More than ever before, companies are relying on their big data and artificial intelligence (AI) systems to find new ways to reduce costs and accelerate decision-making. However, customers using on-premises systems struggle to realize these benefits due to administrative complexity, inability to scale their fixed infrastructure cost-effectively, and lack of a shared collaborative environment for data engineers, data scientists and developers.

To make it easier for customers to modernize their on-premises Spark and big data workloads to the cloud, we’re announcing a new migration offer with Azure Databricks. The offer includes:

  • Up to a 52 percent discount over the pay-as-you-go pricing when using the Azure Databricks Unit pre-purchase plans. This means that customers can free themselves from the complexities and constraints of their on-premises solutions and realize the benefits of the fully managed Azure Databricks service at a significant discount.
  • Free migration assessment for qualified customers.

Azure Databricks is a fast, easy, and collaborative Apache Spark-based service that simplifies building big data and AI solutions. Since its debut two years ago, Azure Databricks has experienced significant adoption from customers, such as Shell, Cerner, Advocate Aurora Health, and Bosch, which are using it to run mission-critical big data and AI workloads.

We’ve also seen several customers accelerating their migration of on-premises systems to Azure Databricks for the following reasons:

  • Reduced costs and enhanced security: Moving to the fully managed Azure Databricks environment enables customers to reduce administrative costs while also helping increase overall security and compliance of their solutions. Autoscaling and auto-termination of jobs help reduce operational costs. In addition, native integration with Azure Data Lake Storage Gen 2, which supports the Hadoop Distributed File System (HDFS) format, helps reduce migration costs.
  • Increased agility: On-premises systems are limited to a fixed amount of compute and storage. With Azure Databricks, customers can quickly scale up or down compute resources as needed to accelerate jobs and increase productivity.
  • Enhanced collaboration: Azure Databricks empowers data engineers, data scientists and developers to collaborate in an interactive workspace using the languages and frameworks of their choice. Integration with Azure Machine Learning, Synapse Analytics, and Cosmos DB provides users easy access to new technologies, thereby accelerating overall time to value.

This new offer is designed to help customers who are still using on-premises big data systems but are looking to move to the cloud and take advantage of Azure Databricks capabilities.

Offer details

The Azure Databricks Unit pre-purchase plan already enables customers to save up to 37 percent over pay-as-you-go pricing when they pre-pay for one or three-year commitments. With the migration offer, we are adding an extra 25 percent discount for three-year pre-purchase plan larger than 150,000 DBCUs and a 15 percent discount for one-year pre-purchase plan larger than 100,000 DBCUs. The offer is valid until January 31, 2021. More information on the Azure Databricks Unit pre-purchase plan can be found on the pricing page.

All Azure Databricks SKUs—Premium and Standard SKUs for Data Engineering Light, Data Engineering, and Data Analytics—are eligible for this migration offer. The Azure Databricks pre-purchase units can be used at any time and can be consumed across all Databricks workload types and tiers.

Qualified customers will also receive a free migration evaluation. This includes an assessment of current tools, systems, and processes, and a two-day workshop to identify value drivers, prioritize use cases, and define the future state architecture.

Get started today

Learn more about migration to Azure Databricks and the offer by watching this webinar. For more information on discount tiers, please visit the Azure Databricks pricing page and contact your sales team to take advantage of this offer.