Databricks combines data engineering, analytics, and machine learning in a scalable cloud architecture – with open standards and strong integration into the modern data stack.
Databricks provides the technological foundation for scalable data and AI initiatives. We help companies strategically integrate Databricks into their existing data landscape — from the initial assessment to a production-ready Lakehouse architecture.
Spark-native processing for batch and streaming workloads — including Change Data Capture (CDC), Auto Loader, and data pipelines.
Databricks enables seamless integration of various data sources in real time, supporting reliable and automated ETL and ELT processes.
Delta Lake ensures consistent data storage with ACID transactions, versioning, and time travel. The Unity Catalog adds centralized governance and lineage capabilities to ensure data quality and transparency.
The Photon Engine delivers high-performance, columnar SQL queries with low latency. This allows large datasets to be analyzed efficiently — the ideal foundation for interactive BI reports and self-service analytics.
MLflow and the Feature Store support the complete ML lifecycle — from experiment tracking and model training to production deployment.
This enables teams to operationalize AI models in a standardized, reproducible, and scalable way.
Integrated workflows, triggers, and alerts enable the automation of complex data processes. Integration with dbt Core/Cloud and Airflow ensures smooth orchestration within the modern data stack.
The Unity Catalog ensures consistent access controls, permissions, and full data lineage — even for sensitive SAP data.
Databricks overcomes the typical integration barriers of proprietary SAP formats, making them usable in modern analytics and AI workflows.
A short evaluation of the platform for your specific use cases.
We assess technical compatibility, architectural options, and economic benefits — providing the foundation for your Databricks strategy.
Databricks provides several cost management features to help monitor and control spending. All billing-relevant usage data is recorded in detailed billing logs, which are available as system tables for analysis. Administrators can assign custom tags to clusters and jobs to allocate costs to specific projects, teams, or departments (for internal showback/chargeback). Additionally, budgets with alerts can be defined to issue notifications when certain spending thresholds are exceeded.
Policies can also be used to enforce cost controls — for example, by limiting the maximum cluster size or runtime. Prebuilt cost reports and dashboards are available to identify the largest cost drivers at a glance. These tools help prevent unexpected expenses and provide visibility into how costs relate to various usage scenarios.
The Databricks platform is designed from the ground up for scalability in the cloud. It can handle large data volumes by simply adding more nodes or servers (horizontal scaling). Thanks to autoscaling capabilities, Databricks automatically adjusts cluster size dynamically: when workloads increase, additional worker nodes are launched, and when demand decreases, unused nodes are automatically shut down.
The underlying data storage is virtually unlimited — all data resides in highly scalable cloud object stores (e.g., AWS S3, Azure Data Lake Storage), providing near-infinite capacity for the Lakehouse. With this architecture, Databricks can handle petabytes of data and support hundreds of concurrent users or jobs without hitting performance bottlenecks.
Furthermore, Databricks is designed for multi-cloud operation, allowing workloads to be distributed across regions or cloud providers as needed to unlock additional scalability and resilience.
Databricks is considered a highly future-proof data platform because it is built on open standards and innovative technologies. Many core components — such as Apache Spark, Delta Lake, MLflow, and Delta Sharing — were co-developed by Databricks and released as open source. This helps users avoid vendor lock-in: data is stored in open formats (e.g., Parquet/Delta) that remain accessible and usable outside of Databricks.
The platform is also multi-cloud compatible, making it easy to switch cloud providers with minimal migration effort. Databricks continuously evolves with emerging technology trends — for instance, Generative AI and LLM support have been seamlessly integrated into the environment, and ongoing performance updates ensure the platform becomes faster and more efficient over time.
Thanks to its openness, flexibility, and high pace of innovation, Databricks-based solutions are well positioned to remain aligned with evolving technological and business requirements for years to come.
Source: Databricks — 2024 CNBC Disruptor 50
