Best Event Stream Processing Software

How Many Event Stream Processing Software Products Does G2 Track?

Total Products under this Category: 74

Category Stats (Nov 2026)

  • Average Rating: 4.35/5 (↓0.02 vs Oct 2026) The average rating of products in this category, based on all submitted ratings
  • Top Trending Product: Apache Kafka (+0.09%) - Among all products in this category, Apache Kafka recorded the largest rating increase compared to last month

Last updated: November 02, 2026

How Does G2 Rank Event Stream Processing Software Products?

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G2's software rankings are built on verified user reviews, rigorous moderation, and a consistent research methodology maintained by a team of analysts and data experts. Each product is measured using the same transparent criteria, with no paid placement or vendor influence. While reviews reflect real user experiences, which can be subjective, they offer valuable insight into how software performs in the hands of professionals. Together, these inputs power the G2 Score, a standardized way to compare tools within every category.

G2 Grid® for Event Stream Processing Software

G2 Grid® for Event Stream Processing Software plotting products by satisfaction and market presence

Highlighted products: Confluent, Aiven for Apache Kafka, Redpanda Data Platform, Amazon Managed Streaming for Apache Kafka (Amazon MSK), Amazon Kinesis Data Streams, Apache Kafka, Google Cloud Dataflow, and Amazon Kinesis Data Firehose.

Underlying data: [Grid® JSON](https://www.g2.com/categories/event-stream-processing/grids.json?focus%5B%5D=confluent&focus%5B%5D=aiven-for-apache-kafka&focus%5B%5D=redpanda-data-platform&focus%5B%5D=amazon-managed-streaming-for-apache-kafka-amazon-msk&focus%5B%5D=aws-amazon-kinesis-data-streams&focus%5B%5D=apache-kafka&focus%5B%5D=google-cloud-dataflow&focus%5B%5D=amazon-kinesis-data-firehose)

Confluent

Today’s customers expect every digital experience to be immediate, connected, and personalized. That takes more than data at rest - it takes trusted data in motion. Confluent is the complete Data Streaming Platform for keeping data in motion from the moment business change occurs through processing, governance, and serving. Built by the original creators of Apache Kafka®, Confluent connects applications, services, and systems; processes streams in real time with Apache Flink®; and serves trusted, always-current data wherever it is needed across cloud, hybrid, and self-managed environments. With deployment options including fully managed Confluent Cloud, self-managed Confluent Platform and Brint-your-own-cloud Confluent WarpStream, teams can power event-driven applications, real-time analytics, AI, and operational workflows while choosing the operating model that fits their environment and turning live data into better customer experiences and business outcomes.

Average Rating: 4.3/5.0

Total Reviews: 139

How Do G2 Users Rate Confluent?

  • Has the product been a good partner in doing business?: 8.5/10 (Category avg: 8.9/10)
  • Data Sources: 8.8/10 (Category avg: 8.6/10)
  • Data Processing: 8.8/10 (Category avg: 8.8/10)
  • Real-Time Processing: 9.2/10 (Category avg: 9.1/10)

Who Is the Company Behind Confluent?

  • Seller: IBM
  • Company Website:
  • Year Founded: 1911
  • HQ Location: Armonk, New York, United States
  • LinkedIn® Page: www.linkedin.com
    344,328 employees on LinkedIn®
  • Ownership: SWX:IBM

Who Uses This Product?

  • Who Uses This: Senior Software Engineer, Software Engineer
  • Top Industries: Computer Software, Information Technology and Services
  • Company Size: 37% Large, 33% Small

What Do G2 Reviewers Say About Confluent?

AI-generated summary from verified user reviews

Pros
  • Users appreciate the simplified real-time data integration with Confluent's managed cloud services and scalable architecture.
  • Users appreciate the simplicity and scalability of Confluent's cloud services, enhancing real-time data integration effectively.
  • Users praise the wide range of connectors in Confluent, which simplify real-time data integration significantly.
  • Users appreciate the effortless real-time data integration provided by Confluent's simple UI and extensive connectors.
  • Users appreciate the ease of use of Confluent, enjoying effortless real-time data integration and management.
Cons
  • Users find the cost estimation challenging as prices rise with data volume, impacting overall value perception.
  • Users find the pricing increases with data volume, making it costly as they scale their usage of Confluent.
  • Users face initial difficulties with a steep learning curve and limited features in lower-priced editions of Confluent.
  • Users are frustrated with the lack of features in lower editions, contributing to a steep learning curve.
  • Users find the steep learning curve of Confluent requires significant time investment to fully grasp its workflow.

What Are Recent G2 Reviews of Confluent?

What Are G2 Users Discussing About Confluent?

Aiven for Apache Kafka

Aiven for Apache Kafka® is a fully managed distributed event streaming service, that can be deployed in the cloud of your choice. Aiven for Apache Kafka is ideal for event-driven applications, near-real-time data transfer and data pipelines, streaming analytics, and any use case that requires moving huge amounts of real-time data between applications and systems. With Aiven for Apache Kafka you can set up fully managed Kafka clusters in less than 10 minutes — using the Aiven web console or programmatically via Aiven’s API, CLI, Terraform provider or Kubernetes operator. You can easily connect it to your existing tech stack with a fully managed Apache Kafka Connect service with over 30+ connectors. Monitoring your clusters with logs and metrics is also available out of the box via multiple service integrations. Get access to a complete open source ecosystem of streaming technologies and tools around Apache Kafka to fully manage, and operate a real time data infrastructure at scale using: Aiven for Apache Kafka: the core event streaming framework allowing you to transport data within your organization Aiven for Apache Kafka Connect: a fully managed, fully open source, distributed service enabling you to integrate your existing data sources and sinks seamlessly with Aiven for Apache Kafka. Aiven for Apache Kafka MirrorMaker2: a fully managed, fully open source distributed data replication service for cluster to cluster data replication, disaster recovery and geo proximity across multiple regions. Karapace®: a fully open source Kafka Schema Registry that applications can access to serialize and deserialize messages with popular formats such as AVRO, Protobuf and JSON. Aiven for Apache Flink®: a fully managed, fully open source streaming SQL engine for stateful stream processing over your data streams. Klaw: an open source data governance tool that helps enterprises exercise Apache Kafka® topic and schema governance. Aiven is ISO / IEC 27001: 2013, SOC 2, HIPAA, GDPR, and CCPA compliant. Check our pricing and try our free 30-day trial at https://aiven.io/kafka.

