Apache Spark vs. Arcadia Data

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
Apache Spark
Score 8.6 out of 10
N/A
N/AN/A
Arcadia Data
Score 9.3 out of 10
N/A
N/AN/A
Pricing
Apache SparkArcadia Data
Editions & Modules
No answers on this topic
No answers on this topic
Offerings
Pricing Offerings
Apache SparkArcadia Data
Free Trial
NoNo
Free/Freemium Version
NoNo
Premium Consulting/Integration Services
NoNo
Entry-level Setup FeeNo setup feeNo setup fee
Additional Details
More Pricing Information
Features
Apache SparkArcadia Data
BI Standard Reporting
Comparison of BI Standard Reporting features of Product A and Product B
Apache Spark
-
Ratings
Arcadia Data
9.2
3 Ratings
13% above category average
Pixel Perfect reports00 Ratings9.03 Ratings
Customizable dashboards00 Ratings9.03 Ratings
Report Formatting Templates00 Ratings9.73 Ratings
Ad-hoc Reporting
Comparison of Ad-hoc Reporting features of Product A and Product B
Apache Spark
-
Ratings
Arcadia Data
9.2
3 Ratings
14% above category average
Drill-down analysis00 Ratings9.33 Ratings
Formatting capabilities00 Ratings8.73 Ratings
Integration with R or other statistical packages00 Ratings9.33 Ratings
Report sharing and collaboration00 Ratings9.33 Ratings
Report Output and Scheduling
Comparison of Report Output and Scheduling features of Product A and Product B
Apache Spark
-
Ratings
Arcadia Data
8.7
3 Ratings
5% above category average
Publish to Web00 Ratings8.33 Ratings
Publish to PDF00 Ratings9.33 Ratings
Report Versioning00 Ratings9.03 Ratings
Report Delivery Scheduling00 Ratings8.03 Ratings
Delivery to Remote Servers00 Ratings9.03 Ratings
Data Discovery and Visualization
Comparison of Data Discovery and Visualization features of Product A and Product B
Apache Spark
-
Ratings
Arcadia Data
9.0
3 Ratings
11% above category average
Pre-built visualization formats (heatmaps, scatter plots etc.)00 Ratings8.73 Ratings
Location Analytics / Geographic Visualization00 Ratings9.03 Ratings
Predictive Analytics00 Ratings9.33 Ratings
Access Control and Security
Comparison of Access Control and Security features of Product A and Product B
Apache Spark
-
Ratings
Arcadia Data
8.8
3 Ratings
3% above category average
Multi-User Support (named login)00 Ratings8.73 Ratings
Role-Based Security Model00 Ratings9.33 Ratings
Multiple Access Permission Levels (Create, Read, Delete)00 Ratings9.03 Ratings
Single Sign-On (SSO)00 Ratings8.33 Ratings
Mobile Capabilities
Comparison of Mobile Capabilities features of Product A and Product B
Apache Spark
-
Ratings
Arcadia Data
9.0
3 Ratings
13% above category average
Responsive Design for Web Access00 Ratings9.03 Ratings
Mobile Application00 Ratings8.73 Ratings
Dashboard / Report / Visualization Interactivity on Mobile00 Ratings9.33 Ratings
Application Program Interfaces (APIs) / Embedding
Comparison of Application Program Interfaces (APIs) / Embedding features of Product A and Product B
Apache Spark
-
Ratings
Arcadia Data
9.2
3 Ratings
16% above category average
REST API00 Ratings9.33 Ratings
Javascript API00 Ratings9.03 Ratings
iFrames00 Ratings9.33 Ratings
Java API00 Ratings9.03 Ratings
Themeable User Interface (UI)00 Ratings9.03 Ratings
Customizable Platform (Open Source)00 Ratings9.33 Ratings
Best Alternatives
Apache SparkArcadia Data
Small Businesses

