Most data projects stall when new sources keep arriving, an upstream schema changes, or a failed overnight load leaves a business team working from incomplete data.
Azati designs, builds, and extends the pipelines, warehouses, and orchestration that move business data from source systems to the people and models that depend on it. We work with batch and streaming data, on cloud, hybrid, and on-premises platforms. Our engineers can join your existing data team, deliver a defined engineering workstream, and provide production support after release.
On one US media data platform, Azati supported more than 300 interdependent ETL processes and a data lake of approximately 90 TB over an engagement lasting more than eight years.
A pipeline is a production system. The cost sits in running it for years, not in building it once.
New sources should arrive through configuration, not custom code. On a retail group’s ETL platform, load rules lived in a PostgreSQL configuration layer, so connecting a new table meant changing configuration.
Load testing belongs before release, not after an incident. Azati load-tested new flows under the client’s own procedure before they reached production.
The right fix is often smaller than a rebuild. One petrochemical reporting pipeline was fixed in place in three months.
Batch, streaming, and API ingestion should meet one reliability standard, not three.
Data pipeline and data warehousing services design, build, and operate the systems that collect data from business applications, transform it, and deliver it to a warehouse, data lake, or data mart. These systems make data available for reporting, analytics, AI, and operational applications.
A data pipeline moves and transforms data between systems. A data warehouse stores structured, curated data for analysis. A data lake holds raw and semi-structured data at scale, while a data mart provides a business-ready dataset for a specific team or purpose.
The first load is rarely the hard part. The cost shows up later, in four places. None of this is exotic. It is why the answer is usually better engineering around the platform you have, not a bigger platform.
When connecting a source means writing new code, the data team becomes the bottleneck and business requests queue up behind it.
In a mature environment, a change to one job can affect analytical applications, historical models, and operational workflows. On the media platform above, keeping 300+ processes reliable meant controlling every change, not just building new flows.
Business teams need data as it stood earlier. A flow that doesn’t preserve history breaks historical analysis for that source, often without anyone noticing until a report can’t be reproduced.
Build teams often hand off at go-live, and the failed load becomes the business’s problem. On the retail engagement, the engineers building new flows also answered for the loads already running, including overnight and weekend on-call. Engineers who are on call for a load build the next flow differently.
Pipelines that ingest data from relational databases, event streams, and REST APIs under consistent reliability and data-quality requirements. We design ingestion around source behavior, expected volumes, dependencies, and the freshness the business actually needs.
Business outcome: new data sources become available without creating a separate engineering project for every request.
Curated datasets and storage structures that give reporting teams and analytical applications a consistent basis for working with business data. We build and extend warehouses, lakes and marts around the existing data estate, workload requirements and intended use.
Business outcome: teams spend less time reconciling extracts and more time using consistent, documented datasets.
Scheduling, dependency management, monitoring, alerts and operational runbooks that make data flows easier to observe and maintain. We help identify failed or delayed jobs, trace downstream dependencies, and resolve issues within the agreed support model.
Business outcome: data teams have clearer operational visibility and a defined way to respond when a load fails.
Pipelines that keep reusable load rules, source settings, and schema definitions in configuration rather than embedding every variation in custom code. Where the existing architecture supports it, this makes onboarding new tables and sources easier to maintain.
Business outcome: less repetitive development when data sources or ingestion requirements change.
Data flows that preserve relevant changes and historical states, allowing teams to reproduce datasets as they existed at a selected point in time. Depending on the use case, this can include snapshots, change records, timestamps, and documented transformation logic.
Business outcome: teams can investigate historical discrepancies and reproduce past reporting states more reliably.
Data pipelines and storage deployed in public cloud, private EU cloud, hybrid or client-controlled on-premises environments. We work with the infrastructure and access requirements of the existing estate, taking workload, data location, security and operating cost into account.
Where AI models or agents will consume the data, we can design the data layer to support those workloads alongside reporting and operational systems.
Business outcome: data is available where it is needed, within the infrastructure and access constraints of the organization.
A new platform is not always the answer. Azati can extend the platform you already have, build a new data flow or warehouse component, or help assess whether a broader modernization is justified.
Data pipeline development or targeted warehouse work is worth considering when:
Whether you need two engineers inside your team, a defined workstream, or an honest view of what’s wrong before you decide, start with a conversation. Describe the pipeline or warehouse that worries you most. We respond within one business day, with an NDA on request before the first call.
The right engagement model depends on whether you need additional engineering capacity, a defined delivery outcome, or ongoing support for existing data flows.
Add data engineers to your team to develop new pipelines, connect sources, improve existing flows, or reduce a delivery backlog. Azati engineers work within your repositories, standards, and sprint or Kanban process.
Suitable when: your architecture and platform are established, but your internal team needs additional capacity.
