Real transformations, measurable results — see how we've helped enterprises unlock the power of their data
Replacing a fragile ETL system with a Debezium + Kafka streaming pipeline
FinTrack's risk and compliance dashboards ran on overnight batch jobs pulling from five disparate PostgreSQL databases. By morning, the data was already 8–12 hours stale. During volatile market periods, this created regulatory exposure and delayed trader decisions. A single broken job could silently corrupt downstream reports.
DataFortis designed and implemented a Change Data Capture pipeline using Debezium connectors on all five Postgres instances, publishing events into Confluent Kafka. A Kafka Streams application handled deduplication, schema evolution, and enrichment before landing clean records into Snowflake every few seconds. We also built a dbt layer on top for the compliance reporting models.
Compliance dashboards now reflect data within 500ms of source transactions. The risk team eliminated their manual morning reconciliation process — saving 3 hours of analyst time per day. Zero production incidents in the 6 months post-launch.
A full Azure infrastructure audit, right-sizing, and FinOps governance programme
RetailNxt's Azure bill had grown 340% over two years to $1.35M annually — far outpacing their revenue growth. The engineering team had no visibility into which workloads were driving costs. Unused Azure Synapse pools ran 24/7, over-provisioned ADF integration runtimes idled at peak capacity, and 60+ orphaned storage accounts silently accumulated charges.
We conducted a full 360° Azure Cost Management audit, tagging every resource by team and workload. We replaced always-on Synapse dedicated pools with serverless SQL pools for ad-hoc queries and migrated heavy batch workloads to Databricks spot clusters. We implemented Azure Advisor automation, budget alerts, and a monthly FinOps governance review cadence with the CTO and engineering leads.
Monthly Azure spend dropped from $112K to $43K within 90 days. The FinOps programme DataFortis established is now run internally by a dedicated cloud engineer. Query performance on Databricks actually improved 40% compared to the old Synapse pools.
Replacing 4,000 lines of undocumented SQL with tested, modular dbt models
GrowthPulse's analytics layer was held together by a sprawling set of stored procedures, scheduled Python scripts, and a massive single-file SQL "monolith" that no single engineer fully understood. Data incidents — wrong numbers in the investor dashboard, broken churn metrics — consumed 30% of the data team's time each week. New hires couldn't onboard without a month of shadow work.
DataFortis performed a full audit of every transformation and mapped the dependency graph. We rebuilt the entire analytics layer in dbt — 87 models across staging, intermediate, and mart layers — with dbt tests on every critical business metric. We integrated dbt Cloud with their CI/CD pipeline so every PR gets schema validation and test runs before merging. Documentation auto-generates from model YAML.
New data engineers are productive within their first week. The CEO's dashboard has shown zero incorrect figures since launch. The data team now ships new metrics in hours instead of days, and the CTO describes it as "the first time I've trusted our numbers in two years."
Book a 30-minute strategy call and let's discuss how DataFortis can deliver measurable results for your data infrastructure.