What users are saying about
113 Ratings
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Score 8.4 out of 101
74 Ratings
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Score 8.2 out of 101

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.
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Informatica PowerCenter

PowerCenter is well equipped to handle large amounts of data movement in an organization with many disparate sources and a structured development team. It excels in enforcing enterprise development standards through things like metadata manager and the monitoring capabilities (as well as being able to design monitoring rules for everything from naming standards to design practices). It is especially well suited at handling flat-file data in addition to its many connectors and native support for just about any ANSI standard database. For large development teams or the desire to remain flexible at an enterprise scale, Powercenter is a top-tier solution.For small projects or even smaller development teams with mostly a single data source, expect frustration with being able to quickly test a solution as the design flow is very structured. It is also designed in a way that segregation of duties at a very high level can also cause small development teams to be counter-productive. Each step in the design process is a separate application, and although stitched together, is not without its problems. In order to design a simple mapping for example, you would first need a connection established to the source (example, ODBC) and keep in mind that it will automatically name the container according to how you named your connection. You would then open the designer tool, import a connection as a source, optionally check it in, create a target, optionally check it in as well, and design a transformation mapping. In order to test or run it, you will need to open a separate application (Workflow Manager) and create a workflow from your mapping, then create a session for that workflow and a workflow for those one or more sessions at which point you can test it. After running it, in order to observe, you then need to open a separate application (Monitor) to see what it is doing and how well. For a developer coming from something like SSIS, this can be daunting and cumbersome for building a simple POC and trying to test it (although from the inverse, building an enterprise scalable ETL solution from SSIS is its own challenge).
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Feature Rating Comparison

Data Source Connection

Apache Spark
Informatica PowerCenter
8.9
Connect to traditional data sources
Apache Spark
Informatica PowerCenter
9.5
Connecto to Big Data and NoSQL
Apache Spark
Informatica PowerCenter
8.3

Data Transformations

Apache Spark
Informatica PowerCenter
8.7
Simple transformations
Apache Spark
Informatica PowerCenter
8.8
Complex transformations
Apache Spark
Informatica PowerCenter
8.6

Data Modeling

Apache Spark
Informatica PowerCenter
8.2
Data model creation
Apache Spark
Informatica PowerCenter
7.4
Metadata management
Apache Spark
Informatica PowerCenter
8.9
Business rules and workflow
Apache Spark
Informatica PowerCenter
8.3
Collaboration
Apache Spark
Informatica PowerCenter
8.6
Testing and debugging
Apache Spark
Informatica PowerCenter
7.8

Data Governance

Apache Spark
Informatica PowerCenter
8.0
Integration with data quality tools
Apache Spark
Informatica PowerCenter
8.1
Integration with MDM tools
Apache Spark
Informatica PowerCenter
8.0

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
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Informatica PowerCenter

  • Informatica has a wide range of support for databases. Pretty much every mainstream DBMS is compatible here.
  • Designing ETL mappings and workflows is a very intuitive process, and takes minimal learning time and effort even for a beginner.
  • Informatica's biggest strength is its sheer performance. It is unmatched in terms of handling large volumes of data.
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Cons

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
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Informatica PowerCenter

  • One of the challenges of PowerCenter is the lack of integration between the components and functionality provided by PowerCenter. PowerCenter consists of multiple components such has the repository service, integration service, metadata service. Considerable time and resources were required to install and configure these components before PowerCenter was available for use.
  • In order to connect to various data sources such as Netezza database or SAS datasets, PowerCenter requires the installation and configuration of separate plug-ins. We spent considerable time trouble-shooting and debugging problems while trying to get the various plug-ins integrated with PowerCenter and get them up and running as described in the documentation.
  • PowerCenter works well with structured data. That is, it is easy to work with input and output data that is pre-defined, fixed, and unchanging. It is much more difficult to work with dynamic data in which new fields are added or removed ad-hoc or if data format changes during the data ingest process. We have not been as successful in using PowerCenter for dynamic data.
  • One of the challenges of learning PowerCenter is that it is difficult to find documentation or publications that help you learn the various details about PowerCenter software. Unlike SAS Institute, Informatica does not publish books about PowerCenter. The documentation available with PowerCenter is sparse; we have learned many aspects of this technology through trial and error.
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Likelihood to Renew

Apache Spark

No score
No answers yet
No answers on this topic

Informatica PowerCenter

Informatica PowerCenter 10.0
Based on 4 answers
Our team enjoys using Informatica and feels that it is one of the best ETL tools on the market.
Robert Goodman profile photo

Usability

Apache Spark

No score
No answers yet
No answers on this topic

Informatica PowerCenter

Informatica PowerCenter 9.5
Based on 2 answers
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.
Gurcan Orhan profile photo

Performance

Apache Spark

No score
No answers yet
No answers on this topic

Informatica PowerCenter

Informatica PowerCenter 9.5
Based on 2 answers
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

Apache Spark

No score
No answers yet
No answers on this topic

Informatica PowerCenter

Informatica PowerCenter 8.0
Based on 1 answer
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 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.
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Informatica PowerCenter

PowerCenter is the industry leader when it comes to interfacing with multiple source and target systems. The graphical interface increases employee productivity while reducing human resource expenditures and training requirements. These other tools offer some similar capabilities, but lack the range and depth when compared with the PowerCenter platform.
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Return on Investment

Apache Spark

  • It has had a very positive impact, as it helps reduce the data processing time and thus helps us achieve our goals much faster.
  • Being easy to use, it allows us to adapt to the tool much faster than with others, which in turn allows us to access various data sources such as Hadoop, Apache Mesos, Kubernetes, independently or in the cloud. This makes it very useful.
  • It was very easy for me to use Apache Spark and learn it since I come from a background of Java and SQL, and it shares those basic principles and uses a very similar logic.
Carla Borges profile photo

Informatica PowerCenter

  • Positive - Easy to maintain processes built in Informatica Power Center.
  • Positive - Rapidly build and deploy ETL data mappings.
  • Positive - Develop the overall workflow process to run all ETL processes for the project.
  • Negative - Informatica Power Center can be a bit expensive, so your application needs to warrant the enterprise support.
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Pricing Details

Apache Spark

General

Free Trial
Free/Freemium Version
Premium Consulting/Integration Services
Entry-level set up fee?
No

Informatica PowerCenter

General

Free Trial
Free/Freemium Version
Premium Consulting/Integration Services
Entry-level set up fee?
No

Rating Summary

Likelihood to Recommend

Apache Spark
8.5
Informatica PowerCenter
8.6

Likelihood to Renew

Apache Spark
Informatica PowerCenter
10.0

Usability

Apache Spark
Informatica PowerCenter
9.5

Performance

Apache Spark
Informatica PowerCenter
9.5

Support

Apache Spark
Informatica PowerCenter
8.0

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