Apache Spark vs. IBM DataStage

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
Apache Spark
Score 8.7 out of 10
N/A
N/AN/A
IBM DataStage
Score 8.6 out of 10
N/A
IBM® DataStage® is a data integration tool that helps users to design, develop and run jobs that move and transform data. At its core, the DataStage tool supports extract, transform and load (ETL) and extract, load and transform (ELT) patterns. A basic version of the software is available for on-premises deployment, and the cloud-based DataStage for IBM Cloud Pak® for Data offers automated integration capabilities in a hybrid or multicloud environment.N/A
Pricing
Apache SparkIBM DataStage
Editions & Modules
No answers on this topic
No answers on this topic
Offerings
Pricing Offerings
Apache SparkIBM DataStage
Free Trial
NoYes
Free/Freemium Version
NoNo
Premium Consulting/Integration Services
NoNo
Entry-level Setup FeeNo setup feeNo setup fee
Additional Details
More Pricing Information
Community Pulse
Apache SparkIBM DataStage
Considered Both Products
Apache Spark
Chose Apache Spark
  • Apache Spark works in distributed mode using cluster
  • Informatica and Datastage cannot scale horizontally
  • We can write custom code in spark, whereas in Datastage and Informatica we can only choose the different features proivided already.
IBM DataStage

No answer on this topic

Top Pros
Top Cons
Features
Apache SparkIBM DataStage
Data Source Connection
Comparison of Data Source Connection features of Product A and Product B
Apache Spark
-
Ratings
IBM DataStage
9.2
9 Ratings
10% above category average
Connect to traditional data sources00 Ratings9.59 Ratings
Connecto to Big Data and NoSQL00 Ratings8.88 Ratings
Data Transformations
Comparison of Data Transformations features of Product A and Product B
Apache Spark
-
Ratings
IBM DataStage
9.5
9 Ratings
14% above category average
Simple transformations00 Ratings9.89 Ratings
Complex transformations00 Ratings9.39 Ratings
Data Modeling
Comparison of Data Modeling features of Product A and Product B
Apache Spark
-
Ratings
IBM DataStage
9.0
9 Ratings
11% above category average
Data model creation00 Ratings9.46 Ratings
Metadata management00 Ratings8.78 Ratings
Business rules and workflow00 Ratings8.18 Ratings
Collaboration00 Ratings9.09 Ratings
Testing and debugging00 Ratings9.59 Ratings
Data Governance
Comparison of Data Governance features of Product A and Product B
Apache Spark
-
Ratings
IBM DataStage
8.9
8 Ratings
7% above category average
Integration with data quality tools00 Ratings8.88 Ratings
Integration with MDM tools00 Ratings9.08 Ratings
Best Alternatives
Apache SparkIBM DataStage
Small Businesses

No answers on this topic

Skyvia
Skyvia
Score 9.7 out of 10
Medium-sized Companies
Cloudera Manager
Cloudera Manager
Score 9.7 out of 10
IBM InfoSphere Information Server
IBM InfoSphere Information Server
Score 8.1 out of 10
Enterprises
IBM Analytics Engine
IBM Analytics Engine
Score 8.8 out of 10
IBM InfoSphere Information Server
IBM InfoSphere Information Server
Score 8.1 out of 10
All AlternativesView all alternativesView all alternatives
User Ratings
Apache SparkIBM DataStage
Likelihood to Recommend
9.9
(24 ratings)
8.8
(9 ratings)
Likelihood to Renew
10.0
(1 ratings)
-
(0 ratings)
Usability
10.0
(3 ratings)
9.0
(2 ratings)
Performance
-
(0 ratings)
9.0
(1 ratings)
Support Rating
8.7
(4 ratings)
9.6
(3 ratings)
User Testimonials
Apache SparkIBM DataStage
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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IBM
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.
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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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IBM
  • Data movement
  • Seamless integration of scripts and etl jobs
  • Descriptive logging
  • Ability to work with myriad of data assets
  • Direct integration for Governance catalog
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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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IBM
  • 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.
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Likelihood to Renew
Apache
Capacity of computing data in cluster and fast speed.
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IBM
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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IBM
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.
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Performance
Apache
No answers on this topic
IBM
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.
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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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IBM
I believe that IBM generally has one of the worst and most complex assistance systems (physical and online) that exists.
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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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IBM
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.
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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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IBM
  • 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.
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