What users are saying about
147 Ratings
<a href='https://www.trustradius.com/static/about-trustradius-scoring' target='_blank' rel='nofollow noopener'>trScore algorithm: Learn more.</a>Score 8.7 out of 100
Based on 147 reviews and ratings
23 Ratings
<a href='https://www.trustradius.com/static/about-trustradius-scoring' target='_blank' rel='nofollow noopener'>trScore algorithm: Learn more.</a>Score 8 out of 100
Based on 23 reviews and ratings
Feature Set Ratings
Data Source Connection

Apache Spark
Feature Set Not Supported
N/A
8.7
IBM InfoSphere DataStage
87%
IBM InfoSphere DataStage ranks higher in 2/2 features
IBM InfoSphere DataStage ranks higher in 2/2 features
Connect to traditional data sources

N/A
0 Ratings
9.4
94%
8 Ratings
Connecto to Big Data and NoSQL

N/A
0 Ratings
7.9
79%
7 Ratings
Data Transformations

Apache Spark
Feature Set Not Supported
N/A
9.3
IBM InfoSphere DataStage
93%
IBM InfoSphere DataStage ranks higher in 2/2 features
IBM InfoSphere DataStage ranks higher in 2/2 features
Simple transformations

N/A
0 Ratings
9.7
97%
8 Ratings
Complex transformations

N/A
0 Ratings
9.0
90%
8 Ratings
Data Modeling

Apache Spark
Feature Set Not Supported
N/A
8.3
IBM InfoSphere DataStage
83%
IBM InfoSphere DataStage ranks higher in 6/6 features
IBM InfoSphere DataStage ranks higher in 6/6 features
Data model creation

N/A
0 Ratings
8.4
84%
5 Ratings
Metadata management

N/A
0 Ratings
7.7
77%
7 Ratings
Business rules and workflow

N/A
0 Ratings
7.5
75%
7 Ratings
Collaboration

N/A
0 Ratings
8.2
82%
8 Ratings
Testing and debugging

N/A
0 Ratings
9.3
93%
8 Ratings
feature 1

N/A
0 Ratings
8.7
87%
3 Ratings
Data Governance

Apache Spark
Feature Set Not Supported
N/A
8.3
IBM InfoSphere DataStage
83%
IBM InfoSphere DataStage ranks higher in 2/2 features
IBM InfoSphere DataStage ranks higher in 2/2 features
Integration with data quality tools

N/A
0 Ratings
8.4
84%
7 Ratings
Integration with MDM tools

N/A
0 Ratings
8.2
82%
7 Ratings
Attribute Ratings
- Apache Spark is rated higher in 2 areas: Likelihood to Recommend, Usability
- IBM InfoSphere DataStage is rated higher in 1 area: Support Rating
Likelihood to Recommend

9.2
Apache Spark
92%
22 Ratings
8.2
IBM InfoSphere DataStage
82%
8 Ratings
Likelihood to Renew

10.0
Apache Spark
100%
1 Rating
IBM InfoSphere DataStage
N/A
0 Ratings
Usability

9.4
Apache Spark
94%
2 Ratings
9.0
IBM InfoSphere DataStage
90%
2 Ratings
Performance

Apache Spark
N/A
0 Ratings
9.0
IBM InfoSphere DataStage
90%
2 Ratings
Support Rating

8.7
Apache Spark
87%
6 Ratings
8.9
IBM InfoSphere DataStage
89%
5 Ratings
Likelihood to Recommend
Apache Spark
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.
Owner, previous CEO
Econometric StudiosFinancial Services, 11-50 employees
IBM InfoSphere DataStage
Excellent Cloud data mapping tool and easy creating multiple project data analytics in real-time and the report distribution are excellent via this IBM product. Easy tool to provide data visualization and the integration is effective and helpful to migrating huge amounts of data across other platforms and different websites insights gathering.
Project Manager
KeywayReal Estate, 11-50 employees
Pros
Apache Spark
- 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
Software Engineer
LinkedInInternet, 5001-10,000 employees
IBM InfoSphere DataStage
- Data movement
- Seamless integration of scripts and etl jobs
- Descriptive logging
- Ability to work with myriad of data assets
- Direct integration for Governance catalog

