AI, Cybersecurity, IoT, Cloud & DevOps, Support, Intelligent Automation, Business Applications.
Driven by passion for learning and making a difference, Yevhen is also a firm believer in flexibility and providing a unique solution to each problem rather than following 300+ page frameworks. He often speaks publicly and is interested in the topic of using Generative AI to solve real-world business problems as well as the art and science of governing AI.
Viktor Sokolov
Head of Data Governance Unit
Viktor brings 20 years of expertise in data and analytics, with a strong focus on the finance, retail, and wireless communications sectors. His key areas of specialization include data governance, data warehouse development, data modeling, and the implementation of BI systems for large-scale data environments.
As the Head of the Data Governance Unit at Intellias, Viktor works with clients to implement strategic frameworks that ensure regulatory compliance, uphold data integrity, and maintain high standards of data quality.
A data lakehouse brings together the flexibility of a cloud data lake with the structured querying power of a data warehouse. It allows your business to pull in raw data from multiple sources — IoT devices, applications, internal systems — without the need for upfront cleaning or transformation. This setup supports real-time analytics with fast access to both structured and unstructured data, reducing the lag between data ingestion and insights. By working with professional data lake consultants, you will be guided through data lakehouse implementation to fit your specific operational and analytical needs. The right data lake consulting services help you identify what data matters, how to manage it over time, and how to apply it in live environments so you can catch issues early, respond faster, and make decisions based on current, trustworthy data.
Think of a data lake as a flexible container for everything: structured data, logs, images, sensor data, and more. A data lake is great if you’re working with messy or mixed data types and want room to explore. Data lakehouse solutions bridge these approaches, offering the flexibility of a data lake with the performance and structure of a data warehouse. If your business needs both agility and reliability when dealing with various sources, data lakehouse services might be the optimal solution. On the flip side, a data warehouse is designed for clean, structured data, making it perfect for consistent reports like sales trends or financial summaries. Enterprise data lake services make sense for collecting raw data, while data lakehouse solutions provide more structured analysis capabilities on top. For more traditional BI reporting, warehouse management could still be useful. These days, many businesses are turning to hybrid setups, like data lakehouses, to get the best of both. Enterprise data lake consulting can help you determine the right architecture for your specific needs.
Security and governance require careful planning. A cloud data lake consulting team specializing in data lakehouse solutions can help you set clear access, ownership, and compliance rules from the start. Begin with clear rules around data ownership, access levels, and compliance requirements (think the GDPR or HIPAA) to keep things clean and accountable. Data lakehouse solutions offer additional governance advantages through built-in schema enforcement and ACID transactions. On the security side, look for cloud data lake engineering services that offer built-in encryption, access controls, and regular audits. Most major cloud platforms (like AWS, Azure, and GCP) offer tools to make this easier — if set up correctly by a data lakehouse specialist. A solid data lake consulting firm can help you define policies from day one so you’re not stuck cleaning up later.
While data lakehouse implementation can be more affordable up front than maintaining separate data lake and data warehouse systems, there are some things to watch out for:
Working with an enterprise data lake engineering services vendor that specializes in data lakehouse consulting can help you build smarter policies (such as data lifecycle rules, query optimization, or automated cleanup workflows) to keep costs and complexity in check.
A data lakehouse builds upon a traditional data lake architecture by adding a transactional layer and structured metadata management. While enterprise data lakes primarily focus on storing raw, unprocessed data in its native format, data lakehouses add warehouse-like features such as ACID transactions, schema enforcement, and optimized SQL query capabilities. This means you can perform reliable business intelligence directly on your data in a data lake without moving it to a separate data warehouse. With data lakehouse implementation by our data lake experts, you get the best of both worlds: the flexibility and scalability of a data lake along with the reliability and performance of a data warehouse, all in a single, unified platform.
We don’t just build data lakehouse solutions. We help you organize your data, understand it, and trust it to meet your business needs.
