Data Engineering & Analytics
Turn data exhaust into decisions — reliably, in real time, at any scale.
Overview
Aadyora builds data platforms that earn trust — by being correct, timely, observable, and documented. We work across the modern data stack (Snowflake, BigQuery, Databricks, dbt, Airflow, Kafka, Iceberg) and open-source equivalents for cost-sensitive or sovereignty-bound environments.
Our engagements span warehouse migrations, lakehouse architectures, streaming pipelines, and the analytics and ML use cases that justify them. We bring data contracts, observability, and lineage from day one — not as an afterthought when the dashboard goes red.
Every project ends with documented models, tested transforms, and runbooks your team can extend without us.
What we deliver
Data Platform Architecture
Warehouse, lakehouse, or hybrid design with clear separation of raw, conformed, and consumption layers. Cost and performance modeled for your workload mix.
ELT & Streaming Pipelines
Batch and real-time ingestion using Airbyte, Fivetran, Kafka, Flink, and custom connectors. Idempotent, observable, and tested.
Analytics Engineering with dbt
Modular, version-controlled transformations with tests, documentation, and lineage. CI/CD for data so changes ship with the same rigor as application code.
BI & Self-Service Analytics
Semantic layers, governed metrics, and dashboards in Looker, Tableau, Power BI, or Superset — designed so business users can answer their own questions.
Predictive Analytics & ML on Data
Forecasting, segmentation, propensity, and uplift modeling deployed back into operational systems and dashboards, not stuck in notebooks.
Data Observability & Governance
Freshness, volume, schema, and quality monitoring; data contracts; access controls; PII tagging; and lineage to support compliance and incident response.
Outcomes you can expect
- Single source of truth for revenue, product, and operational metrics
- Pipeline incidents detected and resolved before downstream consumers notice
- New analyst onboarding measured in days because models are documented and tested
- ML features served from the same governed data assets as BI
How we engage
1. Audit & Strategy
Map current data sources, consumers, and pain points. Identify quick wins and platform investments. Cost-model the target architecture.
2. Foundation
Stand up warehouse/lakehouse, ingestion, orchestration, and CI/CD. Migrate the first high-value pipeline end-to-end as a reference.
3. Modeling
Build conformed dimensions and metrics with dbt. Implement tests, documentation, and lineage. Roll out the semantic layer.
4. Activation
Power dashboards, reverse-ETL into operational tools, and serve features to ML. Measure adoption and iterate.
Frequently asked questions
Which data warehouse should we choose?
It depends on workload, ecosystem, and budget. Snowflake for ease-of-use and elasticity; BigQuery for serverless and GCP integration; Databricks for lakehouse and ML-heavy workloads; Postgres or ClickHouse for cost-sensitive or smaller-scale needs. We model TCO for your usage before recommending.
Can you migrate us off legacy ETL tools?
Yes — Informatica, Talend, SSIS, and hand-rolled scripts migrated to dbt + Airflow / Airbyte / Fivetran. We migrate incrementally with dual-running and reconciliation so the cutover is non-event.
Do you build streaming pipelines too?
Yes. Kafka, Kinesis, Pub/Sub, Flink, and Spark Streaming for real-time use cases — fraud detection, operational dashboards, ML feature freshness, and event-driven workflows.
How do you ensure data quality?
dbt tests, Great Expectations, freshness and volume monitors, schema contracts at ingestion boundaries, and PagerDuty / Slack alerting tied to clear runbooks. Quality is treated as an SLO, not a hope.
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Related services
Case studies
Ready to start?
Tell us about your problem — we’ll respond within one business day with a concrete next step.
Contact Aadyora