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Google Cloud Dataflow VS Apache NiFi

Compare Google Cloud Dataflow VS Apache NiFi and see what are their differences

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Contents:
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  2. » Videos
  3. » Reviews
  4. » Alternatives

Google Cloud Dataflow logo Google Cloud Dataflow

Google Cloud Dataflow is a fully-managed cloud service and programming model for batch and streaming big data processing.

Apache NiFi logo Apache NiFi

An easy to use, powerful, and reliable system to process and distribute data.
  • Google Cloud Dataflow Landing page
    Landing page //
    2023-10-03
  • Apache NiFi Landing page
    Landing page //
    2019-01-17

Google Cloud Dataflow features and specs

  • Scalability
    Google Cloud Dataflow can automatically scale up or down depending on your data processing needs, handling massive datasets with ease.
  • Fully Managed
    Dataflow is a fully managed service, which means you don't have to worry about managing the underlying infrastructure.
  • Unified Programming Model
    It provides a single programming model for both batch and streaming data processing using Apache Beam, simplifying the development process.
  • Integration
    Seamlessly integrates with other Google Cloud services like BigQuery, Cloud Storage, and Bigtable.
  • Real-time Analytics
    Supports real-time data processing, enabling quicker insights and facilitating faster decision-making.
  • Cost Efficiency
    Pay-as-you-go pricing model ensures you only pay for resources you actually use, which can be cost-effective.
  • Global Availability
    Cloud Dataflow is available globally, which allows for regionalized data processing.
  • Fault Tolerance
    Built-in fault tolerance mechanisms help ensure uninterrupted data processing.

Possible disadvantages of Google Cloud Dataflow

  • Steep Learning Curve
    The complexity of using Apache Beam and understanding its model can be challenging for beginners.
  • Debugging Difficulties
    Debugging data processing pipelines can be complex and time-consuming, especially for large-scale data flows.
  • Cost Management
    While it can be cost-efficient, the costs can rise quickly if not monitored properly, particularly with real-time data processing.
  • Vendor Lock-in
    Using Google Cloud Dataflow can lead to vendor lock-in, making it challenging to migrate to another cloud provider.
  • Limited Support for Non-Google Services
    While it integrates well within Google Cloud, support for non-Google services may not be as robust.
  • Latency
    There can be some latency in data processing, especially when dealing with high volumes of data.
  • Complexity in Pipeline Design
    Designing pipelines to be efficient and cost-effective can be complex, requiring significant expertise.

Apache NiFi features and specs

  • User-Friendly Interface
    Apache NiFi offers a drag-and-drop interface for designing data flows, making it easy to use even for those without extensive coding experience.
  • Extensive Connector Support
    NiFi comes with a wide range of pre-built connectors for various data sources and destinations, simplifying integration tasks.
  • Real-time Data Processing
    NiFi supports real-time data ingestion and processing, enabling timely data flow management.
  • Scalability
    Designed to be highly scalable, NiFi can handle both small and large data volumes, adjusting to organizational needs as they grow.
  • Flexible Data Routing
    NiFi allows dynamic routing of data based on content, making it versatile for various data transformation and routing needs.
  • Visual Data Monitoring
    It offers real-time monitoring of data flows with visual representations, aiding in quick issue identification and resolution.

Possible disadvantages of Apache NiFi

  • Resource Intensive
    Running NiFi can be resource-intensive, requiring substantial CPU and memory, especially for large-scale operations.
  • Complexity for Advanced Operations
    While straightforward for basic tasks, more complex workflows can become challenging and may require deeper technical expertise.
  • Security Management
    Although NiFi includes security features, configuring and maintaining a secure environment can be complex and time-consuming.
  • Limited Community Support
    As a specialized tool, the user community and available online resources are smaller compared to more widespread software solutions.
  • Learning Curve
    New users may face a steep learning curve, particularly when dealing with advanced features and custom processor development.
  • Licensing Costs for Enterprise Features
    Additional enterprise features and support offered by commercial versions may incur extra costs, potentially increasing the total cost of ownership.

Google Cloud Dataflow videos

+ Add

Introduction to Google Cloud Dataflow - Course Introduction

More videos:

  • Review - Serverless data processing with Google Cloud Dataflow (Google Cloud Next '17)
  • Review - Apache Beam and Google Cloud Dataflow

Apache NiFi videos

+ Add

Forget Duplicating Local Changes: Apache NiFi and the Flow Development Lifecycle (FDLC)

Category Popularity

0-100% (relative to Google Cloud Dataflow and Apache NiFi)

Google Cloud Dataflow

Apache NiFi

Big Data
100 100%
Big Data
0% 0
Analytics
0 0%
Analytics
100% 100
Data Dashboard
100 100%
Data Dashboard
0% 0
Data Integration
0 0%
Data Integration
100% 100

User comments

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Reviews

These are some of the external sources and on-site user reviews we've used to compare Google Cloud Dataflow and Apache NiFi

Google Cloud Dataflow Reviews

Top 8 Apache Airflow Alternatives in 2024
Google Cloud Dataflow is highly focused on real-time streaming data and batch data processing from web resources, IoT devices, etc. Data gets cleansed and filtered as Dataflow implements Apache Beam to simplify large-scale data processing. Such prepared data is ready for analysis for Google BigQuery or other analytics tools for prediction, personalization, and other purposes.
Source: blog.skyvia.com

