We performed a comparison between Databricks and Salesforce Einstein Analytics 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 load distribution capabilities are good, and you can perform data processing tasks very quickly."
"One of the features provides nice interactive clusters, or compute instances that you don't really need to manage often."
"Databricks gives you the flexibility of using several programming languages independently or in combination to build models."
"The fast data loading process and data storage capabilities are great."
"The integration with Python and the notebooks really helps."
"I like how easy it is to share your notebook with others. You can give people permission to read or edit. I think that's a great feature. You can also pull in code from GitHub pretty easily. I didn't use it that often, but I think that's a cool feature."
"The initial setup is pretty easy."
"It can send out large data amounts."
"The most valuable features of Salesforce Einstein Analytics are the flexibility around the deployment, overall capabilities, user-friendliness, and interactiveness with the tools that came built with it."
"Tableau CRM is a very capable solution. It is easy to use, user-friendly, and integrates well."
"We have found the scalability to be very good."
"Transparency is the most valuable feature of this solution."
"The way Salesforce Einstein Analytics is structured in terms of the work assignments and the user profile is very good."
"The solution scales extremely well."
"It is a comprehensive solution. It has everything in it. I can easily find what I need."
"Costs can quickly add up if you don't plan for it."
"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."
"Databricks is not geared towards the end-user, but rather it is for data engineers or data scientists."
"The integration of data could be a bit better."
"Databricks doesn't offer the use of Python scripts by itself and is not connected to GitHub repositories or anything similar. This is something that is missing. if they could integrate with Git tools it would be an advantage."
"There are no direct connectors — they are very limited."
"It's not easy to use, and they need a better UI."
"Databricks can improve by making the documentation better."
"If a user leaves your organization, you shouldn't lose the visibility of all of the records."
"They have a lot of opportunity to improve BI tools. With Einstein Analytics, we have a very minimal scope."
"If I could improve Tableau CRM I would make it as powerful as Tableau. Tableau CRM is more integrated and it's on the web, but it has less functionality. It is lacking functionality at this time."
"All of the timesheets and appraisal management I have not been happy with. There should be some improvements. For example, as a management team, our managers do not have access to see a summary report, such as who is on leave today or how many hours each person logged in. You have to get into individual users and see. You cannot see a summary report, the reporting is missing. There should improve the reports."
"I would advise others not to customize because they rolled out the newer versions, which are every six months. There had to be some significant testing and verification that happened. It is important to have a strong third-party provider that is very experienced. We used Deloitte, but we evaluated Accenture, KPMG, and IBM, but we decided on Deloitte and that was a good decision for us. Having a partner who has a center of excellence or experts that could give you a lot of the tips that they've learned could jumpstart your deployment and stick to the standards."
"There are some offerings like Sales Cloud, Service Cloud, and Marketing Cloud that have very useful online learning options. There need to be more avenues for self-learning with this particular solution. That would be useful."
"They can provide more end-user customizations. There should be the possibility for the end-users to change some elements in the interface. I have a way of doing my job. My colleague may have his own way of doing his job. If I ask for a change, it'll change for everyone. It'll be good to have some end-user personalizations. I can't see many in Salesforce right now."
"Better pricing would make it available to more users and we would likely use it more broadly within the organization."
Databricks is an industry-leading data analytics platform which is a one-stop product for all data requirements. Databricks is made by the creators of Apache Spark, Delta Lake, ML Flow, and Koalas. It builds on these technologies to deliver a true lakehouse data architecture, making it a robust platform that is reliable, scalable, and fast. Databricks speeds up innovations by synthesizing storage, engineering, business operations, security, and data science.
Databricks is integrated with Microsoft Azure, Amazon Web Services, and Google Cloud Platform. This enables users to easily manage a colossal amount of data and to continuously train and deploy machine learning models for AI applications. The platform handles all analytic deployments, ranging from ETL to models training and deployment.
Databricks deciphers the complexities of processing data to empower data scientists, engineers, and analysts with a simple collaborative environment to run interactive and scheduled data analysis workloads. The program takes advantage of AI’s cost-effectivity, flexibility, and cloud storage.
Databricks Key Features
Some of Databricks key features include:
Reviews from Real Users
Databricks stands out from its competitors for several reasons. Two striking features are its collaborative ability and its ability to streamline multiple programming languages.
PeerSpot users take note of the advantages of these features. A Chief Research Officer in consumer goods writes, “We work with multiple people on notebooks and it enables us to work collaboratively in an easy way without having to worry about the infrastructure. I think the solution is very intuitive, very easy to use. And that's what you pay for.”
A business intelligence coordinator in construction notes, “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.”
An Associate Manager who works in consultancy mentions, “The technology that allows us to write scripts within the solution is extremely beneficial. If I was, for example, able to script in SQL, R, Scala, Apache Spark, or Python, I would be able to use my knowledge to make a script in this solution. It is very user-friendly and you can also process the records and validation point of view. The ability to migrate from one environment to another is useful.”
Salesforce Einstein Analytics is a customer and business analytics platform that’s optimized for mobile use and brings flexible customer analytics to everyone in the company. It works with many types of data, from many data sources, and it can change the way your company answers critical questions. Einstein Analytics allows you to:
Databricks is ranked 1st in Data Science Platforms with 34 reviews while Salesforce Einstein Analytics is ranked 9th in BI (Business Intelligence) Tools with 8 reviews. Databricks is rated 8.2, while Salesforce Einstein Analytics is rated 8.2. The top reviewer of Databricks writes "Good integration with majority of data sources through Databricks Notebooks using Python, Scala, SQL, R". On the other hand, the top reviewer of Salesforce Einstein Analytics writes "Effective interactions, flexible, and easy to use". Databricks is most compared with Microsoft Azure Machine Learning Studio, Amazon SageMaker, Dataiku Data Science Studio, Azure Stream Analytics and Alteryx, whereas Salesforce Einstein Analytics is most compared with Microsoft BI, Tableau, IBM Watson Explorer, MicroStrategy and Qlik Sense.
We monitor all Data Science Platforms 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.