Apache Kafka vs. Informatica PowerCenter

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
Apache Kafka
Score 8.5 out of 10
N/A
Apache Kafka is an open-source stream processing platform developed by the Apache Software Foundation written in Scala and Java. The Kafka event streaming platform is used by thousands of companies for high-performance data pipelines, streaming analytics, data integration, and mission-critical applications.N/A
Informatica PowerCenter
Score 7.8 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 KafkaInformatica PowerCenter
Editions & Modules
No answers on this topic
No answers on this topic
Offerings
Pricing Offerings
Apache KafkaInformatica 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 KafkaInformatica PowerCenter
Top Pros
Top Cons
Features
Apache KafkaInformatica PowerCenter
Data Source Connection
Comparison of Data Source Connection features of Product A and Product B
Apache Kafka
-
Ratings
Informatica PowerCenter
8.5
18 Ratings
4% 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 Kafka
-
Ratings
Informatica PowerCenter
7.5
18 Ratings
11% 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 Kafka
-
Ratings
Informatica PowerCenter
8.2
18 Ratings
1% 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 Kafka
-
Ratings
Informatica PowerCenter
9.0
15 Ratings
9% above category average
Integration with data quality tools00 Ratings9.015 Ratings
Integration with MDM tools00 Ratings9.013 Ratings
Best Alternatives
Apache KafkaInformatica PowerCenter
Small Businesses

No answers on this topic

Skyvia
Skyvia
Score 9.6 out of 10
Medium-sized Companies
IBM MQ
IBM MQ
Score 9.0 out of 10
IBM InfoSphere Information Server
IBM InfoSphere Information Server
Score 8.1 out of 10
Enterprises
IBM MQ
IBM MQ
Score 9.0 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 KafkaInformatica PowerCenter
Likelihood to Recommend
8.3
(18 ratings)
8.0
(21 ratings)
Likelihood to Renew
9.0
(2 ratings)
10.0
(4 ratings)
Usability
10.0
(1 ratings)
9.0
(3 ratings)
Performance
-
(0 ratings)
9.4
(2 ratings)
Support Rating
8.4
(4 ratings)
9.0
(2 ratings)
User Testimonials
Apache KafkaInformatica PowerCenter
Likelihood to Recommend
Apache
Apache Kafka is well-suited for most data-streaming use cases. Amazon Kinesis and Azure EventHubs, unless you have a specific use case where using those cloud PaAS for your data lakes, once set up well, Apache Kafka will take care of everything else in the background. Azure EventHubs, is good for cross-cloud use cases, and Amazon Kinesis - I have no real-world experience. But I believe it is the same.
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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
  • Really easy to configure. I've used other message brokers such as RabbitMQ and compared to them, Kafka's configurations are very easy to understand and tweak.
  • Very scalable: easily configured to run on multiple nodes allowing for ease of parallelism (assuming your queues/topics don't have to be consumed in the exact same order the messages were delivered)
  • Not exactly a feature, but I trust Kafka will be around for at least another decade because active development has continued to be strong and there's a lot of financial backing from Confluent and LinkedIn, and probably many other companies who are using it (which, anecdotally, is many).
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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
  • Sometimes it becomes difficult to monitor our Kafka deployments. We've been able to overcome it largely using AWS MSK, a managed service for Apache Kafka, but a separate monitoring dashboard would have been great.
  • Simplify the process for local deployment of Kafka and provide a user interface to get visibility into the different topics and the messages being processed.
  • Learning curve around creation of broker and topics could be simplified
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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
Kafka is quickly becoming core product of the organization, indeed it is replacing older messaging systems. No better alternatives found yet
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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
Apache Kafka is highly recommended to develop loosely coupled, real-time processing applications. Also, Apache Kafka provides property based configuration. Producer, Consumer and broker contain their own separate property file
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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
Support for Apache Kafka (if willing to pay) is available from Confluent that includes the same time that created Kafka at Linkedin so they know this software in and out. Moreover, Apache Kafka is well known and best practices documents and deployment scenarios are easily available for download. For example, from eBay, Linkedin, Uber, and NYTimes.
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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
I used other messaging/queue solutions that are a lot more basic than Confluent Kafka, as well as another solution that is no longer in the market called Xively, which was bought and "buried" by Google. In comparison, these solutions offer way fewer functionalities and respond to other needs.
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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
  • Positive: Get a quick and reliable pub/sub model implemented - data across components flows easily.
  • Positive: it's scalable so we can develop small and scale for real-world scenarios
  • Negative: it's easy to get into a confusing situation if you are not experienced yet or something strange has happened (rare, but it does). Troubleshooting such situations can take time and effort.
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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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