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What is Databricks and is it the right data and AI platform for your organisation?
Databricks is a unified data, analytics, and AI platform built to simplify and accelerate data engineering, machine learning, and business intelligence.
Founded by the creators of Apache Spark, Databricks enables organisations to process massive volumes of data and build data-driven applications in a collaborative, cloud-native environment.
Its Lakehouse architecture combines the best of data lakes and data warehouses - providing one platform for both structured and unstructured data, batch and real-time processing, and AI model development. Databricks is used by enterprises across finance, healthcare, government, retail, and energy to modernise data strategies, enable advanced analytics, and operationalise AI.
This page outlines what Databricks offers, where it fits best, and how to evaluate its role in your data, AI, and automation roadmap.
Key features of Databricks
- Unified Lakehouse platform for data engineering, analytics, and ML
- Scalable compute for ETL, real-time data, and streaming workloads
- Collaborative notebooks and workflows for data scientists, engineers, and analysts
- Machine learning lifecycle management with MLflow
- Integration with Apache Spark, Delta Lake, and open-source frameworks
- Built-in connectors to Snowflake, Azure Synapse, Power BI, and more
- Native support for Python, SQL, R, Scala, and Java
- Available on AWS, Azure, and Google Cloud
Which organisations is Databricks best suited for?
Databricks is typically a strong fit for:
- Mid-sized to large enterprises investing in modern data infrastructure
- Organisations unifying data science, data engineering, and analytics teams
- Businesses with complex ETL pipelines or real-time data requirements
- Sectors such as financial services, healthcare, energy, retail, and government
- Teams scaling their use of AI and automation beyond experimentation
Pros and cons of Databricks
Where Databricks delivers value:
- Unifies data lakes and data warehouses for greater flexibility
- Accelerates time-to-value for analytics and machine learning projects
- Designed for collaboration across technical teams
- Strong performance for large-scale data processing
- Integrates easily with modern data and AI tools
Where it may fall short:
- Requires advanced skills in data engineering or DevOps to manage effectively
- Not a business-facing platform – must be paired with BI or operational tools
- Complexity and cost can increase without strong internal governance
- May be overpowered for organisations with basic reporting or analytics needs
Alternatives to Databricks
Depending on your data architecture and use cases, alternatives or complementary platforms may include:
- Snowflake – scalable cloud data platform for analytics and secure sharing
- Microsoft Azure Synapse – integrated analytics for Microsoft-centric organisations
- Google BigQuery – serverless warehouse with real-time query performance
- Amazon SageMaker – AI/ML platform for AWS users
- IBM Watsonx – designed for AI governance and foundation model deployment
Compare these in our Data, AI & Automation Guide and Data Management Guide.
Planning your Databricks journey
Whether you’re exploring Databricks for advanced analytics, AI development, or data infrastructure, SMC can help you define a scalable, future-ready strategy.
Expert insights from SMC
At Solution Minds Consulting, we work with enterprise and public sector clients to evaluate platforms like Databricks as part of broader data and AI transformation programs. For many, the value lies not just in technology performance – but in its ability to support long-term data strategy, talent enablement, and automation maturity.
We help clarify your data needs, assess platform fit, and align solution architecture with business priorities – whether you’re building new data capabilities or replacing legacy systems.
Explore our support in Digital Strategy and Roadmaps, Governance and Advisory, and Software Selection.