Find out what your peers are saying about Snowflake Computing, Microsoft, Google and others in Cloud Data Warehouse.
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.
Instead, we rely on third-party partners recognized by IBM, who provide cost-effective support.
We have a multi-level support system, with the initial level handled by the company we bought the license from and subsequent support from IBM.
The patches have sometimes caused issues leading to our jobs being paused for about six hours.
Databricks is an easily scalable platform.
Scalability is quite hard to implement in TM1, largely since the on-premise installation chosen back in 2014.
Scalability is straightforward but it is pricey since it's a SaaS model priced per user.
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.
A stable platform prevents loss of time during this process.
We use MLflow for managing MLOps, however, further improvement would be beneficial, especially for large language models and related tools.
This feature, if made publicly available, may act as a game-changer, considering many global organizations use SAP data for their ERP requirements.
We could use their job clusters, however, that increases costs, which is challenging for us as a startup.
The abundance of features results in complexity, requiring strict guidelines for developers to ensure simplistic approaches are adhered to.
IBM's visualization needs significant improvement.
It is not a cheap solution.
Given the product's old architecture and interface, they need to make it more affordable.
While IBM's solutions were costly before, the introduction of SaaS models has reduced prices significantly.
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.
Developers can share their notebooks. Git and Azure DevOps integration on the Databricks side is also very helpful.
It also integrates machine learning and AI engines, enabling us to use algorithms for inventory forecasting which optimizes our inventory and replenishment rates.
Its stability helps controllers win time in their planning processes.
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.
IBM Planning Analytics is an integrated planning solution that uses AI to automate planning, budgeting, and forecasting and drive more intelligent workflows.
Built on TM1, IBM’s powerful calculation engine, this enterprise performance management tool allows you to transcend the limits of manual planning and become the Analytics Hero your business needs. Quickly and easily drive faster, more accurate plans for FP&A, sales, supply chain and beyond.
We monitor all Cloud Data Warehouse reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.