Service
Data Warehousing & Engineering
We build the modern data pipelines and warehouses that consolidate scattered data into one reliable, analytics-ready source of truth.
What we build.
Six disciplines, from first architecture to production support.
ETL/ELT pipeline design
Reliable pipelines that move, transform, and consolidate data from every source into an analytics-ready foundation.
- Batch and streaming ingestion
- Source-to-target mapping
- Transformation and enrichment
- Orchestration and scheduling
Data lake and lakehouse architecture
Modern lake and lakehouse platforms that unify structured and unstructured data at scale.
- Lakehouse design (Delta, Fabric)
- Structured and unstructured storage
- Bronze, silver, and gold layering
- Cost-efficient scaling
Azure Data Factory pipelines
Cloud-native data integration built on Azure Data Factory and Synapse pipelines.
- Pipeline design and parameterization
- Linked services and connectors
- Incremental and CDC loads
- Monitoring and alerting
Data modeling and optimization
Warehouse models and tuning that keep queries fast and storage efficient as data grows.
- Dimensional and star-schema modeling
- Partitioning and indexing
- Query performance tuning
- Storage and cost optimization
Data quality and governance
Frameworks that keep data accurate, trusted, and compliant across the organization.
- Validation and cleansing rules
- Data lineage and cataloging
- Access control and compliance
- Monitoring and alerting
Master data management
A single, reliable source of truth for the core entities your business depends on.
- Golden-record management
- Deduplication and matching
- Reference-data governance
- Cross-system synchronization
Why Aedista
Why Aedista for data engineering
Our stack
Modern tools we build with
.NET Core
Enterprise web and service backends
Java
Large-scale enterprise systems
Node.js
APIs and real-time services
PHP
High-volume web platforms
React
Modern web front ends
Angular
Enterprise front ends
Next.js
Server-rendered React apps
Swift
Native iOS apps
Kotlin
Native Android apps
Azure
Cloud infrastructure and DevOps
AWS
Cloud infrastructure at scale
Docker
Containerized deployment
Engagement models
Three ways to work with us
Fixed cost
A defined scope, priced upfront, and paid by milestone. Best when requirements are clear and stable.
Time and materials
Built for evolving scope. You pay for delivered work, and priorities can shift as you learn.
Dedicated team
Our engineers work as an extension of your team, on your priorities, at your cadence.
Aedista didn't just advise, they built the system alongside us. They understood our operations, made the complex parts simple, and delivered something our teams actually use every day. The result was immediate and, more importantly, built to last.
Trusted by global brands
Frequently asked questions
What data platforms does Aedista build on?
Snowflake, Azure Synapse, Microsoft Fabric, and Azure Data Factory are the core platforms, with Delta-based lakehouse architectures where they fit. We choose the platform based on your data volumes, team skills, and cost profile, not a default vendor.
What is the difference between a data warehouse and a lakehouse?
A warehouse stores structured, modeled data optimized for reporting. A lakehouse combines warehouse-style management with a data lake's flexibility, holding structured and unstructured data in layered bronze, silver, and gold tiers. We build both; the right answer depends on your sources and workloads.
How long does a data warehouse implementation take?
A focused warehouse with a handful of sources typically takes 3 to 4 months, including pipelines and models. Larger platforms with many sources, CDC loads, and governance frameworks run 6 to 9 months, delivered incrementally so reporting value lands early.
How do you keep data quality high?
Validation and cleansing rules are built into every pipeline, with data lineage, cataloging, and monitoring that alert on anomalies before they reach a dashboard. Master data management gives core entities a single golden record across systems.
Can you work with our existing pipelines?
Yes. We regularly take over, stabilize, and extend existing ETL and ELT estates, from optimizing slow queries and pruning storage costs to re-architecting pipelines for incremental and CDC loads.
How does this connect to BI and AI work?
The warehouse is the foundation both sit on. We design models so Power BI reporting is fast, and so AI and ML workloads have clean, governed features to draw from. One engineered foundation serving both.