Average Rating: 4.3/5.0

Total Reviews: 239

How Do G2 Users Rate Aiven for Apache Kafka?

  • Has the product been a good partner in doing business?: 8.9/10 (Category avg: 8.9/10)
  • Data Sources: 8.3/10 (Category avg: 8.6/10)
  • Data Processing: 8.4/10 (Category avg: 8.8/10)
  • Real-Time Processing: 8.5/10 (Category avg: 9.1/10)

Who Is the Company Behind Aiven for Apache Kafka?

  • Seller: Aiven
  • Year Founded: 2016
  • HQ Location: Helsinki, Southern Finland
  • LinkedIn® Page: www.linkedin.com
    476 employees on LinkedIn®

Who Uses This Product?

  • Who Uses This: Software Engineer, Senior Software Engineer
  • Top Industries: Computer Software, Information Technology and Services
  • Company Size: 45% Medium, 29% Small

What Do G2 Reviewers Say About Aiven for Apache Kafka?

AI-generated summary from verified user reviews

Pros
  • Users find Aiven for Apache Kafka's quick setup and stress-free management to be highly effective for production readiness.
  • Users appreciate the stress-free ease of use of Aiven for Apache Kafka, allowing focus on real business problems.
  • Users praise Aiven for Apache Kafka for its stress-free scaling, enabling focus on real business problems without operational headaches.
  • Users value the management ease of Aiven for Apache Kafka, enabling focus on projects without infrastructure hassles.
  • Users highlight the reliability of Aiven for Apache Kafka, appreciating its seamless setup and effortless management capabilities.
Cons
  • Users find the cost of Aiven for Apache Kafka to be high, especially for small teams and low-traffic scenarios.
  • Users find the poor documentation of Aiven for Apache Kafka hampers their ability to optimize advanced configurations efficiently.
  • Users point out the limited features of Aiven for Apache Kafka, reducing its effectiveness compared to self-managed solutions.
  • Users find the setup process overly complex, requiring significant resources and expertise that may not fit small teams.
  • Users find Aiven for Apache Kafka not user-friendly due to complex configurations and a simplistic interface for advanced tasks.

What Are Recent G2 Reviews of Aiven for Apache Kafka?

What Are G2 Users Discussing About Aiven for Apache Kafka?

Redpanda Data Platform

The Redpanda Data Platform is the streaming platform that simplifies building real-time AI applications. It’s a simple, fast, and secure solution that lets modern engineering teams ship streaming, analytics, and agentic AI apps without the complexity or cost of traditional Kafka-based systems. It comes with 300+ built-in connectors, Kafka-API compatibility and industry-leading data and AI governance with a Bring-Your- Own-Cloud (BYOC) deployment option. Global leaders including Activision Blizzard, Cisco, Moody's, Texas Instruments, Vodafone and 2 of the top 5 banks in the U.S. rely on Redpanda to process hundreds of terabytes of data a day. Backed by premier venture investors Lightspeed, GV and Haystack VC, Redpanda is a diverse, people-first organization with teams distributed around the globe.

Average Rating: 4.6/5.0

Total Reviews: 66

How Do G2 Users Rate Redpanda Data Platform?

  • Has the product been a good partner in doing business?: 9.5/10 (Category avg: 8.9/10)
  • Data Sources: 9.7/10 (Category avg: 8.6/10)
  • Data Processing: 9.7/10 (Category avg: 8.8/10)
  • Real-Time Processing: 9.5/10 (Category avg: 9.1/10)

Who Is the Company Behind Redpanda Data Platform?

  • Seller: Redpanda Data
  • Company Website:
  • Year Founded: 2019
  • HQ Location: San Francisco, US
  • LinkedIn® Page: www.linkedin.com
    211 employees on LinkedIn®

Who Uses This Product?

  • Who Uses This: Software Engineer
  • Top Industries: Computer Software, Financial Services
  • Company Size: 64% Medium, 21% Small

What Do G2 Reviewers Say About Redpanda Data Platform?

AI-generated summary from verified user reviews

Pros
  • Users commend Redpanda's excellent customer support, valuing their guidance and prompt assistance throughout their experience.
  • Users find Redpanda's ease of use significantly enhances their operational efficiency and development speed.
  • Users praise the scalability and reliability of Redpanda, enhancing infrastructure performance with low maintenance requirements.
  • Users celebrate the enhanced speed of Redpanda, significantly improving development and operational reliability across systems.
  • Users are impressed by the robust features and support of Redpanda, greatly enhancing productivity and system reliability.
Cons
  • Users find the complexity of Redpanda's web interface requires reliance on the CLI for advanced configurations.
  • Users face data management issues due to incomplete documentation and reliance on deeper knowledge for setup.
  • Users note several missing features that complicate usage, including incomplete documentation and lack of programmatic setup options.
  • Users highlight the poor documentation of Redpanda Data Platform, causing challenges in setup and feature utilization.
  • Users note the poor UI design of Redpanda Data Platform, leading to reliance on CLI for complex tasks.

