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GoRules.io VS Apache Spark

Compare GoRules.io VS Apache Spark and see what are their differences

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GoRules.io logo GoRules.io

Open-source business rules engine for automating decisions

Apache Spark logo Apache Spark

Apache Spark is an engine for big data processing, with built-in modules for streaming, SQL, machine learning and graph processing.
  • GoRules.io Build decision graphs
    Build decision graphs //
    2024-12-01
  • GoRules.io Easy to use spreadsheets
    Easy to use spreadsheets //
    2024-12-01
  • GoRules.io Validate business logic
    Validate business logic //
    2024-12-01
  • GoRules.io Low-code JavaScript functions
    Low-code JavaScript functions //
    2024-12-01

Next-gen Business Rules Engine

GoRules is an open-source business rules engine that prioritizes business user experience, performance and reliability. It enables you to create rules, and manage multiple versions across multiple workspaces.

Building Blocks That Enhance Collaboration

GoRules is optimized to provide a common language between IT and business, through:

  • Decision Graphs - Build visually stunning decision graphs that are easily understood by both business users and developers.

  • Decision Tables - Simplify business rules management using spreadsheets, with business users taking the lead.

  • Edge functions - Add custom business logic to workflows that is tailored to your organization's unique requirements.

Simplified Business UI

The file-based system is designed to help you optimize your productivity. Revolutionize your productivity with the drag-and-drop rule builder and user-friendly spreadsheets. Organizing and working across multiple teams has never been easier.

Open-source

The engine's core is written in Rust and available in multiple languages through bindings. Supported languages include: Rust, Node.js and Python with more to come.

Enterprise

Scale to over 10,000 requests per second on-premise. The deployment can be done on all 3 major players: AWS, GCP and Azure. Alternatively, you may choose Enterprise Cloud.

  • Apache Spark Landing page
    Landing page //
    2021-12-31

GoRules.io features and specs

  • User-Friendly Interface
    GoRules.io offers a clean and intuitive interface, making it accessible for users with varying levels of technical expertise to quickly implement and manage business rules.
  • Integration Capabilities
    The platform provides robust integration capabilities, allowing seamless connectivity with other software systems, enhancing its applicability in diverse business environments.
  • Scalability
    GoRules.io is designed to efficiently handle growing amounts of work and users, making it a scalable solution for both small and large enterprises.
  • Real-time Processing
    It enables real-time processing of business rules, allowing businesses to quickly react to changes and make informed decisions based on up-to-date data.
  • Strong Security
    GoRules.io includes robust security features to ensure that sensitive business information is protected against unauthorized access and breaches.

Possible disadvantages of GoRules.io

  • Cost
    The pricing of GoRules.io might be on the higher side for small businesses or startups with limited budgets, which could be a potential barrier to entry.
  • Complexity for Non-Technical Users
    While the interface is user-friendly, some complex rule configurations might still require technical understanding, posing challenges for non-technical users.
  • Limited Customization Options
    The platform may have constraints regarding customization, which can limit its flexibility to adapt to specific business requirements or unique use cases.
  • Learning Curve
    New users might experience a learning curve when getting accustomed to the platform's functionalities and optimizing its use for their business needs.
  • Dependence on Internet Connectivity
    Like many cloud-based solutions, GoRules.io requires a stable internet connection, and connectivity issues could interrupt access to the platform.

Apache Spark features and specs

  • Speed
    Apache Spark processes data in-memory, significantly increasing the processing speed of data tasks compared to traditional disk-based engines.
  • Ease of Use
    Spark offers high-level APIs in Java, Scala, Python, and R, making it accessible to a broad range of developers and data scientists.
  • Advanced Analytics
    Spark supports advanced analytics, including machine learning, graph processing, and real-time streaming, which can be executed in the same application.
  • Scalability
    Spark can handle both small- and large-scale data processing tasks, scaling seamlessly from a single machine to thousands of servers.
  • Support for Various Data Sources
    Spark can integrate with a wide variety of data sources, including HDFS, Apache HBase, Apache Hive, Cassandra, and many others.
  • Active Community
    Spark has a vibrant and active community, providing a wealth of extensions, tools, and support options.

