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Higson.io VS Apache Spark

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

Draxlr
Turn SQL Data into Decisions. Build professional dashboards and data visualizations without technical expertise. Easily embed analytics anywhere, receive automated alerts, and discover AI-powered insights all through a straightforward interface. featured
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Contents:
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  3. » Reviews
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Higson.io logo Higson.io

Higson is a BRMS, that was created with very large decisions and hyper-performance in mind. It stands out with the concept of the business domain which organizes the whole configuration in easy to manage way.

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.
  • Higson.io Decision Table in Higson
    Decision Table in Higson //
    2024-08-14
  • Higson.io Tester Mode
    Tester Mode //
    2024-08-14
  • Higson.io Higson Studio
    Higson Studio //
    2024-08-14

It’s a hyper-efficient tool for managing business rules that enables business experts to fine-tune these rules in run-time without relying on IT support. Business rules can be quickly created or updated, without having to go through lengthy and expensive development cycles. During configuration in Higson, a developer may check how a user’s unpublished session will influence the app’s behavior by using dev_mode. Higson is compatible with any tech stack, including Java, .Net, and Python.

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

Higson.io features and specs

  • Structure
    Intuitive tree structure for your rules. Our structure corresponds with your business, so it’s super easy to navigate.
  • Performance
    Higson Engine represents a significant leap forward in performance, scalability, and resource optimization.  
  • Publish changes without deployment
    The application using Higson is updated without the need for releasing a new version of the application.
  • Tester
    HIgson provides testing module for parameters, functions and domain elements. It means that every change done by a user may be verified before publishing.
  • Versionning
    A feature for saving states of decision tables, functions, and business rules at specific moments.
  • Decision table
    The easy tounderstand matrix that matches input data with a decision. They look very trivial; however you can achieve complex configurations using them, which is their power - everybody can understand how to model decisions using them.
  • Functions
    In some cases, you need to write more complex logic. In Higson, you can use Groovy language - very simple for simple logic, yet powerful. So powerful that You can ask your IT department for help implementing complex things that require loops and other complex techniques.
  • Batch Tester
    An advanced testing tool for mass validation of multiple business rules, decision tables, or functions.

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.

Higson.io videos

+ Add

Higson - explainer video

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 Higson.io and Apache Spark)

Higson.io

Apache Spark

Business & Commerce
100 100%
Business & Commerce
0% 0
Databases
0 0%
Databases
100% 100
Data Dashboard
100 100%
Data Dashboard
0% 0
Big Data
0 0%
Big Data
100% 100

Questions and Answers

As answered by people managing Higson.io and Apache Spark.

What makes your product unique?

Higson.io's answer

High Performance: Higson.io is designed to handle complex and large-scale rule processing efficiently. It can process up to 16,500 calls per second on a single CPU core, which is significantly faster than many competitors. This capability is crucial for industries like insurance, where real-time decision-making is essential​​.

User-Friendly Interface: Higson.io is accessible to both business users and developers. Its intuitive interface -Higson Studio, allows business users to define, modify, and deploy business rules without requiring deep programming knowledge. T

Flexibility and Integration: Higson.io can be integrated with various systems using its REST API or Java API, making it adaptable to different technological environments.

Advanced Versioning and Testing: Higson.io supports sophisticated versioning and testing mechanisms. Users can manage multiple versions of rules simultaneously and test them before deployment, ensuring that changes are implemented safely and effectively​.

Who are some of the biggest customers of your product?

Higson.io's answer

Allianz, Warta (Talanx Group), Unum, Nationale Nederlanden, Bosch, Sompo, Husqvarna.

User comments

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Reviews

These are some of the external sources and on-site user reviews we've used to compare Higson.io and Apache Spark

Higson.io Reviews

We have no reviews of Higson.io yet.
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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 more popular. It has been mentiond 70 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.

Higson.io mentions (0)

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

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 Higson.io and Apache Spark, you can also consider the following products

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.

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

Experian PowerCurve - Experian PowerCurve is a customer lifecycle management and decision automation platform purpose-built for finance and marketing leaders.

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

CA Aion Business Rules Expert - CA Aion Business Rules Expert is a business decision and rule development tool that enables you to easily construct and maintain complex business processes in a visual environment.

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

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