Data platforms that scale
Lakehouse-native architectures built on Databricks, Snowflake, and Microsoft Fabric — engineered for petabyte-scale analytics and real-time decision making.
Modern data platforms, not legacy warehouses
Data engineering services encompass the design, implementation, and optimization of enterprise data architectures including lakehouse platforms, real-time streaming pipelines, data mesh topologies, and governance frameworks. Maharadha delivers production data platforms on Databricks, Snowflake, and Microsoft Fabric with 99.9% pipeline uptime, 60% cost reduction, and 10× query performance gains across petabyte-scale deployments.
We build lakehouse architectures that unify batch and streaming, structured and unstructured — eliminating the data swamp problem while enabling AI-ready data foundations.
Our data mesh implementations give domain teams ownership of their data products while maintaining enterprise governance. Real-time pipelines with sub-second latency power operational analytics and event-driven architectures.
Certified across Databricks, Snowflake, and Microsoft Fabric — we select the right platform for your workload profile and build migration paths that minimize disruption while maximizing performance gains.
End-to-end data engineering
From raw ingestion to governed data products — every layer of the modern data stack.
Lakehouse Architecture
Medallion architecture (Bronze/Silver/Gold) on Delta Lake, Apache Iceberg, or Hudi. Unified batch and streaming with ACID transactions at petabyte scale.
Real-Time Pipelines
Apache Kafka, Spark Structured Streaming, and Flink for sub-second data delivery. Event-driven architectures that power operational dashboards and ML feature stores.
Data Mesh
Domain-oriented data ownership with self-serve infrastructure. Federated governance, data product contracts, and discovery catalogs that scale with your organization.
Data Quality & Observability
Great Expectations, Monte Carlo, and custom validation frameworks. Data contracts, SLA monitoring, anomaly detection, and lineage tracking across your entire pipeline.
Migration & Modernization
Zero-downtime migration from legacy warehouses (Teradata, Oracle, Netezza) to modern lakehouse platforms. Automated schema mapping and validation testing.
Governance & Catalog
Unity Catalog, Purview, and Collibra implementations. Role-based access, column-level security, PII classification, and compliance-ready audit trails.
From assessment to production platform
A structured approach that delivers production data pipelines in 11 weeks on average.
Discover
Audit current data landscape, map source systems, assess quality, and identify quick wins. Output: data architecture blueprint and migration priority matrix.
Design
Define medallion layers, streaming topology, governance model, and platform selection. Design data contracts and SLAs with domain stakeholders.
Build
Iterative pipeline development with automated testing. Infrastructure as code, CI/CD for data, and quality gates at every layer of the medallion architecture.
Deploy
Blue-green deployment for zero downtime. Parallel run validation against legacy systems. Performance benchmarking and cost optimization before cutover.
Monitor
Data observability dashboards, SLA tracking, cost monitoring, and automated alerting. Continuous optimization of pipeline performance and cloud spend.
Data Engineering Impact
Frequently asked questions
Data engineering encompasses the design, build, and optimization of enterprise data architectures including lakehouse platforms, real-time streaming pipelines, data mesh topologies, data quality and observability, migration and modernization from legacy warehouses, and governance and catalog implementations. Maharadha delivers production data platforms on Databricks, Snowflake, and Microsoft Fabric with 99.9% pipeline uptime.
Lakehouse architecture combines the reliability and performance of data warehouses with the flexibility and scale of data lakes. Using medallion architecture (Bronze/Silver/Gold) on Delta Lake, Apache Iceberg, or Hudi, it supports both batch and streaming workloads with ACID transactions at petabyte scale — eliminating the need for separate lake and warehouse systems.
A data mesh is a domain-oriented data architecture where individual business domains own and publish their data as products with self-serve infrastructure. You should use a data mesh when centralized data teams become bottlenecks, when domain expertise is needed for data quality, or when your organization has multiple autonomous teams that need to share data at scale with federated governance.
Maharadha is certified across Databricks, Snowflake, and Microsoft Fabric. We select the right platform based on your workload profile — Databricks for ML-heavy lakehouse workloads, Snowflake for analytics-first use cases, and Microsoft Fabric for organizations invested in the Microsoft ecosystem. We also build multi-platform architectures and migration paths between platforms.
Maharadha delivers production data platform migrations in an average of 11 weeks using a structured 5-phase methodology. This includes discovery and audit, architecture design, iterative pipeline development, blue-green deployment with parallel run validation, and continuous monitoring. Zero-downtime migrations from legacy warehouses like Teradata, Oracle, and Netezza are standard.
Ready to modernize your data platform?
From legacy warehouse migration to greenfield lakehouse — we'll design and deliver a platform that scales with your ambitions.