Data Engineering Consultancy

We turn
unreliable data
into trusted decisions.

Bluespark Solutions designs and delivers production-grade data platforms on Microsoft Fabric, Azure, and Databricks. From raw ingestion to governed analytics, every pipeline we build is automated, auditable, and built to last.

300M+
records processed
2TB+
data engineered
42
governed artifacts
3
cloud platforms
Microsoft Fabric Azure Data Factory Databricks Apache Spark PySpark Delta Lake Power BI Azure Data Lake Gen2 Medallion Architecture Spark SQL Azure Logic Apps Python Git / GitHub OneLake
About

Data platforms
that people
actually trust.

Bluespark Solutions is a data engineering consultancy founded by Sriram Murali, specialising in modern cloud data platforms. We design and build the infrastructure that turns raw, messy data into governed, analytics-ready systems.

Our work spans the full data lifecycle: ingestion architecture, transformation pipelines, data quality engineering, semantic modelling, and executive-facing dashboards. We work on Microsoft Fabric, Azure, and Databricks, applying industry-standard patterns like Medallion Architecture and Delta Lake wherever reliability and scale are non-negotiable.

We build with the rigour of a production engineering team. Every pipeline is version-controlled, every data quality failure is traced, and every platform is documented for the engineers who maintain it after we leave.

🏗️
Lakehouse Architecture
Medallion Bronze / Silver / Gold on OneLake and ADLS Gen2
Core
Distributed Processing
PySpark and Spark SQL for batch and streaming at scale
Scale
🛡️
Data Quality Engineering
Dead Letter Queue, validation layers, full lineage tracking
Core
🔄
Pipeline Orchestration
Scheduled, validated, zero-intervention Data Factory pipelines
Ops
📊
Semantic Modelling
Star schema, governed Power BI models, executive dashboards
BI
🔔
Real-Time Alerting
IoT integration and anomaly detection via Azure Logic Apps
IoT
Case Studies

Work that shipped
to production.

These are not prototypes or demos. Each project below was designed, built, version-controlled, and documented to enterprise standards. The kind of platform you hand off to a team and it keeps running.

Databricks · Regulated Finance
Databricks Regulated Data Enterprise Scale

Customer Risk and Loss ETL Platform

"We can't trust our loss reports. Manual reconciliation happens every cycle before anyone will sign them off."

Built for a client in a regulated environment, this ETL platform processes millions of financial loss records daily using Databricks and Spark SQL. The Silver Validated layer enforces strict quality gates: every record carries a validation status, rejection code, confidence score, and hash-based row ID for complete traceability. An average of 5 to 8 percent of daily records are flagged and reported, giving business and compliance teams full visibility into upstream data quality rather than discovering problems at reporting time.

300M+
historical records
2TB+
data across layers
1 to 3M
daily records
Azure · IoT · Facilities
Azure Data Factory IoT Integration Near Real-Time

HVAC Climate Intelligence Pipeline

"We have sensors everywhere but our indoor and outdoor datasets live in different systems. We can't see the full picture."

An Azure-native platform that unifies IoT sensor data from 120 to 180 building sensors with live external weather feeds, two streams that were previously analysed in isolation. The Gold layer joins them on aligned timestamps, computing temperature and humidity deltas for operational reporting. Azure Logic Apps delivers near real-time anomaly alerts to facilities teams when HVAC conditions breach defined thresholds, enabling intervention before occupant comfort is affected.

450K
daily records max
10 to 15m
end-to-end latency
18 mo
data retention
In the Lab

More from our
engineering work.

Real-Time Streaming Orders Pipeline

A production-style streaming pipeline using Apache Spark Structured Streaming and Delta Lake on Databricks. Processes continuously arriving order events through Bronze, Silver, and Gold layers. Invalid records are routed to a quarantine table. The Gold layer produces RFM segmentation, revenue aggregations, and return rate KPIs ready for BI consumption.

Databricks Structured Streaming Delta Lake PySpark

Data Warehouse with Dimensional Modelling

🏗️

A batch data warehouse built with Spark SQL and Delta Lake, featuring SCD Type 2 customer tracking, star schema design, and full audit fields on every layer. Every SQL script is atomic and modular, written for Airflow and dbt compatibility. The validated Gold layer serves KPIs for monthly revenue, top customers, and brand performance.

Spark SQL SCD Type 2 Delta Lake Star Schema
Contact

Ready to build a platform
your team can rely on?

Whether you are starting from scratch or untangling an existing pipeline, Bluespark Solutions brings the engineering depth to get it right.

sriram@bluespark-solutions.com LinkedIn GitHub