Apache Spark vs. SAS/Access

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
Apache Spark
Score 8.6 out of 10
N/A
N/AN/A
SAS/Access
Score 8.0 out of 10
N/A
SAS/Access is a data integration solution, from SAS.N/A
Pricing
Apache SparkSAS/Access
Editions & Modules
No answers on this topic
No answers on this topic
Offerings
Pricing Offerings
Apache SparkSAS/Access
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 SparkSAS/Access
Data Source Connection
Comparison of Data Source Connection features of Product A and Product B
Apache Spark
-
Ratings
SAS/Access
9.7
5 Ratings
15% above category average
Connect to traditional data sources00 Ratings10.05 Ratings
Connecto to Big Data and NoSQL00 Ratings9.54 Ratings
Data Transformations
Comparison of Data Transformations features of Product A and Product B
Apache Spark
-
Ratings
SAS/Access
8.5
3 Ratings
1% above category average
Simple transformations00 Ratings9.03 Ratings
Complex transformations00 Ratings8.03 Ratings
Data Modeling
Comparison of Data Modeling features of Product A and Product B
Apache Spark
-
Ratings
SAS/Access
8.8
3 Ratings
9% above category average
Data model creation00 Ratings9.02 Ratings
Metadata management00 Ratings9.02 Ratings
Business rules and workflow00 Ratings9.02 Ratings
Collaboration00 Ratings9.02 Ratings
Testing and debugging00 Ratings8.02 Ratings
Data Governance
Comparison of Data Governance features of Product A and Product B
Apache Spark
-
Ratings
SAS/Access
9.7
4 Ratings
17% above category average
Integration with data quality tools00 Ratings9.54 Ratings
Integration with MDM tools00 Ratings10.03 Ratings
Best Alternatives
Apache SparkSAS/Access
Small Businesses

No answers on this topic

Dataloader.io
Dataloader.io
Score 8.3 out of 10
Medium-sized Companies
Cloudera Manager
Cloudera Manager
Score 9.7 out of 10
InfoSphere
InfoSphere
Score 10.0 out of 10
Enterprises
IBM Analytics Engine
IBM Analytics Engine
Score 9.1 out of 10
InfoSphere
InfoSphere
Score 10.0 out of 10
All AlternativesView all alternativesView all alternatives
User Ratings
Apache SparkSAS/Access
Likelihood to Recommend
9.4
(23 ratings)
10.0
(5 ratings)
Likelihood to Renew
10.0
(1 ratings)
-
(0 ratings)
Usability
9.4
(2 ratings)
-
(0 ratings)
Support Rating
8.6
(6 ratings)
10.0
(4 ratings)
User Testimonials
Apache SparkSAS/Access
Likelihood to Recommend
Apache
The software appears to run more efficiently than other big data tools, such as Hadoop. Given that, Apache Spark is well-suited for querying and trying to make sense of very, very large data sets. The software offers many advanced machine learning and econometrics tools, although these tools are used only partially because very large data sets require too much time when the data sets get too large. The software is not well-suited for projects that are not big data in size. The graphics and analytical output are subpar compared to other tools.
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SAS
SAS/Access is well suited for companies who need to manipulate and analyze large databases and data-sets. It does the same thing as SQL, and if you already know basic SAS coding it is easier to pick up. SAS/Access works well with analyzing data from multiple data-sources at once, including large databases stored in external and virtual environments like Hadoop. Data can be easily reassembled from relational databases for use by the user. SAS/Access is not necessary if you are only pulling data from one database that you have the physical file for.
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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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SAS
  • Secure connections to databases
  • Managed access to databases and data
  • Customized access to databases
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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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SAS
  • Syntax for the connection string can be tricky
  • Specification of the data source driver can be tricky
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Likelihood to Renew
Apache
Capacity of computing data in cluster and fast speed.
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SAS
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.
Read full review
SAS
No answers on this topic
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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SAS
I have always received excelled service working with SAS technical support. Additionally, the tool is used so widely that there are many online resources and use cases that allow you to see many other uses or support routes.
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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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SAS
Datastage might be the closest one. Being a full ETL tool, it's weird to compare both. Datastage might be more robust for extraction but it lacks the simplicity that the end users need for everyday data extract and analysis.
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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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SAS
  • Bring usable data together quickly for downstream consumption e.g. modelling and reporting.
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