From broken nightly jobs to a trusted, well-modelled data platform — we turn fragile pipelines into a dependable foundation analytics and AI can build on.
No single source of truth — Sales, finance, and operations report different numbers from the same source — each team transforms data its own way with no shared logic.
Pipelines silently break overnight — Reports go red, executives ping the team, and engineers spend mornings firefighting ETL failures with no monitoring or alerting.
Stale data, missed SLAs — Nightly batches miss windows, downstream marts don't refresh, and business users open dashboards that are 24+ hours behind reality.
No lineage, no audit trail — Auditors and risk teams ask where a number came from — and nobody can trace it from report back to source through every transformation.
Legacy on-prem ETL bottleneck — SSIS packages and stored procedures from a decade ago resist change — every modification is risky and the skill pool is shrinking.
Spaghetti Data Factory pipelines — Copy-paste linked services, hard-coded paths, and no metadata-driven design make every change a multi-hour regression test.
No CI/CD, no tests — Pipeline changes go straight from dev to prod via the portal — there's no source control, no peer review, no automated tests, no rollback story.
Runaway Spark / Databricks cost — Clusters stay on too long, jobs aren't tuned, and Photon/cache strategies are missing — DBUs keep climbing without business reason.
Inconsistent SCD & history — Some dimensions track Type 2, others Type 1, some none — analysts can't trust point-in-time queries or trend analysis.
Brittle streaming ingestion — Event Hubs, IoT Hub, and Kafka feeds drop messages or fall behind because there's no checkpointing, schema enforcement, or back-pressure handling.
Tell us a little about your situation — we'll suggest the right Microsoft solution for you.
Real data platforms delivered across multiple industries.
In most organizations, there is a quiet tax on every business decision: the time it takes to get an answer from data. People who need information often cannot get it themselves. Those who can are stretched thin. The result is delay, guesswork, and missed opportunity. We built a conversational data assistant to change that. The goal was simple: let any employee, regardless of their background or technical skill, ask a business question in plain English and receive an accurate, clear answer immediately. No specialist required. No waiting. No back-and-forth. The assistant understands the intent behind a question, retrieves organization's data, and returns a response that is easy to read and act on. It can also suggest next steps, highlight trends, and explore hypothetical scenarios when needed.
Our customer basically needs to extract health care data from their databases into flat files. The data is very bulky. The customer is well versed in SQL and wishes to utilize this fact and develop an SSIS package that eliminates the need of SSIS knowledge for their employees, leverage their knowledge of SQL and make extracts possible by just writing stored procedure(s).
The dataset provided contains extensive information on agricultural crop production across various states and districts in India, spanning multiple years. The dataset includes details on the state, district, crop, year, season, area, production, and yield. However, the raw data, as presented, poses several challenges for stakeholders looking to gain actionable insights: <strong>Data Complexity</strong>: The dataset contains mixed data types and large volumes of information, making it difficult for users to extract meaningful insights without extensive data processing and analysis. <strong>Reporting Limitations</strong>: Without a structured reporting mechanism, it is challenging to analyze trends, compare performance across different regions and crops, and make data-driven decisions. <strong>Granular Insights</strong>: Stakeholders require granular insights into crop production at the State level, seasonal analysis, and year-over-year comparisons to optimize agricultural practices and policies.
We combine modern lakehouse engineering with classical warehouse discipline — designed for reliability, observability, and cost from day one.
Our data engineering practice covers the full lifecycle — source profiling, target architecture, pipeline build, testing, observability, and run-state operations. We design platforms that are reliable at 3am, cheap at scale, and trusted by the business — with metadata-driven design and source-controlled ALM as defaults.
From a single new pipeline to a full lakehouse rebuild, we deliver engineering rigour, not just glue code.
From legacy ETL to streaming lakehouse — we cover every layer of the data stack.
Target-state architecture across Azure Data Factory, Synapse, Databricks, Fabric, and Storage — sized for cost, performance, and team capability.
Metadata-driven ADF pipelines, Synapse Spark / SQL pools, and Databricks notebooks — replacing brittle SSIS and stored-procedure ETL with reliable, testable code.
Inventory, assess, and migrate SSIS, Informatica, or DataStage workloads onto modern Azure stacks — with parallel-run validation so the business never loses a number.
Event Hubs, IoT Hub, Kafka, and Stream Analytics pipelines — with schema registries, checkpointing, and exactly-once semantics for trusted real-time data.
Kimball-style stars, Data Vault, and conformed dimensions engineered for Power BI semantic models — slowly changing dimensions handled correctly everywhere.
CI/CD via Azure DevOps or GitHub, unit and integration tests on pipelines, lineage with Purview, and alerting on freshness, volume, and quality SLAs.