What Are Recent G2 Reviews of Redpanda Data Platform?

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Amazon Managed Streaming for Apache Kafka (Amazon MSK)

Amazon Managed Streaming for Apache Kafka (Amazon MSK) is an AWS streaming data service that manages Apache Kafka infrastructure and operations, making it easy for developers and DevOps managers to run Apache Kafka applications and Kafka Connect connectors on AWS, without the need to become experts in operating Apache Kafka. Amazon MSK operates, maintains, and scales Apache Kafka clusters, provides enterprise-grade security features out of the box, and has built-in AWS integrations that accelerate development of streaming data applications. To get started, you can migrate existing Apache Kafka workloads and Kafka Connect connectors into Amazon MSK, or with a few clicks, you can build new ones from scratch. There are no data transfer charges for in-cluster traffic, and no commitments or upfront payments required. You only pay for the resources that you use.

Average Rating: 4.0/5.0

Total Reviews: 23

How Do G2 Users Rate Amazon Managed Streaming for Apache Kafka (Amazon MSK)?

  • Has the product been a good partner in doing business?: 8.1/10 (Category avg: 8.9/10)
  • Data Sources: 8.9/10 (Category avg: 8.6/10)
  • Data Processing: 8.9/10 (Category avg: 8.8/10)
  • Real-Time Processing: 8.9/10 (Category avg: 9.1/10)

Who Is the Company Behind Amazon Managed Streaming for Apache Kafka (Amazon MSK)?

Who Uses This Product?

  • Top Industries: Computer Software
  • Company Size: 46% Small, 33% Medium

What Do G2 Reviewers Say About Amazon Managed Streaming for Apache Kafka (Amazon MSK)?

AI-generated summary from verified user reviews

Pros
  • Users appreciate how Amazon MSK provides hassle-free management of Kafka, simplifying scaling, maintenance, and availability.
  • Users appreciate the ease of maintenance with Amazon MSK, which simplifies Kafka management significantly.
  • Users value the management ease of Amazon MSK, which simplifies Kafka operations and integrates seamlessly with AWS.
  • Users appreciate the reliable management of Amazon MSK, benefiting from automated scaling, maintenance, and seamless AWS integration.
  • Users appreciate the effortless scalability of Amazon MSK, simplifying management while integrating seamlessly with AWS services.
Cons
  • Users find the cost prohibitive for smaller workloads, making it challenging for early-stage projects.
  • Users find the high cost of Amazon MSK challenging for smaller workloads and early-stage projects.
  • Users find the limited control over configurations restrictive, especially for smaller workloads and early-stage projects.

What Are Recent G2 Reviews of Amazon Managed Streaming for Apache Kafka (Amazon MSK)?

What Are G2 Users Discussing About Amazon Managed Streaming for Apache Kafka (Amazon MSK)?

Amazon Kinesis Data Streams

Amazon Kinesis Data Streams is a massively scalable, durable, and low-cost streaming data service. Kinesis Data Streams can continuously capture gigabytes of data per second from hundreds of thousands of sources, such as website clickstreams, database event streams, financial transactions, social media feeds, IT logs, and location-tracking events. The collected data is available in milliseconds to allow real-time analytics use cases, such as real-time dashboards, real-time anomaly detection, dynamic pricing. Customers run more than two million unique streams and process tens of PB of data per day with Amazon Kinesis Data Streams.

Average Rating: 4.3/5.0

Total Reviews: 81

How Do G2 Users Rate Amazon Kinesis Data Streams?

  • Has the product been a good partner in doing business?: 8.6/10 (Category avg: 8.9/10)
  • Data Sources: 9.2/10 (Category avg: 8.6/10)
  • Data Processing: 9.1/10 (Category avg: 8.8/10)
  • Real-Time Processing: 9.4/10 (Category avg: 9.1/10)

Who Is the Company Behind Amazon Kinesis Data Streams?

Who Uses This Product?

  • Who Uses This: Software Engineer
  • Top Industries: Computer Software, Information Technology and Services
  • Company Size: 38% Large, 37% Medium

What Do G2 Reviewers Say About Amazon Kinesis Data Streams?

AI-generated summary from verified user reviews

Pros
  • Users highlight the real-time data processing capability of Amazon Kinesis Data Streams, ensuring seamless handling of streaming data.
  • Users value the real-time data processing of Amazon Kinesis Data Streams, appreciating its stability and seamless AWS integration.
  • Users highlight the real-time data processing capability of Amazon Kinesis Data Streams, ensuring low-latency and robust performance.
  • Users appreciate the real-time data processing capability of Amazon Kinesis Data Streams for handling large data volumes efficiently.
  • Users appreciate the seamless API integration of Amazon Kinesis Data Streams with other AWS services, enhancing overall convenience.
Cons
  • Users find the difficult setup of Amazon Kinesis Data Streams challenging, particularly for those unfamiliar with AWS and streaming concepts.
  • Users find the service to be notably expensive, particularly as usage scales and requires close cost management.
  • Users report a resource-intensive learning process with Kinesis, particularly regarding setup and cost management challenges.
  • Users find the complexity of configuration and learning steep for effective use of Kinesis Data Streams.
  • Users find the complex configuration across AWS data services to be a significant hurdle in using Kinesis Data Streams.

What Are Recent G2 Reviews of Amazon Kinesis Data Streams?

What Are G2 Users Discussing About Amazon Kinesis Data Streams?

