Databricks and Google Cloud Dataflow compete in the big data processing and machine learning category. Databricks appears to have an upper hand due to its advanced machine learning capabilities and collaborative environment, though it comes at a higher cost.
Features: Databricks offers an extensive set of integrated tools like Delta Lake and MLflow, machine learning support across various languages, and a collaborative notebook environment. Google Cloud Dataflow provides a user-friendly interface, leverages Apache Beam for scalable stream and batch processing, and offers cost-effective pricing models.
Room for Improvement: Databricks could improve visualization tools, third-party integration, and debugging messages. Google Cloud Dataflow may benefit from enhanced error logging, reduced job startup times, and more accessible setup processes.
Ease of Deployment and Customer Service: Databricks supports deployment across public, private, and hybrid clouds with extensive documentation, yet customer service can be inconsistent. Google Cloud Dataflow is praised for its simplicity in deployment on public clouds and has comprehensive documentation, reducing the need for support.
Pricing and ROI: Databricks tends to have higher costs, influenced by the chosen cloud infrastructure, but can deliver good ROI for large workloads. Google Cloud Dataflow is noted for lower costs, appealing to budget-conscious users, with pricing generally aligned with resource usage.
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.
The fact that no interaction is needed shows their great support since I don't face issues.
Whenever we have issues, we can consult with Google.
The patches have sometimes caused issues leading to our jobs being paused for about six hours.
Google Cloud Dataflow has auto-scaling capabilities, allowing me to add different machine types based on pace and requirements.
As a team lead, I'm responsible for handling five to six applications, but Google Cloud Dataflow seems to handle our use case effectively.
They release patches that sometimes break our code.
Cluster failure is one of the biggest weaknesses I notice in our Databricks.
The job we built has not failed once over six to seven months.
I have not encountered any issues with the performance of Dataflow, as it is stable and backed by Google services.
It would be beneficial to have utilities where code snippets are readily available.
Adjusting features like worker nodes and node utilization during cluster creation could mitigate these failures.
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.
Outside of Google Cloud Platform, it is problematic for others to use it and may require promotion as an actual technology.
Dealing with a huge volume of data causes failure due to array size.
It is part of a package received from Google, and they are not charging us too high.
Databricks' capability to process data in parallel enhances data processing speed.
The notebooks and the ability to share them with collaborators are valuable, as multiple developers can use a single cluster.
It supports multiple programming languages such as Java and Python, enabling flexibility without the need to learn something new.
The integration within Google Cloud Platform is very good.
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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