Software Alternatives, Accelerators & Startups
Register | Login

Google Cloud Dataflow VS Apache Ignite

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

Electe
Discover Electe, our data analytics platform dedicated to SMEs. Don't let your data go unused, take your business into the future! featured
Note: These products don't have any matching categories. If you think this is a mistake, please edit the details of one of the products and suggest appropriate categories.
Contents:
  1. » Base Details
  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 Ignite logo Apache Ignite

high-performance, integrated and distributed in-memory platform for computing and transacting on...
  • Google Cloud Dataflow Landing page
    Landing page //
    2023-10-03
  • Apache Ignite Landing page
    Landing page //
    2023-07-08

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 Ignite features and specs

  • In-Memory Data Grid
    Apache Ignite provides a robust in-memory data grid that can drastically improve data access speeds by storing data in memory across distributed nodes.
  • Scalability
    The system is designed to scale horizontally, allowing users to add more nodes to handle increased loads, thereby ensuring high availability and performance.
  • Distributed Compute Capabilities
    Ignite supports parallel execution of tasks across cluster nodes, which is beneficial for complex computations and real-time processing.
  • Persistence
    Although primarily in-memory, Ignite offers a durable and transactional Persistence layer that ensures data can be persisted on disk, providing a hybrid in-memory and persistent storage solution.
  • SQL Queries
    Ignite offers support for ANSI-99 SQL, which allows users to execute complex SQL queries across distributed datasets easily.
  • Integration
    It integrates well with existing Hadoop and Spark setups, allowing users to enhance their existing data pipelines with Ignite’s capabilities.
  • Fault Tolerance
    Apache Ignite includes built-in mechanisms for recovery and ensures that data copies are maintained across nodes for resilience against node failures.

Possible disadvantages of Apache Ignite

  • Complexity
    Apache Ignite can be complex to set up and manage, especially when configuring a large, distributed system with multiple nodes.
  • Resource Intensive
    Running an in-memory data grid like Ignite requires significant memory resources, which can increase operational costs.
  • Learning Curve
    Due to its comprehensive features and distributed nature, there is a steep learning curve associated with effectively utilizing Ignite.
  • Configuration Overhead
    There is substantial configuration overhead involved to optimize performance and ensure proper cluster management.
  • Community Support
    Although it has active development, the community support might not be as robust compared to other more mature solutions, possibly leading to challenges in finding solutions to niche issues.
  • YARN Dependence
    For those looking to integrate with Hadoop, Ignite's optimal performance is sometimes reliant on Hadoop YARN, which can introduce additional complexity.

Analysis of Google Cloud Dataflow

Overall verdict

  • Google Cloud Dataflow is a strong choice for users who need a flexible and scalable data processing solution. It is particularly well-suited for real-time and large-scale data processing tasks. However, the best choice ultimately depends on your specific requirements, including cost considerations, existing infrastructure, and technical skills.

Why this product is good

  • Google Cloud Dataflow is a fully managed service for stream and batch data processing. It is based on the Apache Beam model, allowing for a unified data processing approach. It is highly scalable, offers robust integration with other Google Cloud services, and provides powerful data processing capabilities. Its serverless nature means that users do not have to worry about infrastructure management, and it dynamically allocates resources based on the data processing needs.

Recommended for

  • Organizations that require real-time data processing.
  • Projects involving complex data transformations.
  • Users who already utilize Google Cloud Platform and need seamless integration with other Google services.
  • Developers and data engineers familiar with Apache Beam or those willing to learn.

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 Ignite videos

+ Add

Best Practices for a Microservices Architecture on Apache Ignite

More videos:

  • Review - Apache Ignite + GridGain powering up banks and financial institutions with distributed systems

Category Popularity

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

Google Cloud Dataflow

Apache Ignite

Big Data
100 100%
Big Data
0% 0
Databases
0 0%
Databases
100% 100
Data Dashboard
100 100%
Data Dashboard
0% 0
NoSQL Databases
0 0%
NoSQL Databases
100% 100

User comments

Share your experience with using Google Cloud Dataflow and Apache Ignite. For example, how are they different and which one is better?
Log in or Post with

Reviews

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

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 Ignite Reviews

We have no reviews of Apache Ignite yet.
Be the first one to post

Social recommendations and mentions

Based on our record, Google Cloud Dataflow should be more popular than Apache Ignite. It has been mentiond 14 times since March 2021. 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 Ignite mentions (3)

  • API Caching: Techniques for Better Performance
    Apache Ignite — Free and open-source, Apache Ignite is a horizontally scalable key-value cache store system with a robust multi-model database that powers APIs to compute distributed data. Ignite provides a security system that can authenticate users' credentials on the server. It can also be used for system workload acceleration, real-time data processing, analytics, and as a graph-centric programming model. - Source: dev.to / 8 months ago
  • Ask HN: P2P Databases?
    Ignite works as you describe: https://ignite.apache.org/ I wouldn't really recommend this approach, I would think more in terms of subscriptions and topics and less of a 'database'. - Source: Hacker News / over 3 years ago
  • .NET and Apache Ignite: Testing Cache and SQL API features — Part I
    Last days, I started using Apache Ignite as a cache strategy for some applications. Apache Ignite is an open-source In-Memory Data Grid, distributed database, caching, and high-performance computing platform. Source: over 3 years ago

What are some alternatives?

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

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

Redis - Redis is an open source in-memory data structure project implementing a distributed, in-memory key-value database with optional durability.

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

MongoDB - MongoDB (from "humongous") is a scalable, high-performance NoSQL database.

Qubole - Qubole delivers a self-service platform for big aata analytics built on Amazon, Microsoft and Google Clouds.

memcached - High-performance, distributed memory object caching system

Loading...