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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.
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.
The support was very good.
Databricks is an easily scalable platform.
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.
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.
I would rate the stability of Databricks as highly stable, around nine out of ten.
We could use their job clusters, however, that increases costs, which is challenging for us as a startup.
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.
This feature, if made publicly available, may act as a game-changer, considering many global organizations use SAP data for their ERP requirements.
I would like to see an improvement in the live data connection, specifically making the process faster.
It is not a cheap solution.
They were practically dead even from a pricing perspective.
Databricks' capability to process data in parallel enhances data processing speed.
The Unity Catalog is for data governance, and the Delta Lake is to build the lakehouse.
The platform allows us to leverage cloud advantages effectively, enhancing our AI and ML projects.
It offers two ways to access data: by cubing the data or hitting it live.
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.
Sisense is an end-to-end business analytics software that enables users to easily prepare and analyze large, complex datasets. Sisense’s Single-Stack BI software includes data preparation, data management, analysis, visualization and reporting capabilities.
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