Assign Azati a specific scope, such as a group of source integrations, a new data mart, an ingestion layer, or a warehouse component. Scope, deliverables, and acceptance criteria are agreed before delivery.
Suitable when: you have a defined requirement and want an external team to deliver it without expanding your internal team.
Keep engineering support in place after release to monitor agreed data flows, investigate failures, and maintain the pipelines as sources and requirements change. Coverage, responsibilities, and response expectations are agreed for each engagement.
Suitable when: existing pipelines are business-critical, and your team needs additional operational capacity.
If the main issue is not yet clear, begin with a technical review of the affected pipelines, sources, dependencies, and failure patterns. If the initiative is driven by an AI use case, Azati’s fixed-scope 3-week AI data readiness assessment may be the more appropriate starting point.
Suitable when: you need to understand the problem and prioritize the next engineering steps before committing to a larger scope.
The smallest useful scope comes first. We map what exists, agree what has to change, and only then build.
We map the relevant sources, data flows, dependencies, load schedules, and failure history. We confirm the required data freshness, volumes, and downstream use.
We identify whether the need is a new flow, an extension to the existing platform, a performance fix, or a larger workstream. Architecture and platform decisions remain with your team unless you ask Azati to take them on.
New or changed flows are tested against agreed requirements, including data correctness, processing time, resource use, and expected load. We validate downstream dependencies before release.
We deploy through the client’s agreed release process and document the operational requirements. Production monitoring and incident support can continue under an agreed engagement model.
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 large retail group's internal ETL team supported the data platform behind analytics and business-critical processes. As internal and external business teams requested more data sources and deliveries, the team needed to expand the platform without disrupting existing production loads. New integrations also had to support historical data analysis, meet performance requirements under large daily volumes, and remain maintainable within the existing architecture.
Azati embedded ETL engineers into the client's internal team to develop and support new data flows across three loader architectures: relational databases, streaming, and REST APIs. Working with the platform's existing modular loaders and PostgreSQL metadata configuration layer, the engineers implemented new integrations, applied historical data versioning, performed load testing, and provided ongoing production support.
The ETL platform operated entirely within the client's internal infrastructure. Access followed a role-based model separating developer, service-process, and support permissions. Azati's engineers worked under the client's existing access controls and operational policies.
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.
A petrochemical manufacturer’s Equipment Downtime and Utilization Dashboard became slower as operational transaction volumes grew. The SAP-to-PostgreSQL data pipeline recalculated the entire reporting dataset on every refresh, including static historical maintenance records. The resulting compute overhead affected dashboard responsiveness during peak shift changes, even though the underlying data platform remained fit for purpose.
Azati optimized the existing Apache Airflow pipeline by introducing selective recomputation in PostgreSQL. The solution separated stable historical maintenance data from changing operational parameters, so only modified records required recalculation. This improved dashboard response times without rebuilding the data warehouse, changing the target schema, or disrupting daily reporting.
It might be a failed overnight load, a source that takes too long to integrate, a reporting pipeline that cannot keep up, or a data flow that only one person knows how to maintain. Tell us what the pipeline needs to do, where it runs, and what is going wrong. Azati’s data engineers can help assess the issue and identify a practical next step before recommending a larger change. We respond within one business day. An NDA can be arranged before the first call.
Your team is the bottleneck for new sources, and you need extra engineers inside your own sprints.
You need reports and models to rest on loads that arrive on time and can be reproduced.
You are held back by slow or inconsistent refreshes.
Engineers work within your data team, repositories, and platform standards rather than building a parallel system that requires a separate handover.
Testing, monitoring, and operational support are considered during development, not added only after a flow goes live.
We assess the existing platform before proposing a rebuild or migration. If a targeted fix is sufficient, we do not turn it into a platform replacement.
Architecture, access, release processes, and support expectations are agreed with your team before work begins.
Listed technologies come from delivered projects. We choose the platform that fits your estate, and cloud warehouse targets are assessed case by case.
| Area | Technologies used in delivered projects |
|---|---|
| Warehouses and databases | IBM Db2 Warehouse, Netezza, Greenplum, ClickHouse, PostgreSQL, Oracle, Microsoft SQL Server, Hadoop (HDFS, Hive) |
| Ingestion and orchestration | Apache NiFi, Apache Airflow, Apache Kafka, Apache Sqoop, Prefect, dbt, Microsoft SSIS, Apache Spark |
| Languages | SQL, PL/SQL, Python |
| Cloud and runtime | AWS (S3, EC2, RDS), IBM Object Storage, Kubernetes, Docker |
| Quality and change control | Great Expectations, Flyway |
Data pipelines can run inside client-controlled environments and follow the client’s access policies. Access is assigned according to agreed roles and responsibilities. On one retail engagement, developer, service-process, and support roles were separated, with view-only access for support specialists.