Apache NiFi Reviews

Top 8 Apache Airflow Alternatives in 2024
Another product by Apache is called NiFi – even though it’s also dedicated to data workflow management, it differs from Apache Airflow in many aspects. First of all, Apache NiFi is a completely web-based tool with a drag&drop interface and no coding. It’s easy to add and configure processors as graph nodes of data workflow, set up routing directions as graph edges, and...
Source: blog.skyvia.com
11 Best FREE Open-Source ETL Tools in 2024
Apache NiFi allows you to automate and manage the flow of information systems. It also enables NiFi to be an effective platform for building scalable and powerful dataflows. NiFi follows the fundamental concept of Flow-Based Programming. It has a highly configurable web-based UI, and houses features such as Data Provenance, Extensibility, and Security features.
Source: hevodata.com
10 Best Airflow Alternatives for 2024
Apache NiFi is a free and open-source application that automates data transfer across systems. The application comes with a web-based user interface to manage scalable directed graphs of data routing, transformation, and system mediation logic. It is a sophisticated and reliable data processing and distribution system. To edit data at runtime, it provides a highly flexible...
Source: hevodata.com
15 Best ETL Tools in 2022 (A Complete Updated List)
Apache Nifi simplifies the data flow between various systems using automation. The data flows consist of processors and a user can create their own processors. These flows can be saved as templates and later can be integrated with more complex flows. These complex flows can then be deployed to multiple servers with minimal efforts.
Source: www.softwaretestinghelp.com
Top 10 Popular Open-Source ETL Tools for 2021
Apache NiFi allows you to automate and manage the flow of information systems. It also enables NiFi to be an effective platform for building scalable and powerful dataflows. NiFi follows the fundamental concept of Flow-Based Programming. It has a highly configurable web-based UI, and houses features such as Data Provenance, Extensibility, and Security features.
Source: hevodata.com

Social recommendations and mentions

Apache NiFi might be a bit more popular than Google Cloud Dataflow. We know about 18 links to it since March 2021 and only 14 links to Google Cloud Dataflow. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

Google Cloud Dataflow mentions (14)

  • How do you implement CDC in your organization
    Imo if you are using the cloud and not doing anything particularly fancy the native tooling is good enough. For AWS that is DMS (for RDBMS) and Kinesis/Lamba (for streams). Google has Data Fusion and Dataflow . Azure hasData Factory if you are unfortunate enough to have to use SQL Server or Azure. Imo the vendored tools and open source tools are more useful when you need to ingest data from SaaS platforms, and... Source: over 2 years ago
  • Here’s a playlist of 7 hours of music I use to focus when I’m coding/developing. Post yours as well if you also have one!
    This sub is for Apache Beam and Google Cloud Dataflow as the sidebar suggests. Source: over 2 years ago
  • How are view/listen counts rolled up on something like Spotify/YouTube?
    I am pretty sure they are using pub/sub with probably a Dataflow pipeline to process all that data. Source: over 2 years ago
  • Best way to export several GCP datasets to AWS?
    You can run a Dataflow job that copies the data directly from BQ into S3, though you'll have to run a job per table. This can be somewhat expensive to do. Source: over 2 years ago
  • Why we don’t use Spark
    It was clear we needed something that was built specifically for our big-data SaaS requirements. Dataflow was our first idea, as the service is fully managed, highly scalable, fairly reliable and has a unified model for streaming & batch workloads. Sadly, the cost of this service was quite large. Secondly, at that moment in time, the service only accepted Java implementations, of which we had little knowledge... - Source: dev.to / about 3 years ago
View more

Apache NiFi mentions (18)

  • NSA Ghidra open-source reverse engineering framework
    They also contributed Apache NiFi but that was much earlier: https://nifi.apache.org/. - Source: Hacker News / 12 months ago
  • Workbench for Apache NiFi data flows
    This article presents the concept and implementation of a universal workbench for Apache NiFi data flows. - Source: dev.to / 12 months ago
  • Ask HN: What low code platforms are worth using?
    Apache NIFI (https://nifi.apache.org/). It uses the concept of Flow-based programming. Also its so underacknolged but this tool is very flexible. I have used as an Event Bus all the 3rd-Party Integrations. - Source: Hacker News / over 1 year ago
  • Help with choosing techstack for a new DE team
    Presently setting up Apache Nifi + Apache MiNiFi for the ETL portion of my work. NiFi was easy enough to figure out; but the docs for MiNiFi have been a pain due to differences between the Java and C++ versions. I then entirely configured it with the Java version so that it was easier to search for answers for the MiNiFi yaml syntax. Source: almost 2 years ago
  • Json splitting and Rerouting (new to nifi)
    NIFI, like most Apache projects does most of its discussion on its mailing lists, but also has a slack. Source: about 2 years ago
View more

What are some alternatives?

When comparing Google Cloud Dataflow and Apache NiFi, you can also consider the following products

Google BigQuery - A fully managed data warehouse for large-scale data analytics.

Apache Airflow - Airflow is a platform to programmaticaly author, schedule and monitor data pipelines.

Amazon EMR - Amazon Elastic MapReduce is a web service that makes it easy to quickly process vast amounts of data.

Histats - Start tracking your visitors in 1 minute!

Databricks - Databricks provides a Unified Analytics Platform that accelerates innovation by unifying data science, engineering and business.‎What is Apache Spark?

AFSAnalytics - AFSAnalytics.

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