Top 10 Google Cloud Dataflow Alternatives & Competitors

Top 10 Alternatives to Google Cloud Dataflow Recently Reviewed By G2 Community

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Databricks
By Databricks Inc.
4.6/5
(660)
Product Description
Making big data simple
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Reviewers say compared to Google Cloud Dataflow, Databricks is:
Easier to set up
More usable
More expensive
Categories in common with Google Cloud Dataflow:
Big Data Processing and Distribution

Apache Kafka
By The Apache Software Foundation
4.5/5
(128)
Product Description
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.
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Reviewers say compared to Google Cloud Dataflow, Apache Kafka is:
More usable
Easier to set up
Categories in common with Google Cloud Dataflow:
Event Stream Processing

Amazon Kinesis Data Streams
By Amazon Web Services (AWS)
4.3/5
(90)
Product Description
Amazon Kinesis Data Streams is a serverless streaming data service that makes it easy to capture, process, and store data streams at any scale.
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Reviewers say compared to Google Cloud Dataflow, Amazon Kinesis Data Streams is:
Easier to set up
Easier to admin
Better at support
Categories in common with Google Cloud Dataflow:
Event Stream Processing
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Amazon EMR
By Amazon Web Services (AWS)
4.1/5
(65)
Product Description
Amazon EMR is a web-based service that simplifies big data processing, providing a managed Hadoop framework that makes it easy, fast, and cost-effective to distribute and process vast amounts of data across dynamically scalable Amazon EC2 instances.
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Reviewers say compared to Google Cloud Dataflow, Amazon EMR is:
Easier to set up
Easier to admin
More expensive
Categories in common with Google Cloud Dataflow:
Big Data Processing and Distribution

Confluent
By Confluent
4.4/5
(114)
Product Description
A stream data platform.
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Reviewers say compared to Google Cloud Dataflow, Confluent is:
More expensive
Easier to set up
More usable
Categories in common with Google Cloud Dataflow:
Big Data Processing and Distribution
Event Stream Processing

Control-M
By BMC Software
4.4/5
(148)
Product Description
Control-M simplifies application workflow orchestration. It makes it easy to define, schedule, manage and monitor workflows, ensuring visibility and reliability, and improving SLAs.
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Reviewers say compared to Google Cloud Dataflow, Control-M is:
More expensive
More usable
Easier to admin
Categories in common with Google Cloud Dataflow:
Big Data Processing and Distribution

Microsoft SQL Server
By Microsoft
4.4/5
(2,261)
Product Description
SQL Server 2017 brings the power of SQL Server to Windows, Linux and Docker containers for the first time ever, enabling developers to build intelligent applications using their preferred language and environment. Experience industry-leading performance, rest assured with innovative security features, transform your business with AI built-in, and deliver insights wherever your users are with mobile BI.
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Reviewers say compared to Google Cloud Dataflow, Microsoft SQL Server is:
More usable
More expensive
Easier to admin
Categories in common with Google Cloud Dataflow:
Big Data Processing and Distribution

Teradata Vantage
By Teradata
4.3/5
(360)
Product Description
The Teradata Database easily and efficiently handles complex data requirements and simplifies management of the data warehouse environment.
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Reviewers say compared to Google Cloud Dataflow, Teradata Vantage is:
More expensive
More usable
Easier to set up
Categories in common with Google Cloud Dataflow:
Big Data Processing and Distribution

Posit
By Posit
4.5/5
(568)
Product Description
In addition to our open-source data science software, RStudio produces RStudio Team, a unique, modular platform of enterprise-ready professional software products that enable teams to adopt R, Python, and other open-source data science software at scale.
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Reviewers say compared to Google Cloud Dataflow, Posit is:
Easier to set up
Easier to admin
More usable
Categories in common with Google Cloud Dataflow:
Big Data Processing and Distribution

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