Possible disadvantages of Apache Spark

  • Memory Consumption
    Spark's in-memory processing can be resource-intensive, requiring substantial amounts of RAM, which can drive up costs for large-scale deployments.
  • Complexity in Configuration
    To optimize performance, Spark requires careful configuration and tuning, which can be complex and time-consuming.
  • Learning Curve
    Despite its ease of use, mastering the full range of Spark's features and best practices can take considerable time and effort.
  • Latency for Small Data
    For smaller datasets or low-latency requirements, Spark might not be the most efficient choice, as other technologies could offer better performance.
  • Integration Overhead
    Though Spark integrates with many systems, incorporating it into an existing data infrastructure can introduce additional overhead and complexity.
  • Community Support Variability
    While the community is active, the support and quality of third-party libraries and tools can be inconsistent, leading to potential challenges in implementation.

Analysis of Apache Spark

Overall verdict

  • Yes, Apache Spark is generally considered good, especially for organizations and individuals that require efficient and fast data processing capabilities. It is well-supported, frequently updated, and widely adopted in the industry, making it a reliable choice for big data solutions.

Why this product is good

  • Apache Spark is highly valued because it provides a fast and general-purpose cluster-computing framework for big data processing. It offers extensive libraries for SQL, streaming, machine learning, and graph processing, making it versatile for various data processing needs. Its in-memory computing capability boosts the processing speed significantly compared to traditional disk-based processing. Additionally, Spark integrates well with Hadoop and other big data tools, providing a seamless ecosystem for large-scale data analysis.

Recommended for

  • Data scientists and engineers working with large datasets.
  • Organizations leveraging machine learning and analytics for decision-making.
  • Businesses needing real-time data processing capabilities.
  • Developers looking to integrate with Hadoop ecosystems.
  • Teams requiring robust support for multiple data sources and formats.

GoRules.io videos

+ Add

GoRules - Business Rules Engine

Apache Spark videos

+ Add

Weekly Apache Spark live Code Review -- look at StringIndexer multi-col (Scala) & Python testing

More videos:

  • Review - What's New in Apache Spark 3.0.0
  • Review - Apache Spark for Data Engineering and Analysis - Overview

Category Popularity

0-100% (relative to GoRules.io and Apache Spark)

GoRules.io

Apache Spark

Rule Engine
100 100%
Rule Engine
0% 0
Databases
0 0%
Databases
100% 100
BPM
100 100%
BPM
0% 0
Big Data
0 0%
Big Data
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 GoRules.io and Apache Spark

GoRules.io Reviews

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Apache Spark Reviews

15 data science tools to consider using in 2021
Apache Spark is an open source data processing and analytics engine that can handle large amounts of data -- upward of several petabytes, according to proponents. Spark's ability to rapidly process data has fueled significant growth in the use of the platform since it was created in 2009, helping to make the Spark project one of the largest open source communities among big...
Source: searchbusinessanalytics.techtarget.com
Top 15 Kafka Alternatives Popular In 2021
Apache Spark is a well-known, general-purpose, open-source analytics engine for large-scale, core data processing. It is known for its high-performance quality for data processing – batch and streaming with the help of its DAG scheduler, query optimizer, and engine. Data streams are processed in real-time and hence it is quite fast and efficient. Its machine learning...
Source: www.spec-india.com
5 Best-Performing Tools that Build Real-Time Data Pipeline
Apache Spark is an open-source and flexible in-memory framework which serves as an alternative to map-reduce for handling batch, real-time analytics and data processing workloads. It provides native bindings for the Java, Scala, Python, and R programming languages, and supports SQL, streaming data, machine learning and graph processing. From its beginning in the AMPLab at...
Source: www.analyticsinsight.net

Social recommendations and mentions

Based on our record, Apache Spark seems to be a lot more popular than GoRules.io. While we know about 70 links to Apache Spark, we've tracked only 2 mentions of GoRules.io. 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.

