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CAPABILITY 12

Enterprise Databricks

Bring data engineering, analytics and machine learning together on your enterprise Databricks platform.

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BUSINESS CONTEXT AND APPROACH

Bring data engineering, analytics and machine learning together on your enterprise Databricks platform.

Enterprise Databricks services connect data engineering and machine learning on a shared lakehouse foundation. We design reusable data products, governed workspaces and operating practices that fit your existing cloud environment.

This service covers the standalone enterprise Databricks platform. SAP Databricks remains part of our SAP BDC service context. We assess ingestion, compute, catalog design and data-sharing requirements before defining the implementation scope.

OUTCOME AND MEASURES

Reusable data pipelines and a governed workspace for analytics and machine learning.

Before delivery starts we agree baselines for:

  • Data freshness and pipeline success rate
  • Reuse of governed datasets across workloads
  • Job performance and compute cost
ENTERPRISE DATABRICKS DELIVERY ARCHITECTURE

Ingest, refine, govern, build and operate.

A reference delivery architecture for a governed lakehouse where data engineering, analytics and machine learning share one foundation.

ANIMATED SCENARIO

Build a reusable data product

The scenario follows raw operational feeds through curated Delta Lake models, Unity Catalog governance and reuse by analytics and machine learning.

REPRESENTATIVE USE CASES

Use cases shaped
around real work.

Representative patterns are tailored to your landscape, controls, data readiness and business priorities.

01

Operational lakehouse

Standardise raw operational feeds into curated datasets that reporting and data-science teams can reuse.

02

Predictive supply planning

Develop and track demand models alongside the governed data pipelines that produce their inputs.

03

Streaming exception analysis

Process incoming events and make validated operational exceptions available for investigation.

WHAT WE DELIVER

Artifacts your teams can use.

  • Lakehouse architecture and batch or streaming pipelines
  • Delta Lake data modelling and Spark or SQL engineering
  • Unity Catalog governance and access design
  • MLflow experiments, model deployment and MLOps integration
AGREED BASELINES
01Data freshness and pipeline success rate
02Reuse of governed datasets across workloads
03Job performance and compute cost
SEESAP Databricks within SAP BDC
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