JUNE 27-30
The Databricks Unified Data Service provides a reliable and scalable platform for your data pipelines, data lakes, and data platforms. Manage your full data journey, so you can ingest, process, store, and expose data throughout your organization.
Pull data across all your different data sources, data storages, and data types, including batch and streaming. Leverage a library of connectors, integrations, and APIs for all your needs.
Run scalable and reliable data pipelines. Use Scala, Python, R, or SQL to run processing jobs quickly on distributed Spark runtimes, without having to worry about the underlying compute.
Build reliable data lakes at scale. Improve data quality, optimize storage performance, and manage stored data, all while maintaining data lake compliance and security.
Use your data lake as a shared source of truth across Data Science, Machine Learning, and Business Analytics teams — BI dashboards, production models, and everything in-between.
Delta Lake brings enhanced reliability, performance, and lifecycle management to Data Lakes. No more incomplete jobs to rollback for clean up, suspect data added into your data lake, or difficulty deleting data for compliance changes.
The Databrick Runtime is a distributed data processing engine built on a highly optimized version of Apache Spark, for up to 50x performance gains. Build pipelines, schedule jobs, and train models with easy self-service and cost-saving performance.
BI Reporting on Delta Lake delivers business analytics on your data lake. Connect directly to your most complete and recent data in your data lake with Delta Lake and SparkSQL, and use your preferred BI visualization and reporting tools for more timely business insights.
Build robust data pipelines that scale without having to worry about infrastructure, refine data quality across bronze-silver-gold tables in your data lakes, all while truly unifying batch and streaming data sources.
Simplified data engineering to help you clean and prep you data for exploratory data science or productionized ML models. Spin up autoscaling clusters on demand, for prep, training, or scoring, all available as self-service.
Run BI/SQL reporting on your data lake, for the most complete and up-to-date data possible. Use your BI tool of choice to visualize and dashboard the same single source of truth used for data science and machine learning.
At Healthdirect we use Apache Spark and Delta Lake’s fine-grained table features and data versioning to solve duplication and eliminate data redundancy. This has enabled us to develop and provide high-quality data through federation and interoperability services whilst providing the analytics to improve Health Services demand forecasting and clinical outcomes in service lines, such as Aged Care and Preventative Health.