Apache Kafka

Apache Kafka is an open-source distributed event streaming platform developed by the Apache Software Foundation. It is designed to handle real-time data feeds with high throughput and low latency, making it ideal for building data pipelines, streaming analytics, and integrating data across various systems. Kafka enables organizations to publish, store, and process streams of records in a fault-tolerant and scalable manner, supporting mission-critical applications across diverse industries. Key Features and Functionality: - High Throughput and Low Latency: Kafka delivers messages at network-limited throughput with latencies as low as 2 milliseconds, ensuring efficient data processing. - Scalability: It can scale production clusters up to thousands of brokers, handling trillions of messages per day and petabytes of data, while elastically expanding and contracting storage and processing capabilities. - Durable Storage: Kafka stores streams of data safely in a distributed, durable, and fault-tolerant cluster, ensuring data integrity and availability. - High Availability: The platform supports efficient stretching of clusters over availability zones and connects separate clusters across geographic regions, enhancing resilience. - Stream Processing: Kafka provides built-in stream processing capabilities through the Kafka Streams API, allowing for operations like joins, aggregations, filters, and transformations with event-time processing and exactly-once semantics. - Connectivity: With Kafka Connect, it integrates seamlessly with hundreds of event sources and sinks, including databases, messaging systems, and cloud storage services. Primary Value and Solutions Provided: Apache Kafka addresses the challenges of managing real-time data streams by offering a unified platform that combines messaging, storage, and stream processing. It enables organizations to: - Build Real-Time Data Pipelines: Facilitate the continuous flow of data between systems, ensuring timely and reliable data delivery. - Implement Streaming Analytics: Analyze and process data streams in real-time, allowing for immediate insights and actions. - Ensure Data Integration: Seamlessly connect various data sources and sinks, promoting a cohesive data ecosystem. - Support Mission-Critical Applications: Provide a robust and fault-tolerant infrastructure capable of handling high-volume and high-velocity data, essential for critical business operations. By leveraging Kafka's capabilities, organizations can modernize their data architectures, enhance operational efficiency, and drive innovation through real-time data processing and analytics.

Average Rating: 4.5/5.0

Total Reviews: 127

How Do G2 Users Rate Apache Kafka?

  • Has the product been a good partner in doing business?: 8.4/10 (Category avg: 8.9/10)
  • Data Sources: 8.7/10 (Category avg: 8.6/10)
  • Data Processing: 9.0/10 (Category avg: 8.8/10)
  • Real-Time Processing: 9.1/10 (Category avg: 9.1/10)

Who Is the Company Behind Apache Kafka?

Who Uses This Product?

  • Who Uses This: Software Engineer, Senior Software Engineer
  • Top Industries: Computer Software, Information Technology and Services
  • Company Size: 46% Large, 32% Medium

What Do G2 Reviewers Say About Apache Kafka?

AI-generated summary from verified user reviews

Pros
  • Users value the high scalability of Apache Kafka, which excels in handling growing data volumes seamlessly.
  • Users value the real-time data processing capabilities of Kafka, enjoying high throughput and scalability in their systems.
  • Users appreciate the easy integrations of Apache Kafka, enhancing connectivity between their Java-based applications effortlessly.
  • Users appreciate the high scalability and performance of Apache Kafka, making it perfect for handling growing data volumes.
  • Users value the high scalability and performance of Apache Kafka, making it perfect for enterprise data growth.
Cons
  • Users find the operational complexity of Apache Kafka challenging, along with high resource consumption and limited queuing features.
  • Users struggle with the data management issues in Apache Kafka, particularly due to the need for specialized expertise.
  • Users find debugging issues in Apache Kafka challenging, requiring significant effort and effective tools for resolution.
  • Users find the difficult learning curve for Apache Kafka overwhelming, especially for smaller teams needing expertise.
  • Users find the limited customization of Apache Kafka can complicate initial setup and management of brokers and zookeepers.

What Are Recent G2 Reviews of Apache Kafka?

What Are G2 Users Discussing About Apache Kafka?

Google Cloud Dataflow

Cloud Dataflow is a fully-managed service for transforming and enriching data in stream (real time) and batch (historical) modes with equal reliability and expressiveness -- no more complex workarounds or compromises needed. And with its serverless approach to resource provisioning and management, you have access to virtually limitless capacity to solve your biggest data processing challenges, while paying only for what you use.

Average Rating: 4.2/5.0

Total Reviews: 42

How Do G2 Users Rate Google Cloud Dataflow?

  • Has the product been a good partner in doing business?: 9.0/10 (Category avg: 8.9/10)
  • Data Sources: 9.6/10 (Category avg: 8.6/10)
  • Data Processing: 9.8/10 (Category avg: 8.8/10)
  • Real-Time Processing: 9.6/10 (Category avg: 9.1/10)

Who Is the Company Behind Google Cloud Dataflow?

  • Seller: Google
  • Year Founded: 1998
  • HQ Location: Mountain View, CA
  • LinkedIn® Page: www.linkedin.com
    301,144 employees on LinkedIn®
  • Ownership: NASDAQ:GOOG
  • Phone: +1 (650) 253-0000

Who Uses This Product?

  • Top Industries: Computer Software
  • Company Size: 39% Small, 34% Medium

What Do G2 Reviewers Say About Google Cloud Dataflow?

AI-generated summary from verified user reviews

Pros
  • Users appreciate the real-time monitoring and easy integration features of Google Cloud Dataflow for efficient data processing.
  • Users find the ease of use of Google Cloud Dataflow invaluable for building efficient streaming pipelines effortlessly.
  • Users find Google Cloud Dataflow's easy management facilitates efficient building and monitoring of streaming pipelines.
  • Users appreciate the ease of use and integration with data sources for building efficient streaming pipelines.
  • Users appreciate the ease of use and real-time monitoring with Google Cloud Dataflow for efficient streaming pipeline processing.
Cons
  • Users find Google Cloud Dataflow costly compared to alternatives, impacting their decision-making process significantly.
  • Users find Google Cloud Dataflow to be expensive compared to alternatives, such as Apache Flink, impacting budget considerations.
  • Users find installation difficult, facing challenges with implementing features like watermarks effectively in Google Cloud Dataflow.
  • Users find Google Cloud Dataflow difficult to implement due to its complexity and high cost compared to alternatives.

