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Apache Spark VS DecisionRules.io

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

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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.

DecisionRules.io logo 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 Spark Landing page
    Landing page //
    2021-12-31
  • DecisionRules.io Landing page
    Landing page //
    2023-10-20

DecisionRules is designed to be your rules engine, making your day-to-day analyses and procedures easier, running your business more efficiently and smoothly. DecicionRules allows you to know what customers are eligible for certain products, which prices to apply under certain circumstances, and much more. It is a powerful tool that can make 100k decisions in a a minute via API.

Apache Spark

Website
spark.apache.org
Pricing URL
-
$ Details
Platforms
-
Release Date
-
Edit details

DecisionRules.io

Website
decisionrules.io
Pricing URL
Official DecisionRules.io Pricing
$ Details
freemium
Platforms
Web JavaScript REST API Java Node JS .Net PHP
Release Date
2021 January
Edit details

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.

DecisionRules.io features and specs

  • Easy Versioning
    Versioning and cloning of existing business rules. No GIT knowledge needed!
  • DevOps Compatible
    The infrastructure is adapted for quick change of business rules and their easy deployment.
  • Seamless Integration
    Ready made seamless integration thru SDKs, Sample Projects or REST API.
  • Secure & Scalable
    Secure and Scalable cloud based solution at your fingertips.
  • Client App Or Backend Solution
    Ready to handle both your frontend and backend systems integration.
  • Team Collaboration
    Collaborative mode that allows multiple users to share/edit/view their rules.
  • Transparent Decisions
    Allows you to design and maintain decision’s logic clearly outside your software systems hence reinforce transparency within your organization.
  • Decision Tables
    Ready made solution for handling business rules of medium complexity.
  • Codeless Approach
    Business users driven solution, maintainable without profound programming skills.
  • Import & Export Rules
    Straightforward import and export of the rules definition into JSON format.
  • Organized Organization
    Allows you to create a well organized and swiftly accessible repository of all business rules within your organization.
  • Low IT Costs
    Secure and Scalable cloud based solution at your fingertips.

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.

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

DecisionRules.io videos

+ Add

DecisionRules the Innovative Business Rules Management System🚀

Category Popularity

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

Apache Spark

DecisionRules.io

Databases
100 100%
Databases
0% 0
Business & Commerce
0 0%
Business & Commerce
100% 100
Big Data
100 100%
Big Data
0% 0
Rule Engine
0 0%
Rule Engine
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 Apache Spark and DecisionRules.io

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

DecisionRules.io Reviews

We have no reviews of DecisionRules.io yet.
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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.

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
View more

DecisionRules.io mentions (0)

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

What are some alternatives?

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

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

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

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

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

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

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