GoRules.io mentions (2)

  • 🚀 GoRules Zen Engine: Cross-platform rules engine written in Rust
    On a serious note: We bought gorules.io domain with initial plans for using GoLang, however after a while, the name stuck with us and our clients, and it felt difficult to go back on something we were used to. We don't associate GoLang with the engine, but we do plan support for it sometime soon (via FFI). Source: about 2 years ago
  • 🚀 Introducing GoRules: Open-Source Business Rules Engine
    GoRules is a modern, open-source rules engine designed for high performance and scalability. Our mission is to democratise rules engines and drive early adoption. Rules engines are very useful as they allow business users to easily understand and modify core business logic with little help from developers. You can think of us as a modern, less memory-hungry version of Drools that will be available in many... Source: about 2 years ago

Apache Spark mentions (70)

  • Every Database Will Support Iceberg — Here's Why
    Apache Iceberg defines a table format that separates how data is stored from how data is queried. Any engine that implements the Iceberg integration — Spark, Flink, Trino, DuckDB, Snowflake, RisingWave — can read and/or write Iceberg data directly. - Source: dev.to / about 2 months ago
  • How to Reduce Big Data Analytics Costs by 90% with Karpenter and Spark
    Apache Spark powers large-scale data analytics and machine learning, but as workloads grow exponentially, traditional static resource allocation leads to 30–50% resource waste due to idle Executors and suboptimal instance selection. - Source: dev.to / about 2 months ago
  • Unveiling the Apache License 2.0: A Deep Dive into Open Source Freedom
    One of the key attributes of Apache License 2.0 is its flexible nature. Permitting use in both proprietary and open source environments, it has become the go-to choice for innovative projects ranging from the Apache HTTP Server to large-scale initiatives like Apache Spark and Hadoop. This flexibility is not solely legal; it is also philosophical. The license is designed to encourage transparency and maintain a... - Source: dev.to / 3 months ago
  • The Application of Java Programming In Data Analysis and Artificial Intelligence
    [1] S. Russell and P. Norvig, Artificial Intelligence: A Modern Approach. Pearson, 2020. [2] F. Chollet, Deep Learning with Python. Manning Publications, 2018. [3] C. C. Aggarwal, Data Mining: The Textbook. Springer, 2015. [4] J. Dean and S. Ghemawat, "MapReduce: Simplified Data Processing on Large Clusters," Communications of the ACM, vol. 51, no. 1, pp. 107-113, 2008. [5] Apache Software Foundation, "Apache... - Source: dev.to / 3 months ago
  • Automating Enhanced Due Diligence in Regulated Applications
    If you're designing an event-based pipeline, you can use a data streaming tool like Kafka to process data as it's collected by the pipeline. For a setup that already has data stored, you can use tools like Apache Spark to batch process and clean it before moving ahead with the pipeline. - Source: dev.to / 4 months ago
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What are some alternatives?

When comparing GoRules.io and Apache Spark, you can also consider the following products

Drools - Drools introduces the Business Logic integration Platform which provides a unified and integrated platform for Rules, Workflow and Event Processing.

Apache Flink - Flink is a streaming dataflow engine that provides data distribution, communication, and fault tolerance for distributed computations.

DecisionRules.io - Business rule engine that lets you create and deploy business rules, while all your rules run in a secure and scalable cloud. Unlike other rule engines, you can create your first rule in 5 minutes and make 100k decisions in a minute via API.

Hadoop - Open-source software for reliable, scalable, distributed computing

OptaPlanner - Mathematical optimization software

Apache Storm - Apache Storm is a free and open source distributed realtime computation system.

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