The model is rarely the hard part. The data behind it is. Azati builds, modernizes, and maintains the pipelines, warehouses, and data quality controls that make enterprise data reliable and usable across analytics, AI, and operational systems. Our Europe-based engineering teams work in Central European business hours.
Data engineering involves collecting data from business systems, cleaning and modeling it, and reliably delivering it to the reports, applications, ML models, and AI agents that depend on it. It includes data pipelines (ETL and ELT), storage platforms such as warehouses, data lakes, and lakehouses, data quality controls, and the orchestration that keeps data workflows running.
Data engineering builds and operates the data infrastructure: where data comes from, how it is cleaned and transformed, where it is stored, and whether it is available when needed. Data science uses that foundation to apply statistics and machine learning, identify patterns, and make predictions.
Both disciplines are connected, but they solve different problems. Azati brings them together when a project requires both reliable data foundations and analytical capabilities. For the data science side, see Analytics, BI & Predictive Models.
Most AI pilots don't stall because the model is weak. They stall because the data the model reads is late, duplicated, or defined three different ways in three systems. A model can't fix that. It repeats the problem, with confidence. Our experience comes from engineering and supporting data platforms over time, where changes to sources, schemas, and business requirements have to be handled without disrupting downstream systems.
On a US media data platform we have supported for more than eight years, the hard part was never the 90 TB of storage. It was 300+ ETL processes that depended on each other while sources, schemas, and business questions kept changing. That changed how we scope data work: a data platform is a production system with dependencies, not a warehouse project with an end date.
In an insurance and healthcare reporting warehouse, less reliable sources silently overwrote verified attribute values with blanks. The fix wasn't a bigger server. It was a rule, attribute by attribute, about which source wins. Load time dropped from 30+ minutes to under 5 as a side effect.
Data engineering becomes a priority when the data behind reporting, operations, or AI initiatives is no longer reliable, timely, or accessible enough to support the work.
Common signs include:
The right starting point depends on the problem. It may be one critical pipeline, a data quality issue, a warehouse migration, or an assessment of the data behind a planned AI use case. Azati helps identify the smallest practical intervention before recommending a wider rebuild.
Evaluate whether the data behind a planned AI use case is fit for purpose. Azati offers a fixed 3-week assessment of the data behind one planned AI use case: quality, freshness, lineage, access rights, and gaps, scored against what that specific use case needs. You get a ranked fix list and a roadmap your team can run with or without us.
Build and maintain batch and streaming pipelines, data warehouses, data lakes, and data marts. Azati integrates data from business systems, implements transformation and orchestration workflows, and puts quality checks into the pipeline so downstream applications receive consistent, usable data.
Modernize aging warehouses, databases, and ETL environments in controlled phases. Azati assesses existing dependencies, migrates data and workloads, and validates outputs throughout the transition to reduce disruption to reporting and analytical applications.
Improve the consistency, traceability, and reliability of enterprise data. Azati’s work includes data profiling, deduplication, survivorship rules, master data management, entity resolution, lineage, access controls, and audit-supporting records.
Reliable data foundations enable more effective business intelligence, predictive analytics, machine learning, and AI applications. Azati builds dashboards, segmentation, and forecasts on governed data, tying them to a decision someone actually makes: a lease, a reorder point, an ad slot.
Data engineering initiatives often involve four connected phases. You may need one or several, depending on the state of your platform and the outcome you need.
Most organizations do not need a full platform rebuild. We identify the changes that can address the most important data constraints, whether that means a pipeline, a quality rule, a governance gap, or a targeted modernization.
Find out where your data supports your AI plans and where it blocks them.
Replace the parts of the platform that can't carry the load, and keep the parts that can.
Make every number traceable to its source: lineage, ownership, access, quality rules.
Put analytics, models, and AI on top with Agentic AI Engineering, Knowledge Intelligence, Generative AI & Advanced ML Engineering, and Analytics, BI & Predictive Models.
We build and maintain pipelines, warehouses, and data marts, with ongoing involvement after deployment.
We migrate aging data platforms in phases, validating each step and preserving critical reporting and analytical workflows.
We address inconsistent, duplicated, and unreliable data in the underlying systems and pipelines rather than relying on downstream reporting fixes.
Our engineers can work within your data organization, using your processes, tooling, and delivery practices.
When required, we can extend the data foundation into BI, predictive analytics, and AI applications.
We do not assume that a platform replacement is necessary. We first assess the existing environment and recommend the changes justified by the technical and business requirements.
