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Databricks vs Salesforce Einstein Analytics comparison

 

Comparison Buyer's Guide

Executive Summary

Review summaries and opinions

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:
 

ROI

Sentiment score
6.5
Databricks efficiently lowers costs with cloud services, though ROI varies by sector and integration, particularly with Azure.
Sentiment score
6.2
Salesforce Einstein Analytics offers varied ROI, showing quick returns for some users, while others face challenges due to implementation or company size.
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.
 

Customer Service

Sentiment score
7.2
Databricks support is praised for prompt, professional service, comprehensive resources, and effective communication, enhancing overall user satisfaction.
Sentiment score
7.1
Salesforce Einstein Analytics offers effective customer service, quick responses, knowledgeable support, though some experience slow issue resolution.
As of now, we are raising issues and they are providing solutions without any problems.
Whenever we reach out, they respond promptly.
I rate the technical support as fine because they have levels of technical support available, especially partners who get really good support from Databricks on new features.
Tech support for Salesforce Einstein Analytics is generally good.
 

Scalability Issues

Sentiment score
7.4
Databricks is praised for its scalability, enabling easy adaptation to large data and user loads with efficient resource management.
Sentiment score
7.5
Salesforce Einstein Analytics is scalable, easily integrates, handles diverse user bases, and offers seamless expansion, despite cost concerns.
Databricks is an easily scalable platform.
I would rate the scalability of this solution as very high, about nine out of ten.
The patches have sometimes caused issues leading to our jobs being paused for about six hours.
 

Stability Issues

Sentiment score
7.7
Databricks is stable and robust, with minor issues, handling large data volumes and earning high stability ratings.
Sentiment score
7.9
Salesforce Einstein Analytics is praised for its stability and reliability, with occasional minor issues swiftly resolved.
Although it is too early to definitively state the platform's stability, we have not encountered any issues so far.
They release patches that sometimes break our code.
Databricks is definitely a very stable product and reliable.
There are certain glitches, especially when the modules are upgraded or when there is a source code update, causing the entire tool to go offline.
 

Room For Improvement

Databricks requires visualization improvements, pricing clarity, user-friendliness, expanded integrations, and simplification for non-technical users to enhance usability.
Salesforce Einstein Analytics needs improvements in support, data handling, user-friendliness, flexibility, and cost to boost adoption.
We could use their job clusters, however, that increases costs, which is challenging for us as a startup.
This feature, if made publicly available, may act as a game-changer, considering many global organizations use SAP data for their ERP requirements.
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.
There are certain glitches, especially when the modules are upgraded or when there is a source code update, causing the entire tool to go offline.
There is a learning curve associated with Salesforce Einstein Analytics, particularly since users need to learn a new language.
 

Setup Cost

Enterprise buyers view Databricks as moderately pricey, with high setup costs, though discounts and licensing flexibility are available.
Salesforce Einstein Analytics is costly yet valued for features, with negotiating licenses helping reduce expenses in enterprise environments.
It is not a cheap solution.
A benefit is that the pricing is available online, ensuring there are no hidden costs.
In general, I would rate it as a little bit on the expensive side compared to other available options.
 

Valuable Features

Databricks excels in scalability, integration, and user-friendly features, making it ideal for data processing and AI across industries.
Salesforce Einstein Analytics offers user-friendly, scalable analytics with predictive insights and seamless CRM integration for enhanced decision-making and productivity.
The Unity Catalog is for data governance, and the Delta Lake is to build the lakehouse.
The platform allows us to leverage cloud advantages effectively, enhancing our AI and ML projects.
Databricks' capability to process data in parallel enhances data processing speed.
It allows for a personalized customer experience by providing insights.
Their machine learning model, which they have integrated, provides us with accurate data and creates projection maps.
 

