Modern Data Platform

Build One Data Foundation for Analytics, AI, and What Comes Next

Your data lives across applications, databases, cloud storage, and business systems. Build one foundation for analytics, engineering, governance, and AI, instead of adding another disconnected data environment.

Talk to a Data Expert Explore What You Can Build
Enterprise data sourcesIn
  • ERP
  • CRM
  • Databases
  • Applications
  • Cloud storage
  • Files
Modern Data Platform Built on Databricks
  • 01Data engineering
  • 02Warehousing & BI
  • 03Governance
  • 04AI & ML
One governed copy of trusted data
What the business usesOut
  • BI
  • Analytics
  • Data products
  • Machine learning
  • GenAI
When data grows, complexity follows

Your Data Is Growing. So Is the Complexity.

Most data platforms don't become complicated in one decision. They get there one new requirement at a time.

Fragmented Data

Business information sits across applications, databases, storage environments, and analytical platforms.

Duplicated Pipelines

Different teams extract, transform, and prepare the same data again for each use case.

Growing Architecture Complexity

Every new reporting or analytics need adds another pipeline, integration, or tool to maintain.

AI Without a Trusted Data Foundation

AI initiatives stall when enterprise data is fragmented, inconsistent, or hard to govern.

What should your data platform enable the business to do?

Databricks Data + AI Platform

One Platform. Multiple Data Workloads.

Databricks provides one environment for working with enterprise data across engineering, analytics, governance, machine learning, and AI.

Databricks Implemented & supported by ICS Compute
01

Data Engineering

Ingest, transform, and prepare data from different systems into trusted datasets.

02

Data Warehousing & BI

Run SQL analytics, reporting, dashboards, and business intelligence workloads.

03

Data Governance

Manage access, discovery, and lineage across enterprise data and AI assets.

04

AI & Machine Learning

Use trusted enterprise data for machine learning, GenAI, and AI applications.

Shared data layer · open formats
Unified governance across data & AI
Connects to ERP · CRM · operational databases · SaaS applications · cloud storage · existing warehouses & BI tools

Instead of building a new stack for every requirement, build on one data foundation that can evolve with the business.

Start with the workload

Start With What You Need Today

You do not need to modernize everything at once. Pick the workload with the clearest business need, then expand from there.

Workload 01

Data Warehouse Modernization

For organizations that want better reporting and analytics, or need to modernize an existing warehouse architecture.

What you can build
  • BI dashboards
  • Financial reporting
  • Customer analytics
  • Management reporting
Discuss this workload →
Workload 02

Enterprise Lakehouse

For organizations that need to bring data from many systems into one shared foundation that multiple teams can use.

What you can build
  • Data consolidation
  • Historical data
  • Cross-system analytics
  • Data products
Discuss this workload →
Workload 03

Data Engineering Modernization

For teams managing growing pipeline, transformation, and integration complexity that is becoming hard to maintain.

What you can build
  • ETL / ELT
  • Batch processing
  • Streaming
  • Data transformation
Discuss this workload →
Workload 04

AI-Ready Data Foundation

For organizations preparing enterprise data so machine learning and GenAI initiatives can move beyond pilots.

What you can build
  • Machine learning
  • Enterprise AI
  • GenAI applications
  • AI agents
Discuss this workload →
From fragmented data to a more unified foundation

What Changes With a Modern Data Platform?

  • 01

    Less Data Fragmentation

    More enterprise data in one shared architecture.

  • 02

    Reusable Data

    Trusted datasets that serve multiple teams and workloads.

  • 03

    Simpler Architecture

    Less duplication across platforms, integrations, and pipelines.

  • 04

    Stronger Governance

    Clearer access, visibility, and lineage as data use expands.

  • 05

    AI-Ready Foundation

    Enterprise data prepared for future ML and GenAI initiatives.

Not sure where to start?

Start With What You Need the Data to Do

You do not need to define the architecture before starting the conversation.

Tell us about your current data environment, the workload you want to improve, and where the current approach is getting hard to scale. ICS Compute will help assess the requirement and identify where Databricks fits.

  • 01A conversation with the ICS Compute data team
  • 02A review of your current environment and target workload
  • 03A clear view of where Databricks fits, and where it doesn’t
Talk to a Data Expert
Takes about a minute. No architecture decisions required.

We use this information only to respond to your request.

Thank you. Your request has been received. The ICS Compute data team will review your current environment and get back to you to schedule a conversation.
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FAQ

Common Questions

Is Databricks a data warehouse or a data lake?

Databricks uses a lakehouse architecture. It combines the flexibility and low-cost storage of a data lake with the performance, SQL analytics, and data management capabilities associated with a modern data warehouse, so both types of workloads can run on the same data.

Do we need to replace our existing data platform?

No. Not every implementation requires replacement. Databricks can support one specific workload alongside your existing warehouse, data lake, or BI tools, or it can become part of a broader, phased modernization.

Can Databricks support both analytics and AI?

Yes. Databricks supports data engineering, SQL analytics, BI, machine learning, and GenAI workloads in one environment, so analytics and AI teams work from the same governed data instead of separate copies.

How does Databricks support data governance?

Databricks provides centralized governance through Unity Catalog. It lets teams manage access permissions, discover data, and track lineage across tables, files, dashboards, and machine learning models from one place.

What should we implement first?

Start with the workload where the requirement and business value are already clear, such as a reporting use case, a pipeline that is hard to maintain, or a dataset needed for AI. A focused first workload delivers value sooner and creates a foundation to expand from.