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SQLstream VS Apache Ignite

Compare SQLstream VS Apache Ignite and see what are their differences

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SQLstream logo SQLstream

SQLstream, Big Data stream processing software, powering smart services for the Internet of Things from streaming machine and sensor data.

Apache Ignite logo Apache Ignite

high-performance, integrated and distributed in-memory platform for computing and transacting on...
  • SQLstream Landing page
    Landing page //
    2023-01-21
  • Apache Ignite Landing page
    Landing page //
    2023-07-08

SQLstream features and specs

  • Real-time Data Processing
    SQLstream provides powerful real-time data processing capabilities, allowing businesses to analyze and react to streaming data with minimal latency.
  • SQL-based Interface
    Users can use their existing SQL skills to interact with data streams, making it easier to integrate into existing systems without needing to learn new programming languages.
  • Scalability
    Designed to handle large volumes of streaming data, SQLstream can scale effectively with business needs, providing reliable performance as data loads increase.
  • Integration with Existing Systems
    Offers integration capabilities with various databases and data sources, facilitating seamless data flow between systems for organizations.
  • Analytics and Insights
    Allows for complex analytics and data insights on-the-fly, providing businesses with actionable intelligence derived from real-time data streams.

Possible disadvantages of SQLstream

  • Complex Setup
    The initial setup and configuration of SQLstream can be complex, requiring expertise to properly implement and optimize the system.
  • Cost
    Depending on user requirements and scale, SQLstream can become costly, which might be a concern for small to medium-sized businesses.
  • Resource Intensive
    Operating at scale, SQLstream may require significant computational resources, including memory and processing power, potentially leading to increased infrastructure costs.
  • Learning Curve
    Although it uses SQL, the variations in streaming SQL might present a learning curve to those unfamiliar with real-time data processing paradigms.
  • Dependency on SQL Skills
    Organizations heavily reliant on other programming languages or paradigms may find the SQL-centric approach limiting and may need to invest in training.

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.

SQLstream videos

+ Add

SQLstream PCAP Monitor

More videos:

  • Demo - SQLstream Demonstration: Streaming Operational Intelligence

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 SQLstream and Apache Ignite)

SQLstream

Apache Ignite

Analytics
100 100%
Analytics
0% 0
Databases
0 0%
Databases
100% 100
Stream Processing
100 100%
Stream Processing
0% 0
NoSQL Databases
0 0%
NoSQL Databases
100% 100

User comments

Share your experience with using SQLstream and Apache Ignite. For example, how are they different and which one is better?
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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.

SQLstream mentions (0)

We have not tracked any mentions of SQLstream yet. Tracking of SQLstream recommendations started around Mar 2021.

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 SQLstream and Apache Ignite, you can also consider the following products

AWS IoT Analytics - IoT Management

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

Azure Stream Analytics - Azure Stream Analytics offers real-time stream processing in the cloud.

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

Amazon Kinesis - Amazon Kinesis services make it easy to work with real-time streaming data in the AWS cloud.

memcached - High-performance, distributed memory object caching system

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