What Are Recent G2 Reviews of Google Cloud Dataflow?

What Are G2 Users Discussing About Google Cloud Dataflow?

IBM Event Streams

IBM Event Streams is a high-throughput, fault-tolerant, event streaming solution. Powered by Apache Kafka, it provides access to enterprise data through event streams, enabling businesses to unlock insights from historical data, and identify and take action on situations in real time and at scale.

Average Rating: 4.3/5.0

Total Reviews: 12

How Do G2 Users Rate IBM Event Streams?

  • Has the product been a good partner in doing business?: 8.3/10 (Category avg: 8.9/10)
  • Data Sources: 9.4/10 (Category avg: 8.6/10)
  • Data Processing: 9.4/10 (Category avg: 8.8/10)
  • Real-Time Processing: 10.0/10 (Category avg: 9.1/10)

Who Is the Company Behind IBM Event Streams?

  • Seller: IBM
  • Year Founded: 1911
  • HQ Location: Armonk, New York, United States
  • LinkedIn® Page: www.linkedin.com
    344,328 employees on LinkedIn®
  • Ownership: SWX:IBM
  • Phone: 1-866-277-7488

Who Uses This Product?

  • Company Size: 54% Large, 23% Medium

What Do G2 Reviewers Say About IBM Event Streams?

AI-generated summary from verified user reviews

Pros
  • Users praise the user-friendly UI of IBM Event Streams, which simplifies Kafka's complexities for efficient event-driven solutions.
  • Users appreciate the user-friendly interface of IBM Event Streams, simplifying Kafka complexities for effective event-driven solutions.

What Are Recent G2 Reviews of IBM Event Streams?

Amazon Kinesis Data Firehose

Amazon Kinesis Data Firehose is the easiest way to reliably load real-time streams into data lakes, warehouses, and analytics services. Kinesis Data Firehose can capture, transform, and load streaming data into Amazon S3, Amazon Redshift, Amazon OpenSearch Service, and Splunk, enabling near real-time analytics with existing business intelligence tools and dashboards you’re already using today. It is a fully managed service that automatically scales to match the throughput of your data and requires no ongoing administration. It can also batch, compress, and encrypt the data before loading it, minimizing the amount of storage used at the destination and increasing security.

Average Rating: 4.2/5.0

Total Reviews: 21

How Do G2 Users Rate Amazon Kinesis Data Firehose?

  • Has the product been a good partner in doing business?: 8.9/10 (Category avg: 8.9/10)
  • Data Sources: 7.5/10 (Category avg: 8.6/10)
  • Data Processing: 8.3/10 (Category avg: 8.8/10)
  • Real-Time Processing: 8.1/10 (Category avg: 9.1/10)

Who Is the Company Behind Amazon Kinesis Data Firehose?

Who Uses This Product?

  • Top Industries: Information Technology and Services
  • Company Size: 44% Medium, 36% Large

What Are Recent G2 Reviews of Amazon Kinesis Data Firehose?

IBM StreamSets

IBM StreamSets is a robust streaming data integration tool for hybrid, multi-cloud environments that enables real-time decision making. It allows ingestion and in-flight transformation of structured, unstructured, and semi-structured data from streaming sources, and reliably delivers trusted data into diverse destinations. Flexible deployment options promote security, cost-effectiveness and performance. With several pre-built connectors, an intuitive no-code/low-code interface, and automatic adaptability to data drifts, StreamSets accelerates data pipeline operationalization. It integrates with IBM’s broader data integration capabilities, enabling reliable pipelines that unify multiple data integration patterns, underpinned by data observability capabilities for continuous data quality monitoring and remediation. That’s why the largest companies in the world trust StreamSets to power millions of data pipelines for modern analytics, data science, smart applications, and hybrid integration.

Average Rating: 4.0/5.0

Total Reviews: 113

How Do G2 Users Rate IBM StreamSets?

  • Has the product been a good partner in doing business?: 8.2/10 (Category avg: 8.9/10)
  • Data Sources: 8.0/10 (Category avg: 8.6/10)
  • Data Processing: 8.2/10 (Category avg: 8.8/10)
  • Real-Time Processing: 9.0/10 (Category avg: 9.1/10)

Who Is the Company Behind IBM StreamSets?

  • Seller: IBM
  • Year Founded: 1911
  • HQ Location: Armonk, New York, United States
  • LinkedIn® Page: www.linkedin.com
    344,328 employees on LinkedIn®
  • Ownership: SWX:IBM
  • Phone: 1-866-277-7488

Who Uses This Product?

  • Who Uses This: Data Engineer, Software Engineer
  • Top Industries: Information Technology and Services, Computer Software
  • Company Size: 43% Large, 34% Medium

What Do G2 Reviewers Say About IBM StreamSets?

AI-generated summary from verified user reviews

Pros
  • Users appreciate the ease of use of IBM StreamSets, thanks to its intuitive design and low-code implementation.
  • Users appreciate the intuitive visual interface of IBM StreamSets, making data pipeline creation simple and efficient.
  • Users appreciate the simplified data integration workflows of IBM StreamSets, enhancing efficiency and productivity across teams.
  • Users find IBM StreamSets to excel in data pipelining, simplifying integration workflows with its intuitive design and reliable support.
  • Users appreciate the wide range of connectors IBM StreamSets offers, enhancing ease in data integration workflows.
Cons
  • Users note a steep learning curve for IBM StreamSets, requiring extensive training to master advanced features.
  • Users express concern over the high costs and licensing complexity associated with IBM StreamSets, affecting accessibility.
  • Users note the learning difficulty with advanced features on IBM StreamSets, requiring significant time and training.
  • Users report slow performance with UI, especially when handling complex pipelines, impacting overall usability.
  • Users find the platform has a steep learning curve, requiring significant time and training to utilize advanced features.