Azati's engineering team is based in Europe and works Central European hours, the same as Germany, Austria, Switzerland, the Netherlands, Belgium, and Luxembourg. Collaboration during Central European business hours, without routine overnight handoffs.
Azati builds for public cloud, EU private cloud, or fully on-premises. One research platform we architected runs the same codebase in the cloud or entirely inside a client's data center. An analytics copilot we built runs inside a client's closed network with no external cloud dependency.
Data platforms in regulated industries need more than reliable pipelines. They also need appropriate controls over data access, quality, lineage, and processing records.
The requirements depend on the organization, the data being processed, and the systems involved:
Governs the processing of personal data, including the purpose of processing, lawful basis, data minimization, retention, and security.
Establishes digital operational resilience requirements for financial entities, including the management and oversight of ICT-related risks.
Sets data governance and management requirements for training, validation, and testing datasets used by certain high-risk AI systems.
Azati engineers data platforms with lineage, access controls, quality rules, and relevant processing records so that technical teams can trace how data moves through the environment and support the organization's governance processes.
Regulatory applicability depends on the specific organization and use case. This is engineering guidance, not legal advice.
A large US media company needed to connect streaming, broadcast, advertising, promotion, and audience data to operational decisions. Its hybrid environment included 300+ ETL processes, approximately 90 TB of data lake storage, and 30 TB of application data, making reliable data processing, historical modeling, and infrastructure evolution increasingly complex.
Azati provided long-term media data engineering support across the client's data platform and analytical applications. The work covered ETL orchestration, data quality, historical modeling, database performance, analytics, and DB2 Warehouse migration while keeping interconnected media workflows operational as requirements and infrastructure evolved.
A national leader in customized insurance, claims, and patient safety & risk solutions was losing trust in its own reporting. Reference data arrived from multiple operational systems of inconsistent reliability, and the ETL process had no way to distinguish a complete, trustworthy record from a thin one – so a detailed, verified attribute could be silently overwritten by a blank or low-confidence value from a weaker source. Duplicate and conflicting entries piled up in the warehouse, reporting errors followed downstream, and the nightly ETL run stretched past 30 minutes, delaying same-day analytics.
Azati designed a rules-based Survivorship Matrix, built directly into the Oracle SQL ETL logic, that decides – attribute by attribute – whether an incoming value should overwrite the existing record or be discarded in favor of the higher-priority source already in the warehouse. The decision logic runs inline as data loads, with no separate reconciliation stage.
A large chemical enterprise operated dozens of interconnected business systems feeding a central analytical platform – hundreds of data flows and thousands of analytical tables, all continuously expanding as new business initiatives and systems came online. Business teams depended on this platform for reporting and operational decisions, but growing volumes and increasingly complex transformations meant existing pipelines and loading logic constantly needed rework just to keep performance and consistency from degrading.
Azati joined as an embedded extension of the client's data engineering organization – not a bolt-on vendor – developing analytical pipelines, data marts, and orchestration workflows using SQL, Python, dbt, Apache Airflow, and Apache NiFi, on top of Greenplum, PostgreSQL, and ClickHouse. The engagement ran continuously for 24+ months under a Scrumban delivery model built for constantly evolving requirements.
An international sport organization needed to centralize athlete and event data streaming in from live feeds, official results APIs, media outlets, CSVs, XML, HTML pages, and historical archives – with inconsistent naming conventions and no consistent identifiers linking the same athlete or event across sources. Every update, correction, and conflict was reviewed manually, a process that couldn't scale as feed volume grew, leaving administrators no automated way to catch anomalies before they reached a report or partner feed.
Azati built a four-layer platform: distributed ETL pipelines on Apache Spark for batch and streaming ingestion, a containerized microservices layer for entity linking and deduplication, an AI-assisted enrichment stage using NLP and embeddings for semantic search, and a governance hub with full versioning, conflict comparison, and audit trails.
Financial reporting data that arrives on time: one general-ledger extraction went from 3 hours to 10–15 minutes after we fixed Oracle statistics and query plans.
Survivorship-rule ETL modernization and master data that reconcile claims, policy, and reference data.
Data pipelines for healthcare insurance and claims reporting, plus patent and biological sequence data platforms.
Multi-terabyte audience, advertising, and event data feeding planning and targeting tools.
Production and engineering data turned into analytics that physical decisions depend on.
Analytical pipelines that stay consistent while new plants and systems keep coming online.
Forecasting, segmentation, and analytics copilots on top of existing warehouses.
Lineage, ownership, and quality you can defend to the board and the regulator.
A data platform every team can build on, instead of one pipeline per pilot.
Retrieval quality and governance in place before an agent goes live.
One governed source of truth instead of competing versions of business performance.
