Apache Spark vs. Microsoft Power BI

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
Apache Spark
Score 9.2 out of 10
N/A
N/AN/A
Microsoft Power BI
Score 8.5 out of 10
N/A
Microsoft Power BI is a visualization and data discovery tool from Microsoft. It allows users to convert data into visuals and graphics, visually explore and analyze data, collaborate on interactive dashboards and reports, and scale across their organization with built-in governance and security.
$10
per month per user
Pricing
Apache SparkMicrosoft Power BI
Editions & Modules
No answers on this topic
No answers on this topic
Offerings
Pricing Offerings
Apache SparkMicrosoft Power BI
Free Trial
NoYes
Free/Freemium Version
NoYes
Premium Consulting/Integration Services
NoNo
Entry-level Setup FeeNo setup feeNo setup fee
Additional Details
More Pricing Information
Community Pulse
Apache SparkMicrosoft Power BI
Features
Apache SparkMicrosoft Power BI
BI Standard Reporting
Comparison of BI Standard Reporting features of Product A and Product B
Apache Spark
-
Ratings
Microsoft Power BI
8.4
193 Ratings
3% above category average
Pixel Perfect reports00 Ratings8.3164 Ratings
Customizable dashboards00 Ratings8.8192 Ratings
Report Formatting Templates00 Ratings8.0175 Ratings
Ad-hoc Reporting
Comparison of Ad-hoc Reporting features of Product A and Product B
Apache Spark
-
Ratings
Microsoft Power BI
7.9
191 Ratings
2% below category average
Drill-down analysis00 Ratings8.2188 Ratings
Formatting capabilities00 Ratings7.7188 Ratings
Integration with R or other statistical packages00 Ratings7.3140 Ratings
Report sharing and collaboration00 Ratings8.5186 Ratings
Report Output and Scheduling
Comparison of Report Output and Scheduling features of Product A and Product B
Apache Spark
-
Ratings
Microsoft Power BI
8.1
184 Ratings
2% below category average
Publish to Web00 Ratings8.3174 Ratings
Publish to PDF00 Ratings8.2169 Ratings
Report Versioning00 Ratings7.7141 Ratings
Report Delivery Scheduling00 Ratings8.3144 Ratings
Delivery to Remote Servers00 Ratings7.9107 Ratings
Data Discovery and Visualization
Comparison of Data Discovery and Visualization features of Product A and Product B
Apache Spark
-
Ratings
Microsoft Power BI
7.8
184 Ratings
2% below category average
Pre-built visualization formats (heatmaps, scatter plots etc.)00 Ratings8.2178 Ratings
Location Analytics / Geographic Visualization00 Ratings8.1163 Ratings
Predictive Analytics00 Ratings7.4133 Ratings
Pattern Recognition and Data Mining00 Ratings7.434 Ratings
Access Control and Security
Comparison of Access Control and Security features of Product A and Product B
Apache Spark
-
Ratings
Microsoft Power BI
8.6
175 Ratings
1% above category average
Multi-User Support (named login)00 Ratings8.8165 Ratings
Role-Based Security Model00 Ratings8.5143 Ratings
Multiple Access Permission Levels (Create, Read, Delete)00 Ratings8.4155 Ratings
Report-Level Access Control00 Ratings8.444 Ratings
Single Sign-On (SSO)00 Ratings8.7137 Ratings
Mobile Capabilities
Comparison of Mobile Capabilities features of Product A and Product B
Apache Spark
-
Ratings
Microsoft Power BI
8.0
157 Ratings
2% above category average
Responsive Design for Web Access00 Ratings7.8147 Ratings
Mobile Application00 Ratings7.7128 Ratings
Dashboard / Report / Visualization Interactivity on Mobile00 Ratings8.0150 Ratings
Application Program Interfaces (APIs) / Embedding
Comparison of Application Program Interfaces (APIs) / Embedding features of Product A and Product B
Apache Spark
-
Ratings
Microsoft Power BI
7.8
113 Ratings
1% above category average
REST API00 Ratings8.2101 Ratings
Javascript API00 Ratings7.682 Ratings
iFrames00 Ratings8.055 Ratings
Java API00 Ratings6.967 Ratings
Themeable User Interface (UI)00 Ratings7.588 Ratings
Customizable Platform (Open Source)00 Ratings8.444 Ratings
Best Alternatives
Apache SparkMicrosoft Power BI
Small Businesses