What Are Recent G2 Reviews of IBM StreamSets?

What Are G2 Users Discussing About IBM StreamSets?

Amazon Kinesis Data Analytics

Amazon Kinesis Data Analytics is the easiest way to transform and analyze streaming data in real time with Apache Flink. Kinesis Data Analytics reduces the complexity of building, managing, and integrating Apache Flink applications with other AWS services. Kinesis Data Analytics takes care of everything required to run streaming applications continuously, and scales automatically to match the volume and throughput of your incoming data. With Kinesis Data Analytics, there are no servers to manage, no minimum fee or setup cost, and you only pay for the resources your streaming applications consume.

Average Rating: 4.1/5.0

Total Reviews: 14

How Do G2 Users Rate Amazon Kinesis Data Analytics?

  • Has the product been a good partner in doing business?: 7.5/10 (Category avg: 8.9/10)
  • Data Sources: 9.2/10 (Category avg: 8.6/10)
  • Data Processing: 9.2/10 (Category avg: 8.8/10)
  • Real-Time Processing: 10.0/10 (Category avg: 9.1/10)

Who Is the Company Behind Amazon Kinesis Data Analytics?

Who Uses This Product?

  • Company Size: 67% Large, 27% Small

What Do G2 Reviewers Say About Amazon Kinesis Data Analytics?

AI-generated summary from verified user reviews

Pros
  • Users value the scalable and flexible analytics solutions of AWS, enhancing their data analysis capabilities seamlessly.
  • Users appreciate the scalable and flexible solutions of Amazon Kinesis Data Analytics, enhancing data analysis effectiveness.
  • Users highlight the cost-efficient pay-as-you-go model of Amazon Kinesis Data Analytics, making data analysis affordable and accessible.
  • Users find seamless integration with other AWS services enhances the efficiency and scalability of their data analytics workflows.
  • Users appreciate the seamless integration with other AWS services, enhancing workflow efficiency and simplifying data analysis tasks.
Cons
  • Users face challenges with complexity, including tool selection, steep learning curves, and limited customization in AWS Analytics.
  • Users struggle with complexity issues in tool selection and setup, facing steep learning curves and limited customization.
  • Users face complex setup challenges with AWS Analytics, struggling with tool selection and steep learning curves.
  • Users face challenges with cost management due to careful monitoring needs and potential service limitations in AWS Analytics.
  • Users struggle with a steep learning curve in Amazon Kinesis Data Analytics, making initial setup and tool selection challenging.

What Are Recent G2 Reviews of Amazon Kinesis Data Analytics?

PubNub

PubNub is a real-time platform for developers who build in-app chat and live features. The Chat SDK includes typing indicators, presence, message history, read receipts, reactions, and threads. PubNub supports unlimited subscribers per channel. PubNub assigns each message a server-side timetoken. Messages on a channel form one ordered sequence. End-to-end delivery latency is sub-100ms. DAZN uses PubNub to power chat and Watch Party experiences for 10 million users. PubNub charges by Monthly Active Users or transactions, depending on the plan. PubNub does not bill per connection or per IP address. PubNub does not count peak concurrent connections. You can start free, with no credit card. PubNub is a HIPAA-compliant real-time network with ISO/IEC 27001 certification and SOC 2 Type II compliance. We guarantee up to 99.999% availability. For more information, contact Sales. Start with the Chat SDK docs: https://www.pubnub.com/docs/chat/overview

Average Rating: 4.4/5.0

Total Reviews: 23

How Do G2 Users Rate PubNub?

  • Has the product been a good partner in doing business?: 9.0/10 (Category avg: 8.9/10)
  • Data Sources: 9.2/10 (Category avg: 8.6/10)
  • Data Processing: 8.1/10 (Category avg: 8.8/10)
  • Real-Time Processing: 8.1/10 (Category avg: 9.1/10)

Who Is the Company Behind PubNub?

  • Seller: PubNub
  • Company Website:
  • Year Founded: 2010
  • HQ Location: San Francisco, California
  • LinkedIn® Page: www.linkedin.com
    121 employees on LinkedIn®

Who Uses This Product?

  • Top Industries: Computer Software, Information Technology and Services
  • Company Size: 52% Small, 32% Large

What Do G2 Reviewers Say About PubNub?

AI-generated summary from verified user reviews

Pros
  • Users value the secure and interactive chat functionality of PubNub, enhancing communication and collaboration effectively.
  • Users commend the exceptional customer support of PubNub, ensuring seamless installation and assistance throughout the process.
  • Users appreciate the seamless API integration of PubNub, enhancing real-time communication and data sharing across platforms.
  • Users value the high-quality APIs of PubNub that enhance real-time communication and data sharing across devices.
  • Users value the data security of PubNub, enabling secure communication and file sharing in real-time applications.
Cons
  • Users highlight the need for better billing notice updates, citing a short 30-day notice for data deletion changes.
  • Users feel the 30-day notice for data deletion is too short, highlighting a need for better communication.
  • Users express concern over short notice for data changes, highlighting the need for better communication on such issues.
  • Users face difficult navigation in PubNub's documentation, leading to coding delays and compounded frustration during implementation.
  • Users are concerned about the high costs associated with PubNub, which can escalate unexpectedly and impact budgeting.

