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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.
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.
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.
They release patches that sometimes break our code.
Databricks is definitely a very stable product and reliable.
Although it is too early to definitively state the platform's stability, we have not encountered any issues so far.
We use MLflow for managing MLOps, however, further improvement would be beneficial, especially for large language models and related tools.
They're now coming up with their IBI dashboard, and I think they're on the right track to improve that even further.
It would be beneficial to have utilities where code snippets are readily available.
It is not a cheap solution.
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.
Birst Networked BI and Analytics eliminates information silos. Decentralized users can augment the enterprise data model virtually, as opposed to physically, without compromising data governance.
A unified semantic layer maintains common definitions and key metrics.
Birst’s two-tier architecture aligns back-end sources with line-of-business or local data. Birst’s Automated Data Refinement extracts data from any source into a unified semantic layer. Users are enabled with self-service analytics through executive dashboards, reporting, visual discovery, mobile tools, and predictive analytics. Birst Open Client Interface also offers integration with Tableau, Excel and R.
Birst goes to market in two primary ways: as a direct sale, for enterprises using Birst on internal data to manage their business; and embedded, for companies who offer analytic products, by embedding and white-labeling Birst capabilities into their products.
Birst’s is packaged in 3 available formats: Platform and per-user fee; by Department or Business Unit; by end-customer (for embedded scenarios).
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.
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