Apache Spark vs. Informatica MDM & 360 Applications

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
Apache Spark
Score 9.1 out of 10
N/A
N/AN/A
Informatica MDM & 360 Applications
Score 8.3 out of 10
N/A
Informatica MDM is an enterprise master data management solution that competes directly with IBM's InfoSphere and Oracle's Siebel UCM product.Informatica MDM and the company's 360 applications present a multidomain solution with flexibility to support any master data domain and relationship—whether on-premises, in the cloud, or both.N/A
Pricing
Apache SparkInformatica MDM & 360 Applications
Editions & Modules
No answers on this topic
No answers on this topic
Offerings
Pricing Offerings
Apache SparkInformatica MDM & 360 Applications
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
Community Pulse
Apache SparkInformatica MDM & 360 Applications
Best Alternatives
Apache SparkInformatica MDM & 360 Applications
Small Businesses

No answers on this topic

No answers on this topic

Medium-sized Companies
Cloudera Manager
Cloudera Manager
Score 9.9 out of 10
Informatica Customer 360 for Salesforce
Informatica Customer 360 for Salesforce
Score 7.6 out of 10
Enterprises
IBM Analytics Engine
IBM Analytics Engine
Score 8.4 out of 10
Winshuttle
Winshuttle
Score 8.1 out of 10
All AlternativesView all alternativesView all alternatives
User Ratings
Apache SparkInformatica MDM & 360 Applications
Likelihood to Recommend
9.2
(24 ratings)
8.9
(21 ratings)
Likelihood to Renew
10.0
(1 ratings)
9.0
(1 ratings)
Usability
8.3
(4 ratings)
9.0
(1 ratings)
Support Rating
8.7
(4 ratings)
9.0
(2 ratings)
Implementation Rating
-
(0 ratings)
8.0
(1 ratings)
User Testimonials
Apache SparkInformatica MDM & 360 Applications
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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Informatica
Informatica MDM is a complete MDM solution, from ingestion to data exposition. This tool helps us in gathering customer data, and also it makes it possible for us to support our customers relationships and build customer-related strategies to improve their experience which helps to drive sales geometry and growth and customers satisfaction. On the other hand of price is relatively competitive.
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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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Informatica
  • This program raises us to a professional level where we have better versatility to control all the media of my work and have a correct response for each scenario.
  • It is essential to be right about the destination and development of my data, Informatica MDM is here to simplify all these processes for its users.
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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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Informatica
  • It is unfortunate how this program has a couple of limitations in terms of insertions; it does not have the ability to agglomerate and archive the data in real-time by groups.
  • To have automation functions, the program is very limited in performing one task at a time, compared to other systems that perform functions simultaneously.
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Likelihood to Renew
Apache
Capacity of computing data in cluster and fast speed.
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Informatica
Supporting well in managing our huge customer base and managing the customer hierarchies well aligned with transactional processes
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Usability
Apache
If the team looking to use Apache Spark is not used to debug and tweak settings for jobs to ensure maximum optimizations, it can be frustrating. However, the documentation and the support of the community on the internet can help resolve most issues. Moreover, it is highly configurable and it integrates with different tools (eg: it can be used by dbt core), which increase the scenarios where it can be used
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Informatica
Strong Customer MDM capabilities for de-duplication, merging, golden record, exposing customer master data. Strong Integration capabilities
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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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Informatica
I'm not sure since I never used support. My colleagues never had any issues with it, therefore my rating would be an 8 with a certain range of uncertainty.
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Implementation Rating
Apache
No answers on this topic
Informatica
The integrator did a fair job and even though Business Change Management was complex, it was well concluded on time
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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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Informatica
Informatica MDM has proven it's worth in the organization by driving the revenue growth. It saves our lot of time by filtering out duplicate values and helps in solving critical business problems. It is very helpful when we deal with a lot of data. Apart from this we can populate data on various third party integration which is most useful case
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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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Informatica
  • I cannot speak to this for 2 reasons. 1. I am not privy to the financials associated with this implementation or the previous one. 2. We have not hit our 'go-live' for this implementation yet to compare it's performance to our previous solution.
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