What Are Recent G2 Reviews of PubNub?

Informatica Data Integration and Engineering

Cloud Data Integration (CDI) is a comprehensive, AI-powered ETL and ELT solution designed to easily move, transform, and synchronize data between various sources and target systems across any cloud, anywhere, at scale. Built on a modern technology stack, CDI offers easy, efficient and cost-effective data ingestion, transformation, synchronization, and replication for a multi-cloud and serverless world for everyone and everywhere. It covers diverse integration patterns, ensuring you have well-architected and seamlessly automated data pipelines for all your advanced analytics and AI needs.

Average Rating: 4.3/5.0

Total Reviews: 320

How Do G2 Users Rate Informatica Data Integration and Engineering?

  • Has the product been a good partner in doing business?: 8.4/10 (Category avg: 8.9/10)
  • Data Sources: 8.3/10 (Category avg: 8.6/10)
  • Data Processing: 8.8/10 (Category avg: 8.8/10)
  • Real-Time Processing: 7.5/10 (Category avg: 9.1/10)

Who Is the Company Behind Informatica Data Integration and Engineering?

  • Seller: Informatica
  • Company Website:
  • Year Founded: 1993
  • HQ Location: Redwood City, CA
  • LinkedIn® Page: www.linkedin.com
    2,473 employees on LinkedIn®
  • Ownership: NYSE: INFA

Who Uses This Product?

  • Who Uses This: Data Engineer, Data Analyst
  • Top Industries: Information Technology and Services, Computer Software
  • Company Size: 57% Large, 28% Medium

What Do G2 Reviewers Say About Informatica Data Integration and Engineering?

AI-generated summary from verified user reviews

Pros
  • Users value the ease of use of Informatica Data Integration, making ETL tasks manageable for both beginners and experts.
  • Users appreciate the efficiency of Informatica Data Integration, benefiting from seamless connectivity and scalable data transfers.
  • Users appreciate the robust connectivity and ease of use of Informatica Data Integration for ETL tasks.
  • Users value the time-saving access of Informatica Data Integration, enabling efficient workflows without software installation.
  • Users appreciate the automation features of Informatica CDI, enhancing their data integration workflows efficiently.
Cons
  • Users find the complexity of setup and configuration challenging, especially for newcomers to the platform.
  • Users find the complex usability of Informatica Data Integration and Engineering challenging, especially for beginners.
  • Users find debugging difficult, as it often requires significant effort and complex configurations to resolve issues effectively.
  • Users find the difficult setup process of Informatica Data Integration and Engineering challenging, especially for newcomers to the platform.
  • Users struggle with unusual error messages in Informatica, often requiring tedious troubleshooting to resolve mapping issues.

What Are Recent G2 Reviews of Informatica Data Integration and Engineering?

What Are G2 Users Discussing About Informatica Data Integration and Engineering?

RudderStack

RudderStack’s agentic CDP enables you to collect, unify, and activate customer data from an integrated platform that wraps around your data warehouse as the system of record. Our infrastructure is battle tested with proven reliability at peak loads up to 1M events/second and 99.95% uptime. Proactive governance tools enable you to enforce data quality and compliance in pipeline, so downstream systems always get activation ready data. And our AI capabilities transform both how you work with customer data and what you can do with it. With RudderStack, your whole business can trust and use customer data. Data teams ship faster, business teams self-serve rich customer context, and agents consistently deliver powerful, privacy safe experiences. Over 30,000 sites and apps run RudderStack including Crate & Barrel, Foot Locker, Glassdoor, Stripe, Allbirds, and more. RudderStack acquired Blendo in 2020

Average Rating: 4.7/5.0

Total Reviews: 51

How Do G2 Users Rate RudderStack?

  • Has the product been a good partner in doing business?: 9.5/10 (Category avg: 8.9/10)
  • Data Sources: 9.7/10 (Category avg: 8.6/10)
  • Data Processing: 9.4/10 (Category avg: 8.8/10)
  • Real-Time Processing: 9.8/10 (Category avg: 9.1/10)

Who Is the Company Behind RudderStack?

  • Seller: RudderStack
  • Company Website:
  • Year Founded: 2019
  • HQ Location: San Francisco, California
  • LinkedIn® Page: www.linkedin.com
    126 employees on LinkedIn®

Who Uses This Product?

  • Who Uses This: Data Engineer
  • Top Industries: Computer Software, Information Technology and Services
  • Company Size: 54% Medium, 46% Small

What Do G2 Reviewers Say About RudderStack?

AI-generated summary from verified user reviews

Pros
  • Users value the ease of use of RudderStack, enabling quick setup and smooth navigation for efficient data management.
  • Users appreciate the excellent customer support from RudderStack, highlighting their phenomenal partnership and smooth experiences.
  • Users appreciate the easy integration of RudderStack, enabling smooth connections to various platforms with minimal setup.
  • Users appreciate the easy setup of RudderStack, allowing for quick connections and seamless integrations with tools.
  • Users appreciate the easy integrations of RudderStack, enabling seamless connections and efficient data handling across platforms.
Cons
  • Users find the learning curve steep, especially with setup and the confusing interface affecting usability.
  • Users note limited customization options, wishing for more flexibility to tailor the interface and workflows to their needs.
  • Users find the complexity in RudderStack's setup and UI can lead to confusion and debugging challenges.
  • Users express concern over insufficient guidance, highlighting challenges with documentation and error handling in RudderStack.
  • Users face onboarding difficulties with RudderStack, particularly due to the complexity and lack of simple guides.

What Are Recent G2 Reviews of RudderStack?

