# Which event stream processing platforms are most trusted by data engineers at financial services firms based on user reviews?

<p class="elv-tracking-normal elv-text-default elv-font-figtree elv-text-base elv-leading-base elv-font-normal" elv="true">Looking for input from G2 reviewers who are data engineers, data architects, and platform engineers at banks, investment managers, insurance companies, and fintech firms in the <a class="a a--md" elv="true" href="https://www.g2.com/categories/event-stream-processing">Event Stream Processing category</a>, specifically from those building streaming pipelines where the data being processed includes financial transactions, trading events, fraud signals, or regulatory reporting data.</p><p class="elv-tracking-normal elv-text-default elv-font-figtree elv-text-base elv-leading-base elv-font-normal" elv="true">The platforms with the strongest financial services data engineering trust evidence:</p><ul>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/aws-amazon-kinesis-data-streams/reviews"><strong>Amazon Kinesis Data Streams</strong></a>: The combination of durability, replay capability, and deep AWS analytics integration is the financial services data engineering trust model for teams on AWS whose streaming infrastructure must connect seamlessly to the regulatory reporting, audit, and analytics stack running on the same cloud. </li>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/confluent/reviews"><strong>Confluent Platform</strong></a>: The Kafka protocol's deterministic ordering within partitions makes it the standard for financial event sequencing where the order of transaction events determines the correctness of downstream balance and position calculations. </li>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/apache-flink/reviews"><strong>Apache Flink</strong></a>: The ability to compute correct running aggregates, detect patterns across time windows, and join real-time transaction streams with reference data while handling out-of-order events using event time watermarks is described as the streaming compute capability that makes Flink the platform of choice for complex financial analytics over event streams. </li>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/google-cloud-pub-sub/reviews"><strong>Google Cloud Pub/Sub</strong></a>: The encryption in transit and at rest, combined with GCP's financial services compliance certifications covering PCI-DSS, SOC 2, and ISO 27001, address the security baseline that financial services infrastructure teams require before deploying a streaming service for transaction data.</li>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/redpanda/reviews"><strong>Redpanda</strong></a>: The Kafka-compatible streaming with lower operational overhead is the financial services trust model for engineering teams whose compliance requirements include on-premises or private cloud deployment for data sovereignty reasons that prevent using fully managed cloud streaming services for transaction data. </li>
</ul><p class="elv-tracking-normal elv-text-default elv-font-figtree elv-text-base elv-leading-base elv-font-normal" elv="true">For data engineers at financial services firms who have deployed a streaming pipeline processing transaction or trading data in production: what was the compliance or audit requirement that most constrained your streaming platform selection, and was that requirement met natively by the platform or through additional architectural components built on top of the streaming layer?</p>

##### Post Metadata
- Posted at: 18 days ago
- Author title: Marketing Executive
- Net upvotes: 1


## Comments
### Comment 1

Confluent and Apache Kafka are the two names most trusted by data engineers, Kafka as the open-source standard for event streaming and Confluent as the enterprise-grade managed platform built on top of it, both well suited to the throughput and reliability demands financial services firms need.

##### Comment Metadata
- Posted at: 9 days ago





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