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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
6.6
Organizations experience mixed returns from Databricks, with benefits from cost savings and efficiency, but challenges in initial migration.
Sentiment score
5.8
Salesforce Einstein Analytics provides varied ROI experiences, with smaller companies and implementation stages influencing overall satisfaction and value.
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 customer service is generally effective with prompt responses, though some report issues mainly with third-party support channels.
Sentiment score
7.1
Salesforce Einstein Analytics' customer service is praised for effective support, professionalism, and satisfactory response time despite occasional delays.
As of now, we are raising issues and they are providing solutions without any problems.
Whenever we reach out, they respond promptly.
Tech support for Salesforce Einstein Analytics is generally good.
 

Scalability Issues

Sentiment score
7.4
Databricks is praised for efficient scalability and cloud compatibility, allowing easy resource adjustment across diverse projects and industries.
Sentiment score
7.7
Salesforce Einstein Analytics is highly scalable, integrating well and accommodating large user bases efficiently, despite licensing cost challenges.
The patches have sometimes caused issues leading to our jobs being paused for about six hours.
Databricks is an easily scalable platform.
 

Stability Issues

Sentiment score
7.6
Databricks is stable and efficient for large data, with minor issues during updates and occasional connectivity challenges.
Sentiment score
8.2
Salesforce Einstein Analytics is praised for stability and reliability, with minor issues resolved quickly and rated highly by users.
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.
Cluster failure is one of the biggest weaknesses I notice in our Databricks.
 

Room For Improvement

Databricks users desire improved UI, enhanced data visualization, better integration, clearer error messages, robust support, and comprehensive documentation.
Salesforce Einstein Analytics needs improved usability, affordability, and integration, with enhanced reporting, customization, and transparency features.
Adjusting features like worker nodes and node utilization during cluster creation could mitigate these failures.
It would be beneficial to have utilities where code snippets are readily available.
This feature, if made publicly available, may act as a game-changer, considering many global organizations use SAP data for their ERP requirements.
There is a learning curve associated with Salesforce Einstein Analytics, particularly since users need to learn a new language.
 

Setup Cost

Databricks pricing depends on usage, with flexibility in licensing, and can vary in competitiveness compared to other solutions.
Salesforce Einstein Analytics is subscription-based, perceived as costly, but value can be found in bundled pricing agreements.
It is not a cheap solution.
A benefit is that the pricing is available online, ensuring there are no hidden costs.
 

Valuable Features

Databricks provides a unified platform for data engineering, machine learning, seamless cloud integration, and robust data management capabilities.
Salesforce Einstein Analytics provides intuitive data handling, AI-powered insights, seamless integration, and scalable solutions for efficient decision-making and workflow management.
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.
The notebooks and the ability to share them with collaborators are valuable, as multiple developers can use a single cluster.
It allows for a personalized customer experience by providing insights.
 

Categories and Ranking

Databricks
Average Rating
8.2
Reviews Sentiment
7.0
Number of Reviews
89
Ranking in other categories
Cloud Data Warehouse (7th), Data Science Platforms (1st), Streaming Analytics (1st)
Salesforce Einstein Analytics
Average Rating
8.2
Reviews Sentiment
7.2
Number of Reviews
20
Ranking in other categories
BI (Business Intelligence) Tools (11th)
 

Mindshare comparison

While both are Business Intelligence solutions, they serve different purposes. Databricks is designed for Cloud Data Warehouse and holds a mindshare of 8.8%, up 3.7% compared to last year.
Salesforce Einstein Analytics, on the other hand, focuses on BI (Business Intelligence) Tools, holds 1.2% mindshare, down 1.8% since last year.
Cloud Data Warehouse
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.
Parker Goodson - PeerSpot reviewer
Helpful consistent measurements, high availability, and scales well
When it comes to generating reports, it appears that many users rely on experts to handle technical aspects. For instance, if you require a weekly report displaying accounts transitioning in and out of your modules, it seems challenging to accomplish without consulting experts for assistance. Such tasks should be user-friendly and easily achievable without external assistance. It would be beneficial to have visibility into any changes made to your account by individuals other than yourself or your team. It is also important to clearly track the history of these modifications and identify the responsible parties.
report
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Top Industries

By visitors reading reviews
Financial Services Firm
18%
Computer Software Company
10%
Manufacturing Company
9%
Healthcare Company
6%
Computer Software Company
18%
Financial Services Firm
13%
Manufacturing Company
8%
Healthcare Company
6%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
 

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?
Salesforce is working very rigorously on improvements with each release.
What is your primary use case for Salesforce Einstein Analytics?
I am a Salesforce CPQ developer. I have also worked on Einstein Analytics for reporting purposes.
 

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: April 2025.
849,686 professionals have used our research since 2012.