IBM Cloud Pak for Integration

IBM Cloud Pak for Integration is a set of capabilities to speed integration and scale automation, built for any hybrid cloud, within a single, unified experience. Unlock business data and assets as APIs, connect cloud and on-premise applications, reliably move data with enterprise messaging, deliver real-time event interactions, transfer data across any cloud and deploy and scale with cloud-native architecture and shared foundational services — all with end-to-end enterprise-grade security and encryption, making up the broadest set of integration capabilities available on the market today.

Average Rating: 4.2/5.0

Total Reviews: 40

How Do G2 Users Rate IBM Cloud Pak for Integration?

  • Has the product been a good partner in doing business?: 7.5/10 (Category avg: 8.9/10)
  • Data Sources: 8.3/10 (Category avg: 8.6/10)
  • Data Processing: 8.6/10 (Category avg: 8.8/10)
  • Real-Time Processing: 8.1/10 (Category avg: 9.1/10)

Who Is the Company Behind IBM Cloud Pak for Integration?

  • Seller: IBM
  • Year Founded: 1911
  • HQ Location: Armonk, New York, United States
  • LinkedIn® Page: www.linkedin.com
    344,328 employees on LinkedIn®
  • Ownership: SWX:IBM
  • Phone: 1-866-277-7488

Who Uses This Product?

  • Top Industries: Information Technology and Services, Computer Software
  • Company Size: 35% Large, 35% Medium

What Are Recent G2 Reviews of IBM Cloud Pak for Integration?

What Are G2 Users Discussing About IBM Cloud Pak for Integration?

Bijou Barry
BB
Researched and written by Bijou Barry
Updated October 3, 2024

Event Stream Processing Topics

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Learn More About Event Stream Processing Software

What is Event Stream Processing Software?

Data is stored and subsequently processed with traditional data processing tools. This method is not effective when data is constantly changing, as by the time the data has been stored and analyzed, it has likely already changed and become obsolete.

Event stream processing, also known as stream processing, helps ease these concerns by processing the data when it is on the move. As opposed to batch processing, which focuses on data at rest, stream processing allows for the processing of an uninterrupted flow of records. With event stream processing, the data is constantly arriving, with the focus being on identifying how the data has changed over time or detecting anomalies in the historical data, or both.

Key Benefits of Event Stream Processing Software

  • Allow for extremely low latency
  • Analyze data in real time
  • Scale data processing, giving the user the ability to handle any amount of streaming data and process data from numerous sources

Why Use Event Stream Processing Software?

Event stream processing software is incomplete without the ability to manipulate data as it arrives. This software assists with on-the-fly processing, letting users aggregate, perform joins of data within a stream, and more. Users leverage stream processing tools to process data transferred among a whole range of internet of things (IoT) endpoints and devices, including smart cars, machinery, or home appliances. Real-time data processing is key when companies want deeper insight into their data; it is also helpful when time is of the essence—for example, in the case of retail companies looking to keep a constant and consistent record of their inventory across multiple channels.

Gain insights from data — Users leverage event stream processing software as a buffer to connect a company’s many data sources to a data storage solution, such as a data lake. From movie watching on a streaming service to taxi rides on a ride-hailing app, this data can be used for pattern identification and to inform business decisions.

Real time integration— Through the continuous collection of data from data sources, such as databases, sensors, messaging systems, and logs, users are able to ensure their applications which rely on this data are up to date.

Control data flows — Event stream processing software makes it easier to create, visualize, monitor, and maintain data flows.

Who Uses Event Stream Processing Software?

Business users working with data use event stream processing software which gives them access to data in real time.

Developers — Developers looking to build event streaming applications that rely on the flow of big data benefit from event stream processing software. For example, batch processing does not serve an application well that is aimed at providing recommendations based on real-time data. Therefore, developers rely on event stream processing software to best handle this data and process it effectively and efficiently.

Analysts — To analyze big data as it comes, analysts need to utilize a tool that processes the data. With event stream processing software, they are equipped with the proper tools to integrate the data into their analytics platforms.

Machine learning engineers — Data is a key component of the training and development of machine learning models. Having the right data processing software in place is an important part of this process.

Kinds of Event Stream Processing software

There are different methods or manners in which the stream processing takes place.

At-rest analytics — Like log analysis, at rest-analytics looks back on historical data to find trends.

In-stream analytics — A more complex form of analysis occurs with in-stream analytics in which data streams between or across devices are analyzed.

Edge analytics — This method has the added benefit of potentially lowering the latency for data that is processed on device (for example an IoT device), as the data does not necessarily need to be sent to the cloud.

Event Stream Processing Software Features

Event stream processing software, with processing at its core, provides users with the capabilities they need to integrate their data for purposes such as analytics and application development. The following features help to facilitate these tasks:

Connectors — With connectors to a wide range of core systems (e.g., via an API), users extend the reach of existing enterprise assets.

Metrics — Metrics help users analyze the processing to ascertain its performance.

Change data capture (CDC) — CDC turns databases into a streaming data source where each new transaction is delivered to event stream processing software instantaneously.

Data validation— Data validation allows users to visualize the data flow and ensure their data and data delivery is validated.

Pre-built data pipelines — Some tools provide pre-built data pipelines to enable operational workloads in the cloud.

Potential Issues with Event Stream Processing Software

Data organization — It may be challenging to organize data in a way that is easily accessible and harness big data sets that contain historical and real-time data. Companies often need to build a data warehouse or a data lake that combines all the disparate data sources for easy access. This requires highly skilled employees.

Deployment issues — Search software requires lots of work by a skilled development team or vendor support staff to properly deploy the solution, especially if the data is particularly messy. Some data may lack compatibility with different products while some solutions may be geared for different types of data. For example, some solutions may not be optimized for unstructured data, whilst others may be the best fit for numerical data.