No answers on this topic

BrightGauge
BrightGauge
Score 8.9 out of 10
Medium-sized Companies
Cloudera Manager
Cloudera Manager
Score 9.9 out of 10
Reveal
Reveal
Score 10.0 out of 10
Enterprises
IBM Analytics Engine
IBM Analytics Engine
Score 8.4 out of 10
Kyvos Insights
Kyvos Insights
Score 9.5 out of 10
All AlternativesView all alternativesView all alternatives
User Ratings
Apache SparkMicrosoft Power BI
Likelihood to Recommend
9.1
(24 ratings)
8.5
(194 ratings)
Likelihood to Renew
10.0
(1 ratings)
9.5
(3 ratings)
Usability
8.3
(4 ratings)
8.3
(111 ratings)
Support Rating
8.7
(4 ratings)
9.9
(52 ratings)
Implementation Rating
-
(0 ratings)
9.0
(1 ratings)
User Testimonials
Apache SparkMicrosoft Power BI
Likelihood to Recommend
Apache
Well suited: To most of the local run of datasets and non-prod systems - scalability is not a problem at all. Including data from multiple types of data sources is an added advantage. MLlib is a decently nice built-in library that can be used for most of the ML tasks. Less appropriate: We had to work on a RecSys where the music dataset that we used was around 300+Gb in size. We faced memory-based issues. Few times we also got memory errors. Also the MLlib library does not have support for advanced analytics and deep-learning frameworks support. Understanding the internals of the working of Apache Spark for beginners is highly not possible.
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Microsoft
Has significantly improved collation of data and visualisation especially with business across Europe. Has given me the ability to see the Site availability at the click of a button to see which Site is in the "money" and seize opportunities based on Market data
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Pros
Apache
  • Rich APIs for data transformation making for very each to transform and prepare data in a distributed environment without worrying about memory issues
  • Faster in execution times compare to Hadoop and PIG Latin
  • Easy SQL interface to the same data set for people who are comfortable to explore data in a declarative manner
  • Interoperability between SQL and Scala / Python style of munging data
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Microsoft
  • Options for data source connections are immense. Not just which sources, but your options for *how* the data is brought in.
  • Constant updates (this is both good and bad at times).
  • User friendliness. I can get the data connections set up and draft some quick visuals, then release to the target audience and let them expand on it how they want to.
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Cons
Apache
  • Memory management. Very weak on that.
  • PySpark not as robust as scala with spark.
  • spark master HA is needed. Not as HA as it should be.
  • Locality should not be a necessity, but does help improvement. But would prefer no locality
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Microsoft
  • It would be easier for users could Microsoft Power BI and Excel used the same programming languages.
  • Would like to see the online version of Microsoft Power BI be as powerful as the desktop version.
  • Publishing a Microsoft Power BI file online and then having to save the file is somewhat redundant.
  • Would like to export each page or chart as an image.
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Likelihood to Renew
Apache
Capacity of computing data in cluster and fast speed.
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Microsoft
Microsoft Power BI is an excellent and scalable tool. It has a learning curve, but once you get past that, the sky is the limit and you can build from the most simple to the most complex dashboards. I have built everything from simple reports with only a few data points to complex reports with many pages and advanced filtering.
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Usability
Apache
If the team looking to use Apache Spark is not used to debug and tweak settings for jobs to ensure maximum optimizations, it can be frustrating. However, the documentation and the support of the community on the internet can help resolve most issues. Moreover, it is highly configurable and it integrates with different tools (eg: it can be used by dbt core), which increase the scenarios where it can be used
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Microsoft
Automating reporting has reduced manual data processing by 50-70%, freeing up analysts for higher-value tasks. A finance team that previously spent 20+ hours per week on Excel-based reports now does it in minutes with Microsoft Power BI's automated Real-time dashboards have shortened decision cycles by 30-40%, enabling leadership to react quickly to sales trends, operational bottlenecks, and customer behavior.
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Support Rating
Apache
1. It integrates very well with scala or python. 2. It's very easy to understand SQL interoperability. 3. Apache is way faster than the other competitive technologies. 4. The support from the Apache community is very huge for Spark. 5. Execution times are faster as compared to others. 6. There are a large number of forums available for Apache Spark. 7. The code availability for Apache Spark is simpler and easy to gain access to. 8. Many organizations use Apache Spark, so many solutions are available for existing applications.
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Microsoft
It is a fantastic tool, you can do almost everything related with data and reports, it is a perfect substitutive of Power Point and Excel with a high evolution and flexibility, and also it is very friendly and easy to share. I think all companies should have Power BI (or other BI tool) in their software package and if they are in the MS Suite, for sure Power BI should be the one due to all the benefits of the MS ecosystem.
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Implementation Rating
Apache
No answers on this topic
Microsoft
It was integrated with our erp easily and was accessible on cloud.
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Alternatives Considered
Apache
Spark in comparison to similar technologies ends up being a one stop shop. You can achieve so much with this one framework instead of having to stitch and weave multiple technologies from the Hadoop stack, all while getting incredibility performance, minimal boilerplate, and getting the ability to write your application in the language of your choosing.
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Microsoft
Microsoft Power BI is free. If I didn't want to create a custom platform (i.e. my organization insisted on an existing platform that I *had* to use), I'd use Microsoft Power BI. For any start-up or SMB, I'd just use Claude & Grok to build it quickly, also for free. Would not pay for Tableau or Sigma anymore. Not worth it at all.
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Return on Investment
Apache
  • Business leaders are able to take data driven decisions
  • Business users are able access to data in near real time now . Before using spark, they had to wait for at least 24 hours for data to be available
  • Business is able come up with new product ideas
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Microsoft
  • Power BI usage reduced the effort of analytical reports creation by about 80%
  • Empowered all the level of employee to be more vigilant of the data and business insights, gained the profit of 8% overall.
  • AI-powered predictive analytics improved forecasting accuracy by 17%, that topped the overall sales.
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ScreenShots

Microsoft Power BI Screenshots

Screenshot of Microsoft Power BI - Turns insights into impact for business usersScreenshot of Power BI integrates easily with Microsoft 365Screenshot of Microsoft Power BI - AI-Powered CapabilitiesScreenshot of Microsoft Power BI - Copilot can be used to create reportsScreenshot of Microsoft Power BI - Data HubScreenshot of Microsoft Power BI - Scales as organizational needs grow