Data Engineering Services That Turn Raw Data Into Revenue
Pipelines that never sleep. Insights that never wait.
We design and build modern data platforms — from real-time streaming pipelines and data lakes to analytics warehouses and ML feature stores — that give your teams fast, reliable access to the data they need. Whether you're migrating from legacy ETL, building a lakehouse from scratch, or scaling a petabyte-scale warehouse, our engineers bring production experience with Spark, Kafka, dbt, and every major cloud data service to deliver infrastructure that is observable, cost-efficient, and built for the long term.
Why teams choose us
Sub-Second Query Performance
We optimise every layer — partitioning, clustering, caching, and materialisation — so analysts get answers in seconds, not minutes. Our lakehouse architectures eliminate redundant data copies and reduce query costs by 40–70%.
Batch and Stream Unified
Lambda and Kappa architectures let you process historical and real-time data through the same logic, reducing code duplication and ensuring consistency between dashboards and live alerts.
Cloud Cost Control
Data infrastructure is one of the fastest-growing line items in cloud budgets. We implement auto-scaling, spot instance strategies, and query cost monitoring so your spend grows predictably with usage, not linearly with data volume.
How we work
A clear, repeatable process — no surprises.
Data Landscape Audit
We map your data sources, existing pipelines, storage formats, consumption patterns, and pain points. We identify schema drift, data quality issues, and bottlenecks that slow your teams down.
Architecture Design
We design the target architecture — choosing between batch, streaming, or hybrid processing; selecting the right storage layer (lake, warehouse, or lakehouse); and defining governance, lineage, and access control policies.
Incremental Migration
We build new pipelines alongside existing ones, validate data parity, and cut over source by source. No big-bang migrations. Every pipeline has observability from day one.
Optimise & Operationalise
We tune partition strategies, implement incremental models with dbt, set up alerting for data freshness and quality SLAs, and document the platform so your team can extend it confidently.
Tech stack
What we build
Common use cases and project types.
- Legacy ETL migration to modern lakehouse
- Real-time event processing and alerting
- Centralised analytics warehouse for BI teams
- ML feature store and training data pipelines
- Data quality monitoring and governance
- Multi-cloud data platform consolidation
Data Processing Architecture Comparison
| Aspect | Batch Processing | Stream Processing | Lambda Architecture | Kappa Architecture |
|---|---|---|---|---|
| Latency | Minutes to hours — suitable for daily/hourly reporting cycles | Milliseconds to seconds — essential for real-time alerting and live dashboards | Mixed — real-time layer for speed, batch layer for accuracy correction | Single-digit seconds — stream-only with replay capability for reprocessing |
| Complexity | Low — simple DAG scheduling with Airflow or Dagster, easy to debug and test | Medium — requires windowing logic, watermark handling, and state management | High — dual code paths for batch and stream that must produce identical results | Medium — single stream path but requires robust replay and schema evolution |
| Cost | Lowest — runs on scheduled compute, can use spot instances and aggressive auto-shutdown | Higher — always-on clusters with persistent connections and state stores | Highest — maintains both batch and streaming infrastructure simultaneously | Moderate — single processing layer but always-on streaming infrastructure |
| Data Freshness | Stale between runs — dashboards update only after batch jobs complete | Near real-time — data available within seconds of event generation | Near real-time with eventual consistency as batch layer corrects stream results | Near real-time with full accuracy through stream processing and replay |
| Fault Tolerance | High — failed jobs can be re-run from checkpoints, no data loss risk | Medium — requires careful checkpointing and exactly-once semantics | Very high — batch layer serves as source of truth, stream is reconstructed on failure | High — replay capability from event log provides complete fault recovery |
| Use Case | BI reporting, data warehousing, model training, regulatory reporting | Fraud detection, live monitoring, real-time personalisation, IoT telemetry | Systems requiring both low-latency and complete accuracy (e.g., ad tech, fintech) | Event-driven applications where stream is primary and batch replay is infrequent |
| Tools | Apache Spark, dbt, Airflow, Snowflake, BigQuery, Databricks | Apache Kafka, Apache Flink, Spark Streaming, Amazon Kinesis, Confluent | Combined Spark + Kafka + Flink with separate batch and speed layers | Kafka + Flink or Kafka Streams with compacted topics for replay |
| Scalability | Excellent — horizontal scaling with ephemeral compute, pay per job | Good — scales with partition count and consumer groups, requires capacity planning | Complex — must scale two independent systems that process the same data | Good — scales with Kafka partitions and Flink parallelism, simpler ops model |
Scaling a FinTech Data Platform From 2TB to 10TB Daily
A Series B fintech company processing payment transaction data was hitting walls with their legacy PostgreSQL-based analytics stack. Queries against their growing dataset were taking 5+ minutes, daily ETL jobs were missing SLA windows, and cloud costs were scaling linearly with data volume. We migrated them to a Databricks lakehouse architecture with Delta Lake storage, implemented incremental dbt models for batch processing, and added a Kafka + Flink streaming layer for real-time fraud detection. The result: 10TB of daily throughput, sub-5-second analytical queries, and a 40% infrastructure cost reduction through spot instance orchestration and intelligent data tiering.
Frequently asked questions
Should we choose a data lake, data warehouse, or lakehouse?
It depends on your workloads. A data warehouse (Snowflake, BigQuery) excels at structured SQL analytics with strong governance. A data lake (S3 + Iceberg/Delta) is better for unstructured data and ML workloads. A lakehouse combines both — structured SQL performance with lake-level flexibility and cost. For most organisations starting fresh, we recommend a lakehouse architecture because it avoids the data duplication and consistency problems of maintaining separate lake and warehouse layers.
How do you handle data quality in production pipelines?
We implement data quality as code using tools like Great Expectations, dbt tests, and Soda. Every pipeline has freshness SLAs, schema contracts, and anomaly detection on key metrics. When quality checks fail, we route alerts to the right team with context about what changed, which upstream source caused it, and which downstream dashboards are affected. This prevents silent data corruption from reaching business decisions.
What's the difference between batch and stream processing?
Batch processing runs on a schedule (hourly, daily) over bounded datasets — it's simpler, cheaper, and sufficient for most reporting and analytics use cases. Stream processing handles unbounded data in real time as events arrive — it's essential for fraud detection, live dashboards, and operational alerting where latency matters. Most organisations need both, which is why we design Lambda or Kappa architectures that share business logic between batch and stream layers.
How long does a typical data platform build take?
A focused analytics warehouse with 10–20 pipelines typically takes 6–10 weeks. A full lakehouse platform with streaming, governance, and self-service tooling is usually 3–6 months. We phase delivery so your team gets value incrementally — the first production pipeline is usually live within 2–3 weeks.
Can you reduce our current Snowflake or BigQuery costs?
Almost certainly. The most common issues we find are: queries scanning entire tables instead of using partitioning/clustering, materialised views that refresh too frequently, warehouse sizes set too large for the workload, and lack of query cost attribution. A typical optimisation engagement reduces compute costs by 30–50% within the first month.
Ready to start?
Tell us about your project and we'll send a detailed estimate within 24 hours.