One unified platform for data engineering, data science, and AI, built on an open, scalable Lakehouse.
For years, organisations had to choose between a data warehouse (fast and structured, but rigid) and a data lake (flexible and scalable, but messy). Databricks invented the Lakehouse, a single platform that delivers the best of both. Built on Apache Spark and Delta Lake, it gives your data engineers, scientists, and analysts one collaborative environment for ETL pipelines, machine learning, and real-time analytics.
Nextgenlytics implements and optimises Databricks Lakehouse environments so your data team can move faster, at lower cost, with production-grade reliability.
Lakehouse
One platform, lake + warehouse
Delta Live
Self-healing ETL pipelines
AI-Native
MLflow, Mosaic AI & Genie
Real capability
Databricks Genie (AI/BI natural language query) is now embedded here as a capability within the lakehouse, not a standalone offering. Powered by Code Cheetah for optimised PySpark and SQL generation.
From Delta Lake foundations to Unity Catalog governance, every Databricks environment we deliver is engineered for reliability, performance, and AI from day one.
We implement Delta Lake on Databricks to combine your data lake and warehouse into one, giving you ACID transactions, scalable metadata handling, and unified batch and streaming data processing on a single open platform.
We build Delta Live Tables (DLT) pipelines using Code Cheetah, our automated PySpark and SQL generation tool, with built-in data quality checks and full lineage visibility, replacing brittle, hand-crafted ETL scripts with reliable, self-healing pipelines.
Data engineers, data scientists, and analysts work together in Databricks notebooks, writing SQL, Python, Scala, or R in a single version-controlled environment. No more siloed tools, no more duplicated effort.
We configure Databricks SQL with serverless compute for lightning-fast query performance, giving your BI tools and executive dashboards real-time operational visibility without dedicated warehouse overhead.
We implement Unity Catalog to provide centralised access control, comprehensive data lineage, and consistent governance across your entire Lakehouse, so compliance is managed in one place, not patched across multiple tools.
Data engineering is the foundation every AI initiative is built on. Nextgenlytics builds that foundation on Databricks, open, scalable, and production-ready from day one.
MLflow · Mosaic AI · Genie
A governed data foundation for every AI initiative.
We fine-tune Apache Spark configurations to run your jobs faster and at lower cost, eliminating idle cluster expenses and maximising ROI on every workload.
We connect your Databricks environment to Azure, AWS, SAP S/4HANA, Microsoft Fabric, and SAP DataSphere, creating a fluid, cross-cloud data ecosystem with no blind spots.
Databricks is built for AI. We configure your Lakehouse as the data foundation for MLflow model tracking, Mosaic AI, and Databricks Genie, so your AI initiatives start on solid, governed ground.
We pair Databricks-certified engineers with FinOps discipline, so every cluster, every pipeline, and every model you ship is right-sized, secure, and ready for production AI.
