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For a lot of different tasks, including machine learning, it is a nice solution.
When it comes to big data processing, I prefer Databricks over other solutions.
Whenever we reach out, they respond promptly.
As of now, we are raising issues and they are providing solutions without any problems.
I rate the technical support as fine because they have levels of technical support available, especially partners who get really good support from Databricks on new features.
TIBCO's support, especially for integration products, is swift.
I would rate the scalability of this solution as very high, about nine out of ten.
The patches have sometimes caused issues leading to our jobs being paused for about six hours.
Databricks is an easily scalable platform.
They release patches that sometimes break our code.
Although it is too early to definitively state the platform's stability, we have not encountered any issues so far.
Cluster failure is one of the biggest weaknesses I notice in our Databricks.
There are no significant stability issues as long as the product's capabilities are understood and not overly pushed.
If I could right-click to copy absolute paths or to read files directly into a data frame, it would standardize and simplify the process.
We use MLflow for managing MLOps, however, further improvement would be beneficial, especially for large language models and related tools.
We could use their job clusters, however, that increases costs, which is challenging for us as a startup.
TIBCO Spotfire and ClickView need improvement in scheduling, which is critical yet lacking in quality.
It is not a cheap solution.
TIBCO Spotfire's pricing is high, particularly in traditional markets like Turkey, impacting market penetration.
Databricks' capability to process data in parallel enhances data processing speed.
The platform allows us to leverage cloud advantages effectively, enhancing our AI and ML projects.
The Unity Catalog is for data governance, and the Delta Lake is to build the lakehouse.
TIBCO Spotfire integrates with R and Python, offering a differentiator from the competition.
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.
TIBCO Spotfire is a versatile data analytics platform that can handle large datasets and be deployed on-premise or in the cloud. Its dynamic reporting, easy creation of dynamic dashboards, and highly customizable tool make it a valuable asset for process data analysis, industrial performance dashboards, problem root cause analysis, business intelligence, and real-time analytics for IoT devices in the energy sector.
The solution also offers data science functions, map visuals, R and Python integration, and the ability to write HTML and JavaScript text areas. Spotfire has helped organizations deliver projects more efficiently and cost-effectively, allowing for faster data analysis.
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