Unified data analytics platform for accelerating innovation across data science,
data engineering, and business analytics, integrated with your AWS infrastructure.
One reliable and scalable data lake
for all analytics
One collaborative workspace for data and ML teams
One platform for data science, ML, and analytics
Databricks can read data securely from Redshift and integrate it with other datasets. With Delta Lake, Databricks can make that data into simplified and reliable data pipelines using optimized cluster resources. Data can then be used for machine learning in Databricks, or put back into Redshift for data warehouse workloads.
The AWS Glue service is an Apache compatible Hive serverless metastore which allows you to easily share table metadata across AWS services, applications, or AWS accounts.
This provides several concrete benefits:
Databricks is integrated with Amazon SageMaker using MLflow to enable distribution of machine learning models. Databricks is used to build collaborative ML models and train them at scale. The deployment enables real-time model serving and REST API integration.
Created automated data pipelines at scale that minimize cost with features such as auto-clustering and spot pricing. Using Delta Lake, you can scale up to the largest datasets, with high velocity data providing constant updates, instantly available for analytics.
Quickly prepare clean data at massive scale, and continuously train and deploy state-of-the-art ML models for best-in-class AI applications. Common use cases include:
Patientenorientierte Gesundheitsversorgung – mit Databricks Unified Analytics-Plattform
Built by the original creators of Apache SparkTM, the Databricks Unified Data Analytics Platform enables data processing and machine learning at massive scale — empowering healthcare organizations to drive innovations in care while reducing costs.
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Databricks integrates with Amazon security and single sign-on, making it easy to roll out across your organization. Users can access Databricks with their corporate credentials using AWS SSO. This delivers a better user experience without the need for managing separate sets of credentials.
“Migrating from an on-premises infrastructure to Databricks on AWS was key to unlocking the possibilities of our data and enabling our data team to thrive” – R Tyler Croy, Director of Platform Engineering at Scribd
“Databricks, through the power of Delta Lake and Structured Streaming, allows us to deliver alerts to our product’s users with a very limited latency, so they’re able to react to problems within their home before it affects their comfort levels.” – Steven Galsworthy, Head of Data Science at Quby
ShopRunner ingests over 1TB a day to drive online retail merchandise recommendations. They use Databricks for ingesting data, as well as for running their machine learning jobs. With the Databricks ML runtime that includes machine learning frameworks like TensorFlow, ShopRunner is making recommendations based up physical item appearance.
“Databricks enables us to decide in real time, without having to commit until we know this is a viable solution for our business.”
Paul Fryzel, Conde Nast
“Databricks democratizes data engineering and machine learning, and enables us to instill data driving principles.”
Gregory Rokita, Edmunds
“We were looking for a leader to partner with on analytics infrastructure. With Databricks we can focus our time and resources on innovating new solutions that drive our business.” – John Wulf, Principal Engineer
“Databricks really helps us to expedite model development validation and production so that we can give the intelligence power into the hands of the sales reps.” – Yong Liu — Principal Data Scientist, Outreach