Categories and Ranking

Databricks
Average Rating
8.2
Reviews Sentiment
7.0
Number of Reviews
91
Ranking in other categories
Cloud Data Warehouse (9th), Data Science Platforms (1st), Streaming Analytics (1st)
Salesforce Einstein Analytics
Average Rating
8.0
Reviews Sentiment
7.2
Number of Reviews
21
Ranking in other categories
BI (Business Intelligence) Tools (12th)
 

Mindshare comparison

While both are Business Intelligence solutions, they serve different purposes. Databricks is designed for Cloud Data Warehouse and holds a mindshare of 8.3%, up 5.6% compared to last year.
Salesforce Einstein Analytics, on the other hand, focuses on BI (Business Intelligence) Tools, holds 1.0% mindshare, down 1.5% since last year.
Cloud Data Warehouse Market Share Distribution
ProductMarket Share (%)
Databricks8.3%
Snowflake17.7%
Dremio9.4%
Other64.6%
Cloud Data Warehouse
BI (Business Intelligence) Tools Market Share Distribution
ProductMarket Share (%)
Salesforce Einstein Analytics1.0%
Microsoft Power BI14.5%
Tableau Enterprise11.0%
Other73.5%
BI (Business Intelligence) Tools
 

Featured Reviews

ShubhamSharma7 - PeerSpot reviewer
Capability to integrate diverse coding languages in a single notebook greatly enhances workflow
Databricks offers various courses that I can use, whether it's PySpark, Scala, or R. I can leverage all these courses in a single notebook, which is beneficial for clients as they can access various tools in one place whenever needed. This is quite significant. I usually work with PySpark based on client requirements. After coding, I feed the Databricks notebooks into the ADF pipeline for updates. Databricks' capability to process data in parallel enhances data processing speed. Furthermore, I can connect our Databricks notebook directly with Power BI and other visualization tools like Qlik. Once we develop code, it allows us to transform raw data into visualizations for clients using analysis diagrams, which is very helpful.
Sunny Nair - PeerSpot reviewer
Business insights improve with reliable integration but face occasional system downtimes
It is difficult to integrate the modules. The customization is also a little bit complex currently, but it is getting easier as they continue improving the market. There are certain glitches, especially when the modules are upgraded or when there is a source code update, causing the entire tool to go offline. There is downtime which still affects the particular product. I would provide a reliability rating of 70%, with 30% still having scope for improvement.
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Top Industries

By visitors reading reviews
Financial Services Firm
17%
Computer Software Company
10%
Manufacturing Company
9%
Healthcare Company
6%
Financial Services Firm
14%
Computer Software Company
14%
Manufacturing Company
9%
University
5%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
By reviewers
Company SizeCount
Small Business25
Midsize Enterprise12
Large Enterprise56
By reviewers
Company SizeCount
Small Business7
Midsize Enterprise4
Large Enterprise12
 

Questions from the Community

Which do you prefer - Databricks or Azure Machine Learning Studio?
Databricks gives you the option of working with several different languages, such as SQL, R, Scala, Apache Spark, or Python. It offers many different cluster choices and excellent integration with ...
How would you compare Databricks vs Amazon SageMaker?
We researched AWS SageMaker, but in the end, we chose Databricks. Databricks is a Unified Analytics Platform designed to accelerate innovation projects. It is based on Spark so it is very fast. It...
Which would you choose - Databricks or Azure Stream Analytics?
Databricks is an easy-to-set-up and versatile tool for data management, analysis, and business analytics. For analytics teams that have to interpret data to further the business goals of their orga...
What do you like most about Salesforce Einstein Analytics?
The tool is valuable. It's one of the greatest programs I'm currently working with, and I believe it will continue to be crucial in the next four to five years. It's the future of our operations. I...
What needs improvement with Salesforce Einstein Analytics?
It is difficult to integrate the modules. The customization is also a little bit complex currently, but it is getting easier as they continue improving the market. There are certain glitches, espec...
What is your primary use case for Salesforce Einstein Analytics?
It is more of an integrated platform where marketing, customer service, and the ticketing system are integrated into each other as a SaaS-based solution, which is what we use. For Marketing Cloud, ...
 

Also Known As

Databricks Unified Analytics, Databricks Unified Analytics Platform, Redash
Einstein Analytics, Salesforce Wave Analytics
 

Overview

 

Sample Customers

Elsevier, MyFitnessPal, Sharethrough, Automatic Labs, Celtra, Radius Intelligence, Yesware
ADS Securities, Alstom Grid, American Express, Barclays Bank, Coca-Cola, CoderDojo, Dubai Multi Commodities Centre, Financial Conduct Authority
Find out what your peers are saying about Snowflake Computing, Microsoft, Google and others in Cloud Data Warehouse. Updated: August 2025.
867,497 professionals have used our research since 2012.