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.
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.
It's not structured support, which is why we don't use purely open-source projects without additional structured support.
Whenever we reach out, they respond promptly.
It is a distributed file system and scales reasonably well as long as it is given sufficient resources.
The patches have sometimes caused issues leading to our jobs being paused for about six hours.
Continuous management in the way of upgrades and technical management is necessary to ensure that it remains effective.
They release patches that sometimes break our code.
Cluster failure is one of the biggest weaknesses I notice in our Databricks.
The problem with Apache Hadoop arose when the guys that originally set it up left the firm, and the group that later owned it didn't have enough technical resources to properly maintain it.
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.
We use MLflow for managing MLOps, however, further improvement would be beneficial, especially for large language models and related tools.
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.
Hadoop is a distributed file system, and it scales reasonably well provided you give it sufficient resources.
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.
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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