Big Data & Analytics
Big Data Solutions That Unlock Business Intelligence
We engineer scalable data platforms, real-time analytics pipelines, and AI-powered business intelligence systems that transform petabytes of raw data into strategic decisions — fast, reliable, and production-grade.
50+
Data Platforms Built
10PB+
Data Processed Monthly
60%
Avg Query Speed Improvement
45%
Infrastructure Cost Reduction
What We Deliver
From raw data chaos to actionable intelligence
Whether you are dealing with terabytes of IoT sensor data, millions of customer events, or complex multi-source datasets — Sensussoft builds the infrastructure to ingest, process, store, and visualise it all. Our big data solutions are built for scale, speed, and reliability, integrating seamlessly with your existing tech stack.
- Data Warehouse & Data Lake Architecture
- Real-Time Streaming & Event Processing
- ETL/ELT Pipeline Engineering
- Business Intelligence & Dashboards
- Predictive & Advanced Analytics
- Data Governance & Quality Management
- Cloud Data Platform Migration
- Data Mesh & Data Fabric Architecture
- Self-Service Analytics Platforms
- Big Data Security & Compliance
Data Warehouse & Lake Architecture
Design and build modern data warehouses (Snowflake, BigQuery, Redshift) and data lakes (Delta Lake, Iceberg) optimised for cost and query performance.
Real-Time Data Pipelines
Apache Kafka, Flink, and Spark Streaming pipelines that process millions of events per second with exactly-once guarantees and sub-second latency.
Business Intelligence & Visualisation
Interactive dashboards and self-service analytics using Metabase, Superset, Tableau, or Power BI — connected to your unified data layer.
Full Capabilities
Everything you need to succeed
Data Warehouse & Lake Architecture
Design and build modern data warehouses (Snowflake, BigQuery, Redshift) and data lakes (Delta Lake, Iceberg) optimised for cost and query performance.
Real-Time Data Pipelines
Apache Kafka, Flink, and Spark Streaming pipelines that process millions of events per second with exactly-once guarantees and sub-second latency.
Business Intelligence & Visualisation
Interactive dashboards and self-service analytics using Metabase, Superset, Tableau, or Power BI — connected to your unified data layer.
ETL/ELT Pipeline Engineering
Robust data transformation pipelines using dbt, Airflow, Dagster, or Prefect — orchestrated, tested, and monitored in production.
Advanced & Predictive Analytics
Statistical modeling, cohort analysis, customer segmentation, demand forecasting, and anomaly detection on your structured and unstructured data.
Data Governance & Quality
Data catalogs, lineage tracking, access controls, PII masking, and automated quality checks ensuring trust and compliance across your data estate.
Cloud Data Platform Migration
Migrate from on-premise Hadoop, legacy databases, or siloed systems to modern cloud-native data platforms on AWS, GCP, or Azure.
Data Mesh & Federation
Decentralised data ownership with domain-oriented data products, federated governance, and self-serve data infrastructure for large organisations.
IoT & Sensor Data Processing
Time-series ingestion, edge computing integration, and real-time monitoring dashboards for industrial IoT, smart cities, and connected devices.
Our Process
How we build with you
01
Data Audit & Strategy
Map your data sources, assess quality and gaps, define KPIs, and design a target architecture aligned with your business goals.
02
Platform Engineering
Build the data infrastructure — storage, compute, pipelines, and governance layers — on your chosen cloud with Infrastructure as Code.
03
Pipeline Development & Integration
Develop ETL/ELT workflows, connect source systems, implement transformations, and deploy tested, monitored data pipelines.
04
Analytics & Continuous Optimisation
Build dashboards, train predictive models, optimise query performance, and establish ongoing monitoring and cost governance.
Technology Stack
Built with proven technologies
Apache SparkApache KafkaSnowflakeBigQueryRedshiftdbtApache AirflowApache FlinkDelta LakeDatabricksHadoop / HDFSElasticsearch
FAQ
Common questions
A data warehouse stores structured, processed data optimised for fast analytical queries (e.g., Snowflake, BigQuery). A data lake stores raw data in any format (structured, semi-structured, unstructured) at low cost (e.g., S3, Delta Lake). Modern architectures often use a "lakehouse" combining both — we help you pick the right approach for your use case.
A foundational data platform with ingestion, storage, transformation, and basic dashboards typically takes 8-12 weeks. More complex setups with real-time streaming, ML pipelines, and data mesh governance may take 4-6 months. We deliver in agile sprints with value delivered every 2 weeks.
Yes. We have migrated multiple Hadoop environments to Databricks, Snowflake, and cloud-native architectures on AWS/GCP/Azure. We handle data migration, pipeline refactoring, and performance testing with zero data loss.
Absolutely. We build real-time streaming pipelines using Apache Kafka, Flink, and Spark Structured Streaming that process millions of events per second. These feed into real-time dashboards, alerting systems, and ML models.
We implement automated data quality checks (Great Expectations, dbt tests), data catalogs (DataHub, Amundsen), column-level lineage tracking, PII detection and masking, and role-based access controls. Every pipeline includes monitoring and alerting for data freshness and quality.
Ready to get started?
Let's discuss your project and see how we can help you build something extraordinary.