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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.
Tech support for Salesforce Einstein Analytics is generally 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.
Although it is too early to definitively state the platform's stability, we have not encountered any issues so far.
They release patches that sometimes break our code.
Databricks is definitely a very stable product and reliable.
There are certain glitches, especially when the modules are upgraded or when there is a source code update, causing the entire tool to go offline.
We could use their job clusters, however, that increases costs, which is challenging for us as a startup.
This feature, if made publicly available, may act as a game-changer, considering many global organizations use SAP data for their ERP requirements.
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.
There are certain glitches, especially when the modules are upgraded or when there is a source code update, causing the entire tool to go offline.
There is a learning curve associated with Salesforce Einstein Analytics, particularly since users need to learn a new language.
It is not a cheap solution.
A benefit is that the pricing is available online, ensuring there are no hidden costs.
In general, I would rate it as a little bit on the expensive side compared to other available options.
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.
Databricks' capability to process data in parallel enhances data processing speed.
It allows for a personalized customer experience by providing insights.
Their machine learning model, which they have integrated, provides us with accurate data and creates projection maps.
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.
Salesforce Einstein Analytics is a customer and business analytics platform that’s optimized for mobile use and brings flexible customer analytics to everyone in the company. It works with many types of data, from many data sources, and it can change the way your company answers critical questions. Einstein Analytics allows you to:
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