Databricks and Azure Stream Analytics are key players in the analytics software category. Based on the feature set, Databricks appears to have the upper hand with its comprehensive platform for large-scale analytics and machine learning.
Features: Databricks offers strong features for large-scale analytics, integrating data engineering, streaming, and machine learning. Its fast performance on big data via a Spark cluster and Delta Lake for data governance are notable. Azure Stream Analytics excels in real-time processing, particularly suited for IoT use cases, and offers seamless integration with the Azure ecosystem.
Room for Improvement: Databricks could improve visualization capabilities and expand integrations with more machine learning libraries. There are also concerns about pricing and the need for a more intuitive interface. For Azure Stream Analytics, better scalability and robust data handling are needed, along with more flexible query customization and ease of connection to non-Azure platforms.
Ease of Deployment and Customer Service: Databricks is deployable on both public and hybrid clouds, with comprehensive documentation and generally responsive support. However, users report occasional delays in support response times. Azure Stream Analytics benefits from Microsoft's support structure but also faces support delays due to indirect communication channels.
Pricing and ROI: Databricks is considered expensive, but its pay-as-you-go model provides flexibility and scalability, offering justified ROI for specific use conditions. Conversely, Azure Stream Analytics may be more cost-effective for smaller or simpler use cases, but costs can increase with higher streaming unit usage.
For a lot of different tasks, including machine learning, it is a nice solution.
Whenever we reach out, they respond promptly.
The patches have sometimes caused issues leading to our jobs being paused for about six hours.
They release patches that sometimes break our code.
We prefer using a small to mid-sized cluster for many jobs to keep costs low, but this sometimes doesn't support our operations properly.
Azure Stream Analytics is a robust real-time analytics service that has been designed for critical business workloads. Users are able to build an end-to-end serverless streaming pipeline in minutes. Utilizing SQL, users are able to go from zero to production with a few clicks, all easily extensible with unique code and automatic machine learning abilities for the most advanced scenarios.
Azure Stream Analytics has the ability to analyze and accurately process exorbitant volumes of high-speed streaming data from numerous sources at the same time. Patterns and scenarios are quickly identified and information is gathered from various input sources, such as social media feeds, applications, clickstreams, sensors, and devices. These patterns can then be implemented to trigger actions and launch workflows, such as feeding data to a reporting tool, storing data for later use, or creating alerts. Azure Stream Analytics is also offered on Azure IoT Edge runtime, so the data can be processed on IoT devices.
Top Benefits
Reviews from Real Users
“Azure Stream Analytics is something that you can use to test out streaming scenarios very quickly in the general sense and it is useful for IoT scenarios. If I was to do a project with IoT and I needed a streaming solution, Azure Stream Analytics would be a top choice. The most valuable features of Azure Stream Analytics are the ease of provisioning and the interface is not terribly complex.” - Olubisi A., Team Lead at a tech services company.
“It's used primarily for data and mining - everything from the telemetry data side of things. It's great for streaming and makes everything easy to handle. The streaming from the IoT hub and the messaging are aspects I like a lot.” - Sudhendra U., Technical Architect at Infosys
Databricks is utilized for advanced analytics, big data processing, machine learning models, ETL operations, data engineering, streaming analytics, and integrating multiple data sources.
Organizations leverage Databricks for predictive analysis, data pipelines, data science, and unifying data architectures. It is also used for consulting projects, financial reporting, and creating APIs. Industries like insurance, retail, manufacturing, and pharmaceuticals use Databricks for data management and analytics due to its user-friendly interface, built-in machine learning libraries, support for multiple programming languages, scalability, and fast processing.
What are the key features of Databricks?
What are the benefits or ROI to look for in Databricks reviews?
Databricks is implemented in insurance for risk analysis and claims processing; in retail for customer analytics and inventory management; in manufacturing for predictive maintenance and supply chain optimization; and in pharmaceuticals for drug discovery and patient data analysis. Users value its scalability, machine learning support, collaboration tools, and Delta Lake performance but seek improvements in visualization, pricing, and integration with BI tools.
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