Apache Spark vs. Informatica PowerCenter

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
Apache Spark
Score 8.7 out of 10
N/A
N/AN/A
Informatica PowerCenter
Score 7.7 out of 10
N/A
Informatica PowerCenter is a metadata driven data integration technology designed to form the foundation for data integration initiatives, including analytics and data warehousing, application migration, or consolidation and data governance.N/A
Pricing
Apache SparkInformatica PowerCenter
Editions & Modules
No answers on this topic
No answers on this topic
Offerings
Pricing Offerings
Apache SparkInformatica PowerCenter
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 PowerCenter
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.
Informatica PowerCenter

No answer on this topic

Top Pros
Top Cons
Features
Apache SparkInformatica PowerCenter
Data Source Connection
Comparison of Data Source Connection features of Product A and Product B
Apache Spark
-
Ratings
Informatica PowerCenter
8.5
18 Ratings
2% above category average
Connect to traditional data sources00 Ratings9.018 Ratings
Connecto to Big Data and NoSQL00 Ratings8.014 Ratings
Data Transformations
Comparison of Data Transformations features of Product A and Product B
Apache Spark
-
Ratings
Informatica PowerCenter
7.5
18 Ratings
10% below category average
Simple transformations00 Ratings8.018 Ratings
Complex transformations00 Ratings7.018 Ratings
Data Modeling
Comparison of Data Modeling features of Product A and Product B
Apache Spark
-
Ratings
Informatica PowerCenter
8.2
18 Ratings
2% above category average
Data model creation00 Ratings9.015 Ratings
Metadata management00 Ratings8.016 Ratings
Business rules and workflow00 Ratings9.018 Ratings
Collaboration00 Ratings6.116 Ratings
Testing and debugging00 Ratings9.017 Ratings
Data Governance
Comparison of Data Governance features of Product A and Product B
Apache Spark
-
Ratings
Informatica PowerCenter
9.0
15 Ratings
8% above category average
Integration with data quality tools00 Ratings9.015 Ratings
Integration with MDM tools00 Ratings9.013 Ratings
Best Alternatives
Apache SparkInformatica PowerCenter
Small Businesses

No answers on this topic

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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 SparkInformatica PowerCenter
Likelihood to Recommend
9.9
(24 ratings)
8.0
(21 ratings)
Likelihood to Renew
10.0
(1 ratings)
10.0
(4 ratings)
Usability
10.0
(3 ratings)
9.0
(3 ratings)
Performance
-
(0 ratings)
9.4
(2 ratings)
Support Rating
8.7
(4 ratings)
9.0
(2 ratings)
User Testimonials
Apache SparkInformatica PowerCenter
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
1.- Scenaries with poor sources of data is not recomended (Very bad ROI). The solution is for medium-big enterprises with a lot of sources of data and users. 2.- Bank and finance enviroment to integrate differente data form trading, Regulatory reports, decisions makers, fraud and financial crimes because in this kind of scenary the quality of data is the base of the business. 3.- Departments of development and test of applications in enterprises because you can design enviroments, out of the production systems, to development and test the new API's or updateds made.
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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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Informatica
  • Informatica Powercenter is an innovative software that works with ETL-type data integration. Connectivity to almost all the database systems.
  • Great documentation and customer support.
  • It has a various solution to address data quality issues. data masking, data virtualization. It has various supporting tools or MDM, IDQ, Analyst, BigData which can be used to analyze data and correct it.
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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
  • There are too many ways to perform the same or similar functions which in turn makes it challenging to trace what a workflow is doing and at which point (ex. sessions can be designed as static or re-usable and the override can occur at the session or workflow, or both which can be counter productive and confusing when troubleshooting).
  • The power in structured design is a double edged sword. Simple tasks for a POC can become cumbersome. Ex. if you want to move some data to test a process, you first have to create your sources by importing them which means an ODBC connection or similar will need to be configured, you in turn have to develop your targets and all of the essential building blocks before being able to begin actual development. While I am on sources and targets, I think of a table definition as just that and find it counter intuitive to have to design a table as both a source and target and manage them as different objects. It would be more intuitive to have a table definition and its source/target properties defined by where you drag and drop it in the mapping.
  • There are no checkpoints or data viewer type functions without designing an entire mapping and workflow. If you would like to simply run a job up to a point and check the throughput, an entire mapping needs to be completed and you would workaround this by creating a flat file target.
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Likelihood to Renew
Apache
Capacity of computing data in cluster and fast speed.
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Informatica
Our team enjoys using Informatica and feels that it is one of the best ETL tools on the market.
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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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Informatica
Positives; - Multi User Development Environment - Speed of transformation - Seamless integration between other Informatica products. Negatives; - There should be less windows to maintain developers' focus while using. You probably need 2 big monitors when you start development with Informatica Power Center. - Oracle Analytical functions should be natively used. - E-LT support as well as ETL support.
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Performance
Apache
No answers on this topic
Informatica
PowerCenter is robust and fast, and it does a great job meeting all the needs, not just the most commercially vocal needs. In the hands of an expert power user, you can accomplish almost anything with your data. It is not for new users or intermittent users-- for that the Cloud version is a better fit. Be prepared for costly connectors (priced differently for each source or destination you are working with), and just be planful of your projects so you are not paying for connectors you no longer need or want
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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
Informatica power center is a leader of the pack of ETL tools and has some great abilities that make it stand out from other ETL tools. It has been a great partner to its clients over a long time so it's definitely dependable. With all the great things about Informatica, it has a bit of tech burden that should be addressed to make it more nimble, reduce the learning curve for new developers, provide better connectivity with visualization tools.
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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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Informatica
While Talend offers a much more comfortable interface to work with, Informatica's forte is performance. And on that front, Informatica Enterprise Data Integration certainly leaves Talend in the dust. For a more back-end-centric use case, Informatica is certainly the ETL tool of choice. On the other hand, if business users would be using the tool, then Talend would be the preferred tool.
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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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Informatica
  • The data pipeline automation capability of Informatica means that few resources are needed to pre-process the data that ultimately resides in a Data Warehouse. Once a workflow is implemented, manual intervention is not needed.
  • PowerCenter did require more resources and time for installation and configuration than was expected/planned for.
  • The lack of or minimal support of unstructured data means that newer sources of dynamic/changing data cannot be easily processed/transformed through PowerCenter workflows.
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