1. Data Processing: Adobe Analytics is primarily focused on collecting, processing, and analyzing data related to user interactions with web content. On the other hand, Azure Databricks is a unified analytics platform that enables collaboration between data scientists, data engineers, and business analysts for big data and machine learning tasks.

2. Scalability: Adobe Analytics is designed to handle high volumes of real-time data from web and mobile sources, making it suitable for tracking user behavior on digital platforms. Whereas Azure Databricks is scalable enough to process large-scale data workloads and fits well in cloud-based environments, providing high performance for data processing tasks.

3. Integration Capabilities: Adobe Analytics seamlessly integrates with various Adobe products such as Adobe Experience Manager, Adobe Target, and Adobe Campaign, offering a comprehensive digital marketing solution. Azure Databricks, on the other hand, integrates well with other Azure services like Azure Storage, Azure SQL Database, and Azure Machine Learning, enabling users to build end-to-end data pipelines.

4. Data Processing Techniques: Adobe Analytics employs sophisticated algorithms and machine learning models to provide insights into user behavior and optimize marketing strategies. In contrast, Azure Databricks leverages Spark-based processing techniques and machine learning libraries to analyze and visualize large datasets efficiently, enabling data-driven decision-making.

5. Cost Implications: Adobe Analytics is a subscription-based platform, and the cost varies depending on the features and support levels chosen by the user. Azure Databricks offers a pay-as-you-go pricing model, allowing users to scale resources based on demand and optimize costs for data processing and analytics tasks.

6. Customization and Flexibility: Adobe Analytics provides pre-built dashboards and reports for analyzing data, with limited customization options available for users. Azure Databricks, on the other hand, offers greater flexibility for customization, allowing users to create and tailor data processing workflows and analytics pipelines according to their specific requirements.

In Summary, Adobe Analytics and Azure Databricks differ in terms of their focus on data processing, scalability, integration capabilities, data processing techniques, cost implications, and customization options.