Find out in this report how the two Cloud Data Warehouse solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI.
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.
Databricks is an easily scalable platform.
The patches have sometimes caused issues leading to our jobs being paused for about six hours.
Most of our functions or jobs are queued due to that.
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.
Cluster failure is one of the biggest weaknesses I notice in our Databricks.
I have faced stability issues, mainly due to the storage my organization has, though I am not sure if it's specifically due to the tool.
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 prefer using a small to mid-sized cluster for many jobs to keep costs low, but this sometimes doesn't support our operations properly.
Adjusting features like worker nodes and node utilization during cluster creation could mitigate these failures.
It is not a cheap solution.
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.
Developers can share their notebooks. Git and Azure DevOps integration on the Databricks side is also very helpful.
The product is not complex; I do not have to create stored procedures, functions, or views.
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.
VMware Tanzu is a robust platform tailored for data warehousing, complex analytics, BI applications, and predictive analytics. It excels in scalability, performance, and parallel processing, enhancing data handling efficiency. Users report significant productivity improvements and streamlined operations, making it ideal for comprehensive data solutions.
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