Apache Kafka vs. Informatica PowerCenter (legacy)

For brokering messages, Confluent Kafka is well suited since it offers a managed solution ready to use. Scenarios where the solution is not very well suited are for example, where pricing is an issue. The solution costs quite a lot for basic usage (for example: for 3 clusters, pricing is above 100k$ a year).

Incentivized

Informatica Powercenter is the centerpiece of our overall enterprise data warehouse strategy. It's a critical enablement to ensure we can feed in multiple data stream and transform them into digestible data within our data warehouse. With its flexible capabilities and API availability, we were able to feed in industry standard data format as well as home grown data structure. Overall, we are very pleased with their capability and contribution to our data warehouse strategy.

Incentivized

• Apache Kafka is able to handle a large number of I/Os (writes) using 3-4 cheap servers.
• It scales very well over large workloads and can handle extreme-scale deployments (eg. Linkedin with 300 billion user events each day).
• The same Kafka setup can be used as a messaging bus, storage system or a log aggregator making it easy to maintain as one system feeding multiple applications.

Incentivized

• 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.

Incentivized

• The Kafka Tool is a community-made Java application that looks and feels from the past century.
• Logging can be confusing. This certainly shows when we have to do troubleshooting.
• Hybrid scenarios - pub/sub, but there are services in and outside a Kubernetes cluster. Then there are a ~3 options, but only 2 (the harder ones) are production-safe.

Incentivized

• 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.

Incentivized

Kafka has suited our use case very well so far. Going forward we are planning to expand our platform manifold so the load on Kafka and our reliance on Kafka is going to increase only.

Our team enjoys using Informatica and feels that it is one of the best ETL tools on the market.

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

Incentivized

The tool is very flexible and will meet most, if not all, of your data transformation needs. It is an expert-level tool, so building your knowledge-base and user-base (and keeping that base healthy!) is very important. But it will pay off with strong data management and the ability to leverage that data in ways you haven’t thought of yet. Bottom line, data is money, and PowerCenter helps you monetize your data.

Incentivized

Positives; - Multi-user development environment. - The speed of transformation. - Seamless integration with other Informatica products. Negatives; - There should be fewer windows, to maintain developers' focus while using. You probably need two 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.

Incentivized

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.

Incentivized

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.

Incentivized

Apache Kafka is built for scale. From high throughput and real-time data streaming, it has a strong advantage over RabbitMQ with its low latency. This put Apache Kafka at the forefront as the platform of choice for large datasets messaging and ensuring scalability when data scale up tremendously. RabbitMQ however has its strengths in traditional messaging. Routing and message delivery reliability are the bedrock of RabbitMQ and this is where RabbitMQ excels. In my previous workplace, RabbitMQ was of choice as reliability matters more than scale. In two words. Apache Kafka for scale, RabbitMQ for reliability. And for cloud deployment and large dataset messaging in what I am doing now, Apache Kafka is the default choice.

Basically the two solutions have, more or less, the same functions and features.The difference, for me, is that ThreatQuotient make more features over the security and I think is oriented to a SOC enviroments. InformaticaExchange Connectors is oriented to the quality, integration and distribution of the data in order to ensure the reliability and access of data from different sources, as well as the integration in a single repository of enterprise data (External/internal)

Incentivized

• Positive: bursts of traffic on special holidays are easy to handle because Kafka can absorb and buffer all the messages we need to process long enough to let an understaffed set of back-end services catch up on processing. Hard to put a number to it but we probably save $5k a month having fewer machines running.
• Positive: makes decoupling the web and API services from the deeper back-end services easier by providing topics as an interface. This allowed us to split up our teams and have them develop independently of each other, speeding up software development.
• Negative: our engineers have made mistakes such as accidentally dropping a few thousand messages due to the CLI being confusing to use, and as a result a customer lost some of their precious data. I'd say that was more our fault than Kafka's though.

Incentivized

• PowerCenter has been instrumental in being the center of all data movement within the organization.
• It has also provided a foundation for which re-usability and scalability are the focus.
• Finding talent with experience and expertise in PowerCenter is far more likely due to its presence and market share.

Incentivized