Data, Analytics & AI with Databricks

Enterprise Lakehouse Platform

Build a modern enterprise data platform using Databricks Lakehouse architecture. We can design the overall data structure, catalogs, schemas, storage patterns and processing layers needed to bring data from operational databases, applications, cloud platforms, files, APIs, streaming systems and other enterprise sources into a governed analytical environment. Depending on the requirements, the architecture can use patterns such as Bronze, Silver and Gold layers to progressively ingest, clean, enrich and prepare data for downstream consumption.

Data Integration & Engineering

Design and implement scalable data engineering solutions using Databricks. We can build data ingestion, transformation and orchestration processes using SQL, Python, PySpark, notebooks and Databricks-native data engineering capabilities.

The solution can support:

• Batch processing
• Incremental processing
• Change data capture
• Streaming data
• Data transformation
• Data quality
• Dependency management
• Workflow orchestration

The objective is not simply to build pipelines that work, but to build engineering processes that are maintainable, observable and scalable.

Lakeflow

Build and orchestrate enterprise data pipelines using Databricks Lakeflow. Lakeflow provides capabilities for data ingestion, transformation and orchestration, including declarative pipelines for batch and streaming workloads. Databricks Lakeflow pipelines use Apache Spark Declarative Pipelines and can automatically manage dependencies and incremental processing. We can help determine where declarative pipelines, jobs, notebooks and other orchestration patterns are most appropriate within your architecture.

Delta Lake & Data Management

Use Delta Lake as the foundation for reliable analytical data. Delta Lake adds capabilities such as ACID transactions and schema enforcement to data lake storage, helping organizations manage large analytical datasets with greater reliability. We can help design Delta tables, partitioning or clustering strategies, data retention, optimization and maintenance practices appropriate for your workloads.

Modern Data Warehouse with Databricks SQL

Build data warehouse and analytical solutions using Databricks SQL. Databricks SQL provides SQL-based data warehousing directly on the lakehouse and runs through SQL warehouses. This allows SQL analysts and BI tools to work with governed lakehouse data without requiring a separate traditional warehouse platform.

We can design:

• Dimensional models
• Analytical tables
• Data marts
• SQL-based transformation layers
• Views and materialized views
• Semantic and metric layers
• BI consumption patterns

The architecture can be designed for SQL users while continuing to use the same governed data platform used by engineering and AI workloads.

Unity Catalog & Data Governance

Design and implement a governed Databricks environment using Unity Catalog. Unity Catalog provides centralized governance for data and AI assets, including access control, lineage, auditing and data discovery across Databricks environments.

We can help design:

• Catalog and schema structures
• Data ownership
• Workspace access
• Managed and external data
• Storage credentials and external locations
• Role-based access
• Data lineage
• Audit requirements
• Development and production boundaries
• Governance across data and AI assets

Governance should be part of the architecture from the beginning rather than added after the platform becomes difficult to control.

Streaming & Real-Time Data Processing

Design data platforms that process continuously changing data rather than relying entirely on scheduled batch workloads. Using Apache Spark Structured Streaming, Lakeflow and related Databricks capabilities, we can build streaming architectures for workloads that need frequent or near-real-time processing. We can help determine when traditional batch, micro-batch, streaming or other supported processing modes are appropriate based on the business requirement rather than selecting a technology first.

Business Intelligence & Analytics

Enable analytical and BI workloads directly over governed Databricks data. Using Databricks SQL, SQL warehouses, metric views, dashboards and integrations with external BI platforms such as Power BI, we can create analytical layers that make engineering data usable by analysts and business users. Databricks SQL also supports AI/BI dashboards and reusable governed business metrics through Unity Catalog metric views.

Machine Learning & MLflow

Extend the data platform into machine learning workloads using Databricks and MLflow. Databricks provides capabilities for experimentation, feature engineering, model management, deployment and MLOps, with MLflow integrated into the broader data science lifecycle. We can help design architectures where machine learning teams work with governed enterprise data while maintaining appropriate lifecycle management and deployment practices.

AI & Agentic Solutions

Extend your Databricks data platform into generative AI and agentic solutions. Enterprise AI solutions often require more than access to a language model. They need governed enterprise data, retrieval, business logic, APIs, tools, model management, monitoring and secure deployment. Databricks provides capabilities for building AI applications while allowing data and AI assets to remain governed through Unity Catalog. We can help design solutions where AI applications and agents securely use enterprise data and combine it with models, tools, APIs and business processes.

Performance & Cost Optimization

A technically correct Databricks implementation can still become expensive or slow if compute, data layout and workloads are not designed appropriately.

We can review and optimize areas such as:

• Compute selection
• Serverless versus classic compute
• SQL warehouses
• Spark workloads
• Data layout
• Delta optimization
• Clustering
• Query performance
• Shuffle and skew
• Pipeline execution
• Job scheduling
• Workload concurrency
• Cost utilization

Performance and cost should be considered as part of the architecture rather than only after the system enters production.

Complete Databricks Implementation – Outsource the Entire Project to Us

You provide the business requirements and access to the required systems, and we take responsibility for designing and implementing the solution.

A complete engagement can include: Requirements → Architecture → Databricks Environment → Governance → Data Integration → Data Engineering → Lakehouse → Data Warehouse → Analytics/AI → Deployment → Knowledge Transfer.

Depending on your requirements, the implementation may cover:

• Workspace and account architecture
• Unity Catalog
• Cloud storage integration
• Data ingestion
• Lakeflow
• Delta Lake
• Batch and streaming pipelines
• Lakehouse design
• Databricks SQL
• BI integration
• ML and AI workloads
• Security and governance
• Deployment
• Performance optimization

This model is suitable when you need an experienced team to take responsibility for delivering the Databricks solution end to end.

Architecture & Design – Let Us Design It Before You Build It

You may already have an internal engineering team but need experienced architects to make the important architectural decisions before implementation begins. We can work with you on:

• Existing platform assessment
• Databricks suitability assessment
• Target architecture
• Account and workspace architecture
• Unity Catalog design
• Catalog and schema strategy
• Storage architecture
• Lakehouse architecture
• Medallion architecture
• Data integration patterns
• Batch and streaming architecture
• Lakeflow design
• Databricks SQL architecture
• Security and governance
• Compute strategy
• Performance considerations
• Cost considerations
• Development and deployment strategy
• Implementation roadmap

At the end of the engagement, your team can continue the implementation with a clearly defined architecture and implementation plan.

Databricks Experts & Resources – Extend Your Existing Team

If your organization already owns and manages the project, we can provide experienced Databricks architects, data engineers and consultants to work alongside your existing team. Resources can support a specific technical area or participate throughout the implementation. This model is suitable when you need additional Databricks expertise or delivery capacity without outsourcing the complete project.