Snowflake and Databricks are competing in the data platform category. Snowflake appears to have the upper hand in ease of deployment and customer service, while Databricks is preferred for feature richness.
Features: Snowflake users value its seamless integration capabilities, robust data warehousing, and ease of use. Databricks stands out for advanced analytics, real-time data processing, and machine learning features.
Room for Improvement: Snowflake could improve in real-time analytics, simplified pricing models, and performance at scale. Databricks users seek better SQL support, a more intuitive user interface, and improved documentation.
Ease of Deployment and Customer Service: Snowflake has a straightforward deployment process and excellent customer service. Databricks, while powerful, is more complex to deploy and has mixed reviews in customer support.
Pricing and ROI: Snowflake offers a predictable pricing structure and high ROI based on user feedback. Databricks, though more expensive initially and with a steeper learning curve, is considered worth the investment by users for its extensive functionality.
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.
I received great support in migrating data to Snowflake, with quick responses and innovative solutions.
The technical support from Snowflake is very good, nice, and efficient.
The patches have sometimes caused issues leading to our jobs being paused for about six hours.
The billing doubles with size increase, but processing does not necessarily speed up accordingly.
Snowflake is very scalable and has a dedicated team constantly improving the product.
They release patches that sometimes break our code.
Cluster failure is one of the biggest weaknesses I notice in our Databricks.
Snowflake as a SaaS offering means that maintenance isn't an issue for me.
Snowflake is very stable, especially when used with AWS.
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.
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 use MLflow for managing MLOps, however, further improvement would be beneficial, especially for large language models and related tools.
Enhancements in user experience for data observability and quality checks would be beneficial, as these tasks currently require SQL coding, which might be challenging for some users.
Cost reduction is one area I would like Snowflake to improve.
Snowflake's pricing is on the higher side.
Snowflake lacks transparency in estimating resource usage.
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.
One key feature is the separation of compute and storage, which eliminates storage limitations.
The scalability options it provides, addressing issues without tying workloads into one virtual machine, enhance functionality.
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.
Snowflake is a cloud-based data warehousing solution for storing and processing data, generating reports and dashboards, and as a BI reporting source. It is used for optimizing costs and using financial data, as well as for migrating data from on-premises to the cloud. The solution is often used as a centralized data warehouse, combining data from multiple sources.
Snowflake has helped organizations improve query performance, store and process JSON and XML, consolidate multiple databases into one unified table, power company-wide dashboards, increase productivity, reduce processing time, and have easy maintenance with good technical support.
Its platform is made up of three components:
Snowflake has many valuable vital features. Some of the most useful ones include:
There are many benefits to implementing Snowflake. It helps optimize costs, reduce downtime, improve operational efficiency, and automate data replication for fast recovery, and it is built for high reliability and availability.
Below are quotes from interviews we conducted with users currently using the Snowflake solution:
Sreenivasan R., Director of Data Architecture and Engineering at Decision Minds, says, "Data sharing is a good feature. It is a majorly used feature. The elastic computing is another big feature. Separating computing and storage gives you flexibility. It doesn't require much DBA involvement because it doesn't need any performance tuning. We are not doing any performance tuning, and the entire burden of performance and SQL tuning is on Snowflake. Its usability is very good. I don't need to ramp up any user, and its onboarding is easier. You just onboard the user, and you are done with it. There are simple SQL and UI, and people are able to use this solution easily. Ease of use is a big thing in Snowflake."
A director of business operations at a logistics company mentions, "It requires no maintenance on our part. They handle all that. The speed is phenomenal. The pricing isn't really anything more than what you would be paying for a SQL server license or another tool to execute the same thing. We have zero maintenance on our side to do anything and the speed at which it performs queries and loads the data is amazing. It handles unstructured data extremely well, too. So, if the data is in a JSON array or an XML, it handles that super well."
A Solution Architect at a wholesaler/distributor comments, "The ability to share the data and the ability to scale up and down easily are the most valuable features. The concept of data sharing and data plumbing made it very easy to provide and share data. The ability to refresh your Dev or QA just by doing a clone is also valuable. It has the dynamic scale up and scale down feature. Development and deployment are much easier as compared to other platforms where you have to go through a lot of stuff. With a tool like DBT, you can do modeling and transformation within a single tool and deploy to Snowflake. It provides continuous deployment and continuous integration abilities. There is a separation of storage and compute, so you only get charged for your usage. You only pay for what you use. When we share the data downstream with business partners, we can specifically create compute for them, and we can charge back the business."
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