Join this session for a one-hour deep-dive on how companies can apply advanced analytics to geospatial datasets and deliver on a broad range of use cases like mining exploration, oil discovery, asset inspection, flood surveys, environment protection, facility management, transportation planning, fraud detection, and more.
Apache Spark™️ has become the de-facto open-source standard for big data processing due to its ease of use and performance. And the open source Delta Lake project enhances Spark’s lead with new capabilities like ACID transactions, Schema Enforcement and Time Travel. In this webinar, learn the advantages of combining Apache Spark 3.0 and Delta Lake. You’ll also get a walk-through of Apache Spark 3.0 as part of our Databricks Runtime 7.0 Beta.
In this virtual workshop, we will introduce you to the Databricks Unified Analytics Platform - a fully managed service on AWS that offers a collaborative workspace for Data Engineers, Data Scientists and Business Analysts for faster innovation and less operational overhead. We’ll cover best practices for enterprises to use powerful open source technologies to simplify and scale your Data and ML efforts. We’ll discuss how to leverage Apache SparkTM, the de-facto data processing and analytics engine in enterprises today, for data preparation as it unifies data at massive scale across various sources. You’ll also learn how to use Data and ML frameworks (i.e. TensorFlow, XGBoost, Scikit-Learn, etc.) to train models based on different requirements. And finally, you can learn how to use MLflow to track experiment runs between multiple users within a reproducible environment, and manage the deployment of models to production on Amazon SageMaker.
We are pleased to announce the availability of Azure Databricks in the Swiss Azure Datacenters which marks another milestone on the way to digitize Switzerland. To have a local presence helps companies in Switzerland, getting value by making the most out of their data and are able to follow the local data residency guidelines. Also the fact that the Service is locally available shows the importance the Swiss market has for both Databricks and Microsoft.
Join this webinar to hear about the optimized solution J. B. Hunt implemented, which automates real-time data pipelines for a reliable cloud data lake and provides multiple user groups an in-the-moment view of data without overwhelming internal operational systems.
Join Databricks and Microsoft as we share how you can easily query your data lake using SQL and Delta Lake on Azure. We’ll show how Delta Lake enables you to run SQL queries without moving or copying your data. We will also explain some of the added benefits that Azure Databricks provides when working with Delta Lake. The right combination of services, integrated in the right way, makes all the difference!
Government agencies are looking for ways to use analytics and AI to deliver on their missions and serve the needs of their employees and constituents. To address this need, many organizations are bringing all of their data together into a data lake, and making it ready for analytics using Delta Lake. From Delta Lake, the data can be accessed for BI and Reporting using Tableau, and for Data Science using Databricks. See how departments are standing up solutions faster and delivering greater results than anticipated with Databricks, and a demonstration of how this data can be served up and consumed in Tableau.
In this virtual workshop, we’ll walk through how the Databricks Unified Data Analytics Platform for Genomics makes it simple to deploy Spark-based bioinformatics tools in the cloud, rapidly accelerate common genomic analyses and take advantage of machine learning techniques.
In this virtual workshop, we’ll cover best practices for enterprises to use powerful open source technologies to simplify and scale your Data and ML efforts. We’ll discuss how to leverage Apache Spark™, the de-facto data processing and analytics engine in enterprises today, for data preparation as it unifies data at massive scale across various sources. You’ll also learn how to use Data and ML frameworks (i.e. TensorFlow, XGBoost, Scikit-Learn, etc.) to train models based on different requirements.