Verified User
Manager in Information Technology
Insurance Company, 501-1000 employeesCons
Apache Spark
- 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
Data Czar
Envisagenics, Inc.Marketing and Advertising, 51-200 employees
IBM InfoSphere DataStage
- Connector Stages to Snowflake on the cloud. We had some issues initially but since then had been corrected.
- Accessing tool from a browser (zero foot-print). Currently we need to either install locally or connect to a server to do ETL work.
- Diversify ways of authenticating users.
Lead Developer
Office DepotRetail, 10,001+ employees
Pricing Details
Apache Spark
General
Free Trial
—Free/Freemium Version
—Premium Consulting/Integration Services
—Entry-level set up fee?
No
Starting Price
—IBM InfoSphere DataStage
General
Free Trial
—Free/Freemium Version
—Premium Consulting/Integration Services
—Entry-level set up fee?
No
Starting Price
—Likelihood to Renew
Apache Spark
Apache Spark 10.0
Based on 1 answer
Capacity of computing data in cluster and fast speed.
Senior Software Developer (Consultant)
Morgan StanleyBanking, 10,001+ employees
IBM InfoSphere DataStage
No score
No answers yet
No answers on this topic
Usability
Apache Spark
Apache Spark 9.4
Based on 2 answers
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.

Verified User
Engineer in Information Technology
Information Technology & Services Company, 11-50 employeesIBM InfoSphere DataStage
IBM InfoSphere DataStage 9.0
Based on 2 answers
Because it is robust, and it is being continuously improved.DS is one of the most used and recognized tools in the market. Large companies have implemented it in the first instance to develop their DW, but finding the advantages it has, they could use it for other types of projects such as migrations, application feeding, etc.

Verified User
Team Lead in Customer Service
Logistics and Supply Chain Company, 201-500 employeesPerformance
Apache Spark
No score
No answers yet
No answers on this topic
IBM InfoSphere DataStage
IBM InfoSphere DataStage 9.0
Based on 2 answers
It could load thousands of records in seconds. But in the Parallel version, you need to understand how to particionate the data. If you use the algorithms erroneously, or the functionalities that it gives for the parsing of data, the performance can fall drastically, even with few records.It is necessary to have people with experience to be able to determine which algorithm to use and understand why.

Verified User
Team Lead in Customer Service
Logistics and Supply Chain Company, 201-500 employeesSupport Rating
Apache Spark
Apache Spark 8.7
Based on 6 answers
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.
Technical Manager
Rishabh Software Private LimitedInformation Technology & Services, 501-1000 employees
IBM InfoSphere DataStage
IBM InfoSphere DataStage 8.9
Based on 5 answers
I believe that IBM generally has one of the worst and most complex assistance systems (physical and online) that exists.
Data Analyst | Data Developer - Advanced Analytics
Unieuro S.p.A.Retail, 1001-5000 employees
Alternatives Considered
Apache Spark
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.

Verified User
Engineer in Engineering
Computer Software Company, 51-200 employeesIBM InfoSphere DataStage
It's obvious since they both are from the same vendors and it makes it easier and can get better rates for licensing. Also, sales rapes are very helpful in case of escalations and critical issues.

Verified User
Administrator in Information Technology
Information Technology & Services Company, 10,001+ employeesReturn on Investment
Apache Spark
- 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
Senior Data Engineer
A.P. Moller - MaerskLogistics & Supply Chain, 10,001+ employees
IBM InfoSphere DataStage
- Reduce development time by 65% compared with hand coding.
- Reduces ETL process maintenance times.
- Better data governance for technical and non-technical people.
- Improve time to market for initiatives that require data integration.
Regional Product & Solution Architect Manager
GRUPO DATCOInformation Technology and Services, 501-1000 employees