Databricks is a unified analytics platform designed for big data engineering, machine learning, and AI, enabling advanced data science and collaborative development.
Combine the openness of data lakes with the reliability of data warehouses in a single architecture.
Dynamically adjust compute resources to manage the largest and most complex data and AI workloads.
Shared, multi-language notebooks allow engineers, analysts, and scientists to co-develop solutions.
Natively integrate with TensorFlow, PyTorch, and other frameworks for end-to-end model development and deployment.
Provide ACID transactions, schema enforcement, and time travel queries to ensure consistency and reliability in data lakes.
Built on Spark, enabling fast, distributed computing for large-scale data processing and transformation tasks.
BIG DATA
Databricks is designed for organisations working with massive datasets. Its Spark-based engine can process structured and unstructured data at petabyte scale, enabling efficient ETL, data preparation, and exploration that would overwhelm traditional systems.
MACHINE LEARNING
Databricks provides an end-to-end environment for ML, from preparing training data to model development and deployment. With integrated MLflow, data scientists can track experiments, collaborate on code, and push models into production with reduced friction.
STREAMING ANALYTICS
Databricks enables real-time analytics by processing data streams from IoT devices, logs, or transactions. This supports time sensitive use cases like fraud detection, predictive maintenance, and operational monitoring where immediate insight is critical.
DATA LAKE MANAGEMENT
Delta Lake brings reliability and governance to large scale data lakes by ensuring ACID compliance, schema enforcement, and time-travel queries. This enables organisations to confidently use lakes for both raw storage and production grade analytics.
COLLABORATIVE DATA SCIENCE
Through shared, multi-language notebooks, Databricks fosters collaboration between engineers, analysts, and scientists. Teams can experiment, refine, and operationalise models together, reducing silos and accelerating the pace of data-driven innovation.
Databricks enables big data engineering and AI at scale. P42 helps clients build robust lakehouse architectures, manage Delta Lake, and operationalise Machine Learning models.
We accelerate time-to-insight, improve collaboration across teams, and turn raw data into competitive advantage.
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