Databricks is one of the most widely adopted and modern data and analytics platforms globally, designed to unify data engineering, data science, and business analytics on a single technological foundation. Thousands of organizations, from innovative startups to large corporations and government entities, use Databricks to accelerate the development of data-driven products, reduce operational complexity, and drive innovation through artificial intelligence and machine learning at scale.
Why Databricks?
Velocity in value delivery
It accelerates the entire cycle, from data ingestion to the production of analytical or machine learning models, reducing time-to-insight and improving data-driven decision-making.
Unification of teams and workflows
Databricks unifies engineers, data scientists and analysts on a single platform, fostering frictionless collaboration and eliminating silos and unnecessary data movement between tools.
Reduction in operating costs
Thanks to the lakehouse model, duplications are avoided and multiple workloads are allowed on a single source, reducing operational complexity and infrastructure and maintenance costs.
Improved governance and compliance
With Unity Catalog and Delta Lake, the control of permissions, lineage, and data quality is centralized, facilitating compliance with regulations such as GDPR, HIPAA, and SOX.
Cloud Computing Leadership
Databricks named a Leader in the 2024 Gartner® Magic Quadrant™ for Data Science and Machine Learning Platforms.
Databricks named a Leader in the 2024 Gartner® Magic Quadrant™ for Data Science and Machine Learning Platforms.
Features
Databricks has positioned itself as a leader in data analytics and data science platforms through a combination of technological innovation, a unified approach, and business adaptability, which has been supported by analysts such as Gartner and Forrester in their respective Magic Quadrant and Wave reports (Magic Quadrant for Data Science and ML Platforms, Forrester Wave for AI/ML Platforms). Here’s a summary of its key differentiating features:
Lakehouse Architecture
It works natively on AWS, Azure, and Google Cloud, integrating with each cloud's native tools (S3, Azure Data Lake, Google Storage, Power BI), making it easy to adapt to the client's technological stack.
It works natively on AWS, Azure, and Google Cloud, integrating with each cloud's native tools (S3, Azure Data Lake, Google Storage, Power BI), making it easy to adapt to the client's technological stack.
Delta Lake, the storage standard
Delta Lake, the open-source format powered by Databricks, offers ACID transactions, versioning, and high performance for both batch and streaming workloads, serving as the technical core of the Lakehouse.
Delta Lake, the open-source format powered by Databricks, offers ACID transactions, versioning, and high performance for both batch and streaming workloads, serving as the technical core of the Lakehouse.
Data Teams, connected
Databricks integrates notebooks, version control, repositories, and real-time collaboration, enabling data engineers, scientists, and analysts to work together in a single environment, with support for SQL, Python, Scala, and R.
Databricks integrates notebooks, version control, repositories, and real-time collaboration, enabling data engineers, scientists, and analysts to work together in a single environment, with support for SQL, Python, Scala, and R.
Distributed power with Spark + Photon
Built on Apache Spark, Databricks includes Photon, its optimized engine that boosts SQL analytical workload performance by up to 20x, positioning it as a competitor to Snowflake and BigQuery.
Built on Apache Spark, Databricks includes Photon, its optimized engine that boosts SQL analytical workload performance by up to 20x, positioning it as a competitor to Snowflake and BigQuery.
Power ML with integrated MLOps
It includes tools for the entire ML lifecycle: experiment tracking (MLflow), deployment, versioning, and model monitoring. This makes it easier to move from experiments to production without needing external tools.
It includes tools for the entire ML lifecycle: experiment tracking (MLflow), deployment, versioning, and model monitoring. This makes it easier to move from experiments to production without needing external tools.
Security, governance and corporate compliance
With Unity Catalog, Databricks centralizes metadata, permissions, and governance, ensuring compliance with standards such as GDPR and HIPAA, making it ideal for regulated environments.
With Unity Catalog, Databricks centralizes metadata, permissions, and governance, ensuring compliance with standards such as GDPR and HIPAA, making it ideal for regulated environments.
Multi-cloud compatibility and flexibility
It works natively on AWS, Azure, and Google Cloud, integrating with each cloud's native tools (such as S3, Azure Data Lake, Google Storage, Power BI, etc.), making it adaptable to the client's technological stack.
It works natively on AWS, Azure, and Google Cloud, integrating with each cloud's native tools (such as S3, Azure Data Lake, Google Storage, Power BI, etc.), making it adaptable to the client's technological stack.
Databricks + ZAT: Your competitive advantage
Innovation, speed, and control with a partner that supports you.
