We performed a comparison between Databricks and ELK Kibana based on real PeerSpot user reviews.
Find out in this report how the two Data Science Platforms solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI."The integration with Python and the notebooks really helps."
"Databricks makes it really easy to use a number of technologies to do data analysis. In terms of languages, we can use Scala, Python, and SQL. Databricks enables you to run very large queries, at a massive scale, within really good timeframes."
"The solution is very easy to use."
"The initial setup is pretty easy."
"Databricks' most valuable feature is the data transformation through PySpark."
"The processing capacity is tremendous in the database."
"The capacity of use of the different types of coding is valuable. Databricks also has good performance because it is running in spark extra storage, meaning the performance and the capacity use different kinds of codes."
"Specifically for data science and data analytics purposes, it can handle large amounts of data in less time. I can compare it with Teradata. If a job takes five hours with Teradata databases, Databricks can complete it in around three to three and a half hours."
"Having a tool where you can find logs that were generated months ago, and being able to search over a long period of time, is great."
"The optimization and flexibility of visualization tools."
"The automatic update of the graphs from a dashboard is very convenient."
"It would be great if Databricks could integrate all the cloud platforms."
"There could be more support for automated machine learning in the database. I would like to see more ways to do analysis so that the reporting is more understandable."
"There is room for improvement in the documentation of processes and how it works."
"I would love an integration in my desktop IDE. For now, I have to code on their webpage."
"Pricing is one of the things that could be improved."
"Databricks has added some alerts and query functionality into their SQL persona, but the whole SQL persona, which is like a role, needs a lot of development. The alerts are not very flexible, and the query interface itself is not as polished as the notebook interface that is used through the data science and machine learning persona. It is clunky at present."
"Databricks has a lack of debuggers, and it would be good to see more components."
"I'm not the guy that I'm working with Databricks on a daily basis. I'm on the management team. However, my team tells me there are limitations with streaming events. The connectors work with a small set of platforms. For example, we can work with Kafka, but if we want to move to an event-driven solution from AWS, we cannot do it. We cannot connect to all the streaming analytics platforms, so we are limited in choosing the best one."
"Security could be improved thereby avoiding the necessity of a third party plugin."
"Having a kind of wizard that would help you when you are typing your search would make it easier and quicker to refine your search, and ultimately find what you are looking for."
"This solution should allow the user to combine two indices into one graph."
Earn 20 points
Databricks is ranked 1st in Data Science Platforms with 78 reviews while ELK Kibana doesn't meet the minimum requirements to be ranked in Data Science Platforms. Databricks is rated 8.2, while ELK Kibana is rated 7.2. The top reviewer of Databricks writes "A nice interface with good features for turning off clusters to save on computing". On the other hand, the top reviewer of ELK Kibana writes "Visualization tools are optimized providing us with increased flexibility". Databricks is most compared with Amazon SageMaker, Informatica PowerCenter, Dataiku Data Science Studio, Microsoft Azure Machine Learning Studio and Dremio, whereas ELK Kibana is most compared with Splunk Enterprise Security. See our Databricks vs. ELK Kibana report.
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