From raw data ingestion to production AI — we cover the full stack so your team can focus on outcomes. Every engagement is built on proven patterns, open standards, and deep platform expertise.
Architect and implement enterprise Lakehouse platforms — from ingestion pipelines and Unity Catalog governance to MLflow-powered model management.
Design and build production-grade Databricks Lakehouse platforms with medallion architecture, optimized Delta Lake tables, and high-performance query patterns.
Build reliable, scalable ingestion pipelines using Auto Loader, DLT (Delta Live Tables), and structured streaming for batch and real-time data sources.
Implement Unity Catalog for unified data discovery, lineage tracking, fine-grained access controls, and audit-ready data governance across your entire lakehouse.
End-to-end ML lifecycle management — experiment tracking, model registry, automated retraining pipelines, and feature store integration on Databricks.
Configure Databricks SQL warehouses, build high-performance dashboards, and connect BI tools (Power BI, Tableau, Looker) for self-service analytics.
Migrate from legacy Hadoop, Spark clusters, or other data platforms to Databricks — with minimal downtime, full data validation, and team enablement.
Design and deploy resilient, cost-optimized cloud infrastructure on AWS, Azure, and GCP — from greenfield builds to complex enterprise migrations.
Landing zones, VPC design, IAM strategy, S3 data lakes, EMR, Glue, SageMaker, and end-to-end AWS data platform implementations.
Azure landing zones, ADLS Gen2, Synapse Analytics, ADF pipelines, Azure ML, and enterprise data platform architectures on Microsoft Azure.
GCP landing zones, BigQuery, Dataflow, Vertex AI, and GCS-based data platforms with cloud-native architecture and FinOps optimization.
Terraform, Pulumi, and CloudFormation to codify your infrastructure — ensuring repeatability, version control, and compliance across all environments.
Lift-and-shift, re-platform, and modernization strategies — including on-prem to cloud, inter-cloud, and legacy data warehouse migrations.
Cloud cost governance, right-sizing, reserved instance strategies, and Spot/Preemptible usage — reducing cloud spend by 30–60% on average.
Build reliable, scalable data platforms that deliver consistent, trusted data to every stakeholder — from operational systems to executive dashboards.
Design and build modern ELT pipelines using dbt, Spark, and cloud-native tools — with testing, documentation, and lineage built in from day one.
Architect and implement event-driven data pipelines with Kafka, Flink, and Spark Streaming for sub-second latency at petabyte scale.
Build and optimize analytical stores on Snowflake, Redshift, Synapse, BigQuery, or Databricks — with performant dimensional models and query tuning.
From exploratory analysis to production machine learning — we build models that work at enterprise scale and deliver measurable business value.
Build production-ready supervised and unsupervised models for classification, regression, clustering, forecasting, and anomaly detection.
Design reusable feature pipelines and centralized feature stores for consistent, point-in-time correct feature delivery across training and serving.
Rigorous model evaluation pipelines — bias detection, fairness audits, champion/challenger testing, and concept drift monitoring in production.
Transform data into decisions with self-service analytics, executive dashboards, and embedded intelligence that empower every stakeholder.
Build executive dashboards, operational reports, and self-service analytics workspaces in Power BI, Tableau, Looker, or Databricks SQL Dashboards.
Design and implement unified semantic layers and business metric frameworks ensuring consistent KPI definitions across every report and system.
Customer segmentation, cohort analysis, attribution modeling, and geospatial analytics to uncover the insights hidden in your data.
Treat your data pipelines like production software — with CI/CD, automated quality checks, and governance frameworks that scale with your organization.
Implement Git-based workflows, automated testing, and deployment pipelines for your Databricks notebooks, dbt models, and Spark jobs.
Automated data quality checks, expectations management, and anomaly detection with Great Expectations, dbt tests, and Soda Core.
Implement data catalogs (Unity Catalog, Apache Atlas, Collibra) with automated lineage tracking, business glossaries, and data ownership workflows.