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When it comes to big data processing, I prefer Databricks over other solutions.
For a lot of different tasks, including machine learning, it is a nice solution.
The biggest return on investment is the time saving for the customer.
As of now, we are raising issues and they are providing solutions without any problems.
Whenever we reach out, they respond promptly.
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.
I would rate the customer support a solid 10.
The patches have sometimes caused issues leading to our jobs being paused for about six hours.
I would rate the scalability of this solution as very high, about nine out of ten.
Databricks is an easily scalable platform.
Databricks is definitely a very stable product and reliable.
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.
We could use their job clusters, however, that increases costs, which is challenging for us as a startup.
Adjusting features like worker nodes and node utilization during cluster creation could mitigate these failures.
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.
In QlikView, I believe the improvement that should be made is to bring the costs down, as you'll have to be competitive with Power BI, aiming for at least a 30% reduction to stop the hemorrhaging to Power BI.
It is not a cheap solution.
The license cost per user or per year for QlikView is about 500 Euros annually.
The Unity Catalog is for data governance, and the Delta Lake is to build the lakehouse.
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 best features in QlikView are rapid development, the fact that I can do what I want in QlikView, and full control along with ease of use.
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.
QlikView is a Business Intelligence tool that allows you to keep tabs on all of your business-related information in a clean, clear, and easy to access database that is intuitive to build and simple to navigate. It is ideal for business owners who wish to improve overall output by creating the most productive system possible.
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