Databricks is a cloud-based data and AI platform designed to help organizations process, analyze, and gain insights from large volumes of data.
It was founded by the creators of Apache Spark, and it provides a unified platform for data engineering, data science, machine learning, and business analytics.
Key Features of Databricks:
- Unified Data Platform.
- Combines data lakes and data warehouses into a Lakehouse Architecture, enabling both analytical and machine learning workloads.
- Apache Spark-Based.
- Leverages Spark for distributed data processing, enabling large-scale data transformations and analytics.
- Collaborative Notebooks.
- Provides collaborative notebooks that support multiple languages (Python, SQL, R, Scala) for teams to work together on data workflows and ML models.
- Machine Learning & AI.
- Supports ML model development, training, and deployment, with MLOps capabilities.
- Delta Lake
- An open-source storage layer that brings ACID transactions and reliability to data lakes.
- Data Engineering
- Enables ETL (Extract, Transform, Load) pipelines and data orchestration at scale.
- Integrations
- Integrates with major cloud services (Azure, AWS, GCP), BI tools (Power BI, Tableau), and data storage solutions.
Core Use Cases:
- Data engineering and ETL pipelines.
- Data warehousing and analytics (via Databricks SQL).
- Machine learning lifecycle (development, training, deployment).
- Real-time data processing and streaming analytics.
A comparison between Databricks vs Snowflake vs traditional data warehouses
Here’s a clear comparison between Databricks, Snowflake, and traditional data warehouses:
1. Databricks
| Aspect | Description |
| Architecture | Lakehouse (combines data lake + data warehouse) |
| Core Strength | Data engineering, ML/AI, real-time & batch data |
| Data Storage | Open data lake (e.g., Delta Lake on cloud storage) |
| Processing Engine | Apache Spark (distributed compute) |
| SQL Support | Strong, but primarily optimized for data science & engineering workloads |
| ML/AI Support | Built-in MLflow, notebooks, MLOps capabilities |
| Best For | Companies doing both advanced analytics and ML/AI on big data |
2. Snowflake
| Aspect | Description |
| Architecture | Cloud data warehouse (separates storage & compute) |
| Core Strength | SQL analytics, BI reporting, data sharing |
| Data Storage | Proprietary cloud storage (internal to Snowflake) |
| Processing Engine | Snowflake’s proprietary SQL engine |
| SQL Support | Extremely strong, optimized for BI and reporting |
| ML/AI Support | Limited (requires integrations with other tools like DataRobot or SageMaker) |
| Best For | Companies focused on BI, SQL workloads, and data sharing across teams and organizations |
3. Traditional Data Warehouses (e.g., Teradata, Oracle Exadata)
| Aspect | Description |
| Architecture | On-prem or hybrid data warehouse |
| Core Strength | Classic BI reporting, structured data |
| Data Storage | Proprietary on-prem storage |
| Processing Engine | SQL engines (often less elastic/scalable) |
| SQL Support | Strong |
| ML/AI Support | Very limited, often requires external systems |
| Best For | Enterprises with legacy systems, strict compliance, or low data volume needs |
Summary
| Feature | Databricks | Snowflake | Traditional DW |
| Data Types Supported | Structured, Semi-structured, Unstructured | Mostly Structured, some semi-structured | Structured |
| ML/AI Integration | Built-in | Via integrations | Limited |
| Real-time Data Support | Strong (streaming support) | Limited | Weak |
| Scalability | Very High (cloud-native) | Very High (cloud-native) | Medium (hardware-based) |
| Best Use Case | Unified data + ML/AI + BI | BI, analytics, data sharing | Traditional reporting |