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Apache Ignite VS AWS IoT Analytics

Compare Apache Ignite VS AWS IoT Analytics and see what are their differences

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Apache Ignite logo Apache Ignite

high-performance, integrated and distributed in-memory platform for computing and transacting on...

AWS IoT Analytics logo AWS IoT Analytics

IoT Management
  • Apache Ignite Landing page
    Landing page //
    2023-07-08
  • AWS IoT Analytics Landing page
    Landing page //
    2022-02-05

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.

AWS IoT Analytics features and specs

  • Scalable
    AWS IoT Analytics automatically scales to support large volumes of IoT data, accommodating billions of messages from millions of devices without the need for extensive infrastructure management.
  • Integration
    Seamlessly integrates with other AWS services like AWS Lambda, Amazon S3, and Amazon QuickSight for extended functionality and complete data processing and visualization workflows.
  • Time-series analysis
    Designed specifically to handle time-series data, providing tools and pre-built functions to analyze and visualize trends over time, which is crucial for monitoring IoT devices.
  • Data Enrichment
    Enables the enrichment of IoT data by integrating external data sources and using metadata, allowing for more contextual and meaningful data insights.
  • Machine Learning Support
    Supports integration with AWS's machine learning services, allowing users to build, train, and deploy models for predictive analysis directly on their IoT data.

Possible disadvantages of AWS IoT Analytics

  • Complexity
    The broad feature set and integration options can lead to a steep learning curve for users unfamiliar with AWS services and IoT analytics workflows.
  • Cost
    While offering extensive capabilities, the cost of using AWS IoT Analytics can become significant, especially as data volumes and processing needs increase.
  • Dependency on AWS Ecosystem
    Requires reliance on the AWS ecosystem, which can be a limitation for organizations using multi-cloud strategies or those wanting to maintain vendor neutrality.
  • Latency
    Although designed for handling IoT data, there can be latency issues in data processing and analysis, especially with high-frequency data ingestion.
  • Security Complexity
    Managing security and ensuring compliance can be complex due to the sensitive nature of IoT data and the need to configure various AWS security settings properly.

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

AWS IoT Analytics videos

+ Add

AWS IoT Analytics - How It Works

More videos:

  • Review - Learn Step by Step How iDevices Uses AWS IoT Analytics - AWS Online Tech Talks

Category Popularity

0-100% (relative to Apache Ignite and AWS IoT Analytics)

Apache Ignite

AWS IoT Analytics

Databases
100 100%
Databases
0% 0
Analytics
0 0%
Analytics
100% 100
NoSQL Databases
100 100%
NoSQL Databases
0% 0
Data Dashboard
0 0%
Data Dashboard
100% 100

User comments

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Social recommendations and mentions

Based on our record, Apache Ignite seems to be more popular. It has been mentiond 3 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.

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 / 7 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 / about 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

AWS IoT Analytics mentions (0)

We have not tracked any mentions of AWS IoT Analytics yet. Tracking of AWS IoT Analytics recommendations started around Mar 2021.

What are some alternatives?

When comparing Apache Ignite and AWS IoT Analytics, you can also consider the following products

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

ThingSpeak - Open source data platform for the Internet of Things. ThingSpeak Features

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

Countly - Product Analytics and Innovation. Build better customer journeys.

memcached - High-performance, distributed memory object caching system

Azure IoT Hub - Manage billions of IoT devices with Azure IoT Hub, a cloud platform that lets you easily connect, monitor, provision, and configure IoT devices.

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