1. Scalability: Databricks is built on Apache Spark, a scalable and distributed computing engine, which allows it to handle large-scale data processing tasks effectively. It can easily scale up or down based on demand, making it suitable for handling big data workloads. On the other hand, Splunk is primarily designed for log analysis and search queries, and it may not scale as efficiently as Databricks for big data processing.

2. Data Sources: Databricks supports a wide range of data sources and connectors, allowing users to easily integrate and analyze data from different platforms and file formats. It can connect to databases, cloud storage, and streaming data sources, making it versatile for data analysis. Splunk, on the other hand, focuses on log data and is generally used for analyzing machine-generated data such as logs, events, and metrics.

3. Data Analytics Capabilities: Databricks provides a rich set of data analytics capabilities, including built-in machine learning libraries and tools. It offers a collaborative environment for data scientists and analysts to develop and deploy machine learning models. Additionally, Databricks provides advanced analytics features like graph processing, data streaming, and time series analysis. Splunk, on the other hand, provides powerful search and visualization capabilities for log data analysis but has limited built-in machine learning capabilities compared to Databricks.

4. Ease of Use: Databricks provides a user-friendly interface and a collaborative workspace for data scientists and analysts. It offers integrated notebooks, which allow users to combine code, documentation, and visualizations in a single environment. Databricks also supports multiple programming languages such as Python, R, Scala, and SQL, making it easy for users to work with. Splunk, on the other hand, has a more specialized focus on log data analysis and may have a steeper learning curve for users without prior experience in working with logs.

5. Cost: Databricks pricing is based on a subscription model, which includes the compute resources used, while the storage is charged separately. The cost of using Databricks can vary based on the scale of the data processing and the usage of compute resources. Splunk, on the other hand, uses a data volume-based pricing model, where the cost is determined by the amount of data indexed and stored in Splunk. This can make Splunk a more expensive option for organizations with large data volumes.

6. Deployment Options: Databricks offers both cloud-based and on-premises deployment options. Users can choose between Databricks on AWS, Azure, or deploy it on their own infrastructure. This provides flexibility for organizations to choose the deployment option that best suits their requirements. Splunk also offers deployment options on-premises and in the cloud, but it is primarily known for its on-premises deployment.

In Summary, Databricks and Splunk differ in their scalability, data sources they support, data analytics capabilities, ease of use, cost structure, and deployment options. These differences make each platform suitable for different use cases and organizations' requirements.