Extra engineers who join your sprints and tooling, not a parallel team.
| Layer | Technologies used in delivered projects |
|---|---|
| Warehouses & databases | IBM Db2 Warehouse, Netezza, Oracle, Microsoft SQL Server, PostgreSQL, Greenplum, ClickHouse, MongoDB |
| Pipelines & orchestration | dbt, Apache Airflow, Apache NiFi, Prefect, Microsoft SSIS, Apache Spark |
| Data quality & schema change | Great Expectations, Flyway |
| Search & retrieval | Elasticsearch, hybrid BM25 + vector search |
| Cloud & runtime | AWS (S3, EC2, Athena, SageMaker), Oracle Cloud Infrastructure, Docker, Kubernetes |
| BI & analytics | IBM Cognos, custom web dashboards, Python (pandas, scikit-learn, XGBoost, LightGBM) |
A fixed-scope review of one defined AI use case: the relevant data sources, data quality, freshness, lineage, access and availability, plus a prioritized gap analysis and remediation roadmap.
A scoped build or data migration with a team that owns the result.
Specialists who join your data team, your sprints, and your tooling. That's how our longest data engagements run.
We keep pipelines and models monitored and maintained after launch.
“The biggest misconception about AI is that companies think they're buying a model. They're actually buying data maturity. By the time a model or an agent does something impressive, the real work was done months earlier, in the pipelines nobody demos.”
Alexander
Data Engineering Team Lead
Tell us what systems hold your data, what needs to use it, and where the current setup falls short. Whether you need to assess AI data readiness, modernize a warehouse, stabilize pipelines, or improve data quality, we'll help identify a practical starting point. We'll review your description and respond within one business day.
Talk to a data engineering expertData engineering makes data reliable and available. Data science uses that data to explain and predict. A data scientist working on an unreliable pipeline spends most of the time cleaning data instead of modeling it, which is why we run both in one practice.
Usually yes, but rarely all of it. AI systems repeat whatever is wrong in the data they read. The useful question is which data a specific use case needs and whether that data is fit for it, which is what Azati’s 3-week assessment answers.
An AI data readiness assessment checks whether the data behind one planned AI use case is fit for it. Azati’s version is a fixed three-week review of quality, freshness, lineage, access rights, and gaps, scored against what that specific use case needs. The output is a ranked fix list and a roadmap your team can run with or without Azati.
Three weeks for one AI use case, with a fixed scope and duration. Each additional use case adds about a week, because quality thresholds for a customer-facing assistant differ from those for an internal forecasting model.
No, in most cases. Targeted fixes to integration, quality rules, and governance usually unblock more AI work than a replacement. When data migration is needed, we do it in phases. Azati’s IBM Db2 Warehouse migration for a media client was planned around the analytical applications that kept depending on it throughout.
Azati’s recent projects ran on IBM Db2 Warehouse, Netezza, Oracle, SQL Server, PostgreSQL, Greenplum, and ClickHouse, with dbt, Airflow, NiFi, Prefect, SSIS, and Spark for pipelines. We work with the platform you have before suggesting a new one.
Yes. In our longest data engagements, Azati engineers worked inside the client's data organization, in their sprints and tooling: 24+ months for a chemical manufacturer and more than eight years for a US media company.
Azati’s engineering team is based in Europe and works Central European hours, the same time zone as DACH and the Benelux. Our EU headquarters is in Warsaw.
Yes. Azati builds data and AI platforms for public cloud, EU private cloud, or fully on-premises deployment. That includes systems that run with no external cloud dependency at all.
Azati works inside client-controlled environments: access, credentials, and infrastructure are provisioned by you, and data stays where you decide, whether public cloud, EU private cloud, or on-premises. For regulated clients, we document lineage, access controls, and processing records, and support the third-party risk questionnaires you require under frameworks such as DORA and GDPR.
For high-risk AI systems, Article 10 requires documented data governance for training, validation, and test data, covering origin, preparation, quality, and bias checks. For stand-alone high-risk systems, this applies from 2 December 2027. Pipelines with lineage and audit trails make that documentation something you generate, not something you reconstruct. This is engineering guidance, not legal advice.
Most of Azati’s clients start with the 3-week assessment, or with one pipeline or data migration that is already causing pain. Send us a short description through the form below, and we'll reply within one business day.
There’s no single figure, because cost depends on the number of sources, the state of the existing platform, and how much needs rebuilding versus fixing. Azati’s AI data readiness assessment has a fixed scope and duration, and later builds or data migrations are scoped from its findings. Embedded engineers are scoped by team size and duration.
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