No answers on this topic

Cyfe
Cyfe
Score 8.8 out of 10
Medium-sized Companies
Cloudera Manager
Cloudera Manager
Score 9.7 out of 10
TIBCO Jaspersoft Community Edition
TIBCO Jaspersoft Community Edition
Score 9.6 out of 10
Enterprises
IBM Analytics Engine
IBM Analytics Engine
Score 9.3 out of 10
TIBCO Jaspersoft Community Edition
TIBCO Jaspersoft Community Edition
Score 9.6 out of 10
All AlternativesView all alternativesView all alternatives
User Ratings
Apache SparkArcadia Data
Likelihood to Recommend
9.7
(24 ratings)
9.3
(3 ratings)
Likelihood to Renew
10.0
(1 ratings)
-
(0 ratings)
Usability
10.0
(3 ratings)
9.3
(3 ratings)
Support Rating
8.6
(6 ratings)
9.3
(3 ratings)
User Testimonials
Apache SparkArcadia Data
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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Arcadia Data
It is suitable for companies without a proper data warehouse. He does very well in sales analysis and KPI management. It builds mini data warehouses, is good at data fusion, and interfaces well with other systems. Also, the export function and filter can greatly help you to get only the information you want in the format you want.
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Pros
Apache
  • Apache Spark makes processing very large data sets possible. It handles these data sets in a fairly quick manner.
  • Apache Spark does a fairly good job implementing machine learning models for larger data sets.
  • Apache Spark seems to be a rapidly advancing software, with the new features making the software ever more straight-forward to use.
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Arcadia Data
  • Coding is also simple and can be learned easily.
  • It is my favorite because it shows how mathematical models are used in real life.
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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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Arcadia Data
  • You have to make sure that the information thrown makes sense and is well organized.
  • There is a risk when saving information in the cloud from computer attacks
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Likelihood to Renew
Apache
Capacity of computing data in cluster and fast speed.
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Arcadia Data
No answers on this topic
Usability
Apache
The only thing I dislike about spark's usability is the learning curve, there are many actions and transformations, however, its wide-range of uses for ETL processing, facility to integrate and it's multi-language support make this library a powerhouse for your data science solutions. It has especially aided us with its lightning-fast processing times.
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Arcadia Data
We can easily provide the information that the user wants and customize it according to their needs. Sometimes a certain report can be used as the basis for creating another one that saves you time to deliver critical information in the shortest amount of time with the best results. Builds mini data warehouses, is good at data fusion, and interfaces well with other systems.
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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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Arcadia Data
I love how easy it is to create prototypes due to its simple simulation and modeling system. Other than that, the codes are usually simple and not very complex and it's built-in debugging adds to that ease. is an excellent tool for analyzing, classifying, and visualizing data. I do this most of the time to help me grab huge collections of data.
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Alternatives Considered
Apache
All the above systems work quite well on big data transformations whereas Spark really shines with its bigger API support and its ability to read from and write to multiple data sources. Using Spark one can easily switch between declarative versus imperative versus functional type programming easily based on the situation. Also it doesn't need special data ingestion or indexing pre-processing like Presto. Combining it with Jupyter Notebooks (https://github.com/jupyter-incubator/sparkmagic), one can develop the Spark code in an interactive manner in Scala or Python
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Arcadia Data
You can have a good reading of the data, you undoubtedly have cost savings and eliminate unnecessary and repetitive processes, we have unstructured data that, when structured, are elements of information that have become a competitive advantage for our organization, it is undoubtedly a strategic ally for the organization in the decision-making process
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Return on Investment
Apache
  • Faster turn around on feature development, we have seen a noticeable improvement in our agile development since using Spark.
  • Easy adoption, having multiple departments use the same underlying technology even if the use cases are very different allows for more commonality amongst applications which definitely makes the operations team happy.
  • Performance, we have been able to make some applications run over 20x faster since switching to Spark. This has saved us time, headaches, and operating costs.
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Arcadia Data
  • Download the report data only in Excel. Unable to download report formats such as colors, fonts, etc.
  • It does not support the presentation of images of our products as part of the analysis.
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