For regulated environments, Azati can help document technical controls, data lineage, access arrangements and processing records within the agreed project scope. This can support internal reviews and third-party risk assessments related to requirements such as GDPR and DORA. The client’s own governance, policies and compliance responsibilities remain part of the overall control environment.
Data pipeline development services design, build, and run the automated jobs that move data from source systems into a warehouse, data lake, or data mart, transforming it along the way. A good service also covers orchestration, monitoring, history handling, and production support, because pipelines fail in operation, not in design. Azati builds batch, streaming, and API-based pipelines and stays through production support.
Data pipeline development focuses on collecting, moving, and transforming data between systems. Data warehouse development focuses on organizing and storing curated data for reporting and analysis. The two are closely related: pipelines deliver and prepare the data that warehouses make available to business users and analytical applications. Azati works across both areas.
Yes. Azati can extend existing data platforms and build new flows using the client's current architecture, repositories, and engineering standards. The work may include new source integrations, batch or streaming ingestion, orchestration, data validation, and production support. A platform migration is considered separately when the existing environment cannot meet the required scale, latency, cost or security needs.
Azati can provide monitoring, incident investigation, and production support as part of an agreed project or ongoing engagement. Support coverage, responsibilities, escalation paths, and response expectations are defined with the client. The scope should be agreed according to the criticality of the data flows and the client's operating model.
Use streaming when a decision loses value as data ages, such as continuously arriving operational or event data. Use batch when a daily or hourly refresh is enough, which is cheaper and simpler to run. Many enterprises need both. On a retail group's ETL platform, Azati built relational, streaming and REST API flows to one reliability standard rather than treating streaming as a separate stack.
Yes. Azati has delivered batch, streaming, and REST API ingestion flows. The choice depends on how quickly the data must be available, the source-system behavior, workload, and operational cost. Many enterprise environments need a combination of batch and streaming rather than a single ingestion pattern.
Often, if the platform is built for it. When load parameters, source settings, and schemas live in a configuration layer, connecting a new table becomes a configuration change rather than a rewrite. On a retail group's ETL platform, Azati's engineers connected new tables by changing configuration in a PostgreSQL metadata layer. Whether that works for your estate depends on the existing loader design.
Reliability comes from testing before release and ownership after it. Azati load-tests new flows under the client's own procedure, covering modeled load, resource usage, and processing time for large volumes, before they reach production. We then monitor loads and resolve incidents in first- and second-line support. Engineers who are accountable for running a flow tend to build the next one differently.
Not necessarily. Moving platforms is a separate decision from building reliable pipelines. Azati's delivered projects run on enterprise warehouses such as IBM Db2 Warehouse and Greenplum, on ClickHouse and PostgreSQL, and on AWS. If a migration is justified by scale, cost, or end-of-life, it is scoped as a modernization engagement after an assessment, with the target platform assessed case by case.
Not necessarily. New pipelines can often be developed on the existing warehouse or data platform. Migration should be considered when the current platform cannot meet requirements for scale, performance, cost, security, or lifecycle support. Azati assesses the existing environment before recommending that decision.
Depending on the use case, Azati can implement snapshots, change records, and point-in-time data handling so that historical states can be reproduced. The approach depends on source behavior, retention requirements, and the level of historical detail needed by downstream reporting or operational systems.
An initial estimate usually requires the source systems and data formats, approximate data volumes, ingestion frequency, target platform, transformation requirements, downstream dependencies, and support expectations. If these are not known, an assessment can help establish the scope before a delivery estimate is prepared.
Yes. Platforms can be deployed in a public cloud, a private EU cloud, or on your own infrastructure, depending on data-residency and security requirements. Azati's pipelines have run inside client-controlled environments under the client's access policies, and one research platform is designed to run the same pipeline in AWS or inside a client's data center. Fit is confirmed during assessment.
A single new flow on an existing platform can be scoped as a defined request and released independently. A new warehouse or platform is scoped after an assessment, because source count, data quality, and integration needs vary widely. Azati's AI data readiness assessment runs three weeks at a fixed scope, and its roadmap sets the timeline for what follows.
Cost depends on the number and type of sources, the state of the existing platform, how much must be rebuilt rather than extended, and the support level you need. We scope after a short assessment. Embedded engineers can join your team on a time-and-materials basis, as on our retail ETL engagement, while larger builds are scoped as defined workstreams.
Yes. Azati's data engineers work embedded inside client-led teams, using your repositories, standards, and sprint or Kanban process. In one retail group's ETL team of 15 to 30 people, Azati engineers joined for eight months to build flows, test under load, and provide production support. At a chemical manufacturer, the embedded delivery ran for more than 24 months.
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