Apache Spark vs. SAS Data Management

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
Apache Spark
Score 8.7 out of 10
N/A
N/AN/A
SAS Data Management
Score 8.0 out of 10
N/A
A suite of solutions for data connectivity, enhanced transformations and robust governance. Solutions provide a unified view of data with access to data across databases, data warehouses and data lakes. Connects with cloud platforms, on-premises systems and multicloud data sources.N/A
Pricing
Apache SparkSAS Data Management
Editions & Modules
No answers on this topic
No answers on this topic
Offerings
Pricing Offerings
Apache SparkSAS Data Management
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 Data Management
Data Source Connection
Comparison of Data Source Connection features of Product A and Product B
Apache Spark
-
Ratings
SAS Data Management
8.3
10 Ratings
1% above category average
Connect to traditional data sources00 Ratings8.610 Ratings
Connecto to Big Data and NoSQL00 Ratings8.19 Ratings
Data Transformations
Comparison of Data Transformations features of Product A and Product B
Apache Spark
-
Ratings
SAS Data Management
6.7
8 Ratings
22% below category average
Simple transformations00 Ratings6.18 Ratings
Complex transformations00 Ratings7.48 Ratings
Data Modeling
Comparison of Data Modeling features of Product A and Product B
Apache Spark
-
Ratings
SAS Data Management
6.7
8 Ratings
20% below category average
Data model creation00 Ratings5.56 Ratings
Metadata management00 Ratings7.47 Ratings
Business rules and workflow00 Ratings6.67 Ratings
Collaboration00 Ratings7.07 Ratings
Testing and debugging00 Ratings6.17 Ratings
Data Governance
Comparison of Data Governance features of Product A and Product B
Apache Spark
-
Ratings
SAS Data Management
7.9
9 Ratings
4% below category average
Integration with data quality tools00 Ratings7.69 Ratings
Integration with MDM tools00 Ratings8.27 Ratings
Best Alternatives
Apache SparkSAS Data Management
Small Businesses

No answers on this topic

Skyvia
Skyvia
Score 9.6 out of 10
Medium-sized Companies
Cloudera Manager
Cloudera Manager
Score 9.7 out of 10
InfoSphere
InfoSphere
Score 8.2 out of 10
Enterprises
IBM Analytics Engine
IBM Analytics Engine
Score 9.3 out of 10
InfoSphere
InfoSphere
Score 8.2 out of 10
All AlternativesView all alternativesView all alternatives
User Ratings
Apache SparkSAS Data Management
Likelihood to Recommend
9.9
(24 ratings)
7.6
(11 ratings)
Likelihood to Renew
10.0
(1 ratings)
9.0
(2 ratings)
Usability
10.0
(3 ratings)
6.0
(2 ratings)
Performance
-
(0 ratings)
9.0
(1 ratings)
Support Rating
8.7
(4 ratings)
7.7
(6 ratings)
User Testimonials
Apache SparkSAS Data Management
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.
Read full review
SAS
When data is in a system that needs a complex transformation to be usable for an average user. Such tasks as data residing in systems that have very different connection speeds. It can be integrated and used together after passing through the SAS Data Integration Studio removing timing issues from the users' worries. A part that is perhaps less appropriate is getting users who are not familiar with the source data to set up the load processes.
Read full review
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.
Read full review
SAS
  • SAS/Access is great for manipulating large and complex databases.
  • SAS/Access makes it easy to format reports and graphics from your data.
  • Data Management and data storage using the Hadoop environment in SAS/Access allows for rapid analysis and simple programming language for all your data needs.
Read full review
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
Read full review
SAS
  • Requires third-party drivers to connect to common data sources like SFDC, MS SQL, Postgres.
  • Debugging errors from the logs is a complicated process.
  • E-mail alert system is very primitive and needs customization to make it more modern,
  • Cannot send SMS alerts for jobs.
Read full review
Likelihood to Renew
Apache
Capacity of computing data in cluster and fast speed.
Read full review
SAS
We are happy with the software and its functionality. As a SAS-shop, DataFlux is a logical choice for complex data integration.
Read full review
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
The main negative point is the use of a non-standard language for customizations, as well as the poor integration with non-SAS systems. However, there is no doubt that it is a high-performance and powerful product capable of responding optimally to certain requirements.
Read full review
Performance
Apache
No answers on this topic
SAS
It worked as expected.
Read full review
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.
Read full review
SAS
With SAS, you pay a license fee annually to use this product. Support is incredible. You get what you pay for, whether it's SAS forums on the SAS support site, technical support tickets via email or phone calls, or example documentation. It's not open source. It's documented thoroughly, and it works.
Read full review
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
Read full review
SAS
Because of ease of using SAS DI and data processing speed. There were lots of issues with AWS Redshift on cloud environment in terms of making connections with the data sources and while fetching the data we need to write complex queries.
Read full review
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.
Read full review
SAS
  • We have more users who can connect to the many different data sources.
  • Our users do have existing SAS programming knowledge and that can carry over.
  • Business functions are starting to rely on SAS Data Integration Studio work product shortly after introduction.
Read full review
ScreenShots