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Bright Data VS Apache SystemML

Compare Bright Data VS Apache SystemML and see what are their differences

Bright Data

World's largest proxy service with a residential proxy network of 72M IPs worldwide and proxy management interface for zero coding.

Bright Data Landing page
Rating
4.0 · 1 review
Pricing
Open source
Is it good? Alternatives
Apache SystemML

Apache SystemML is a machine learning platform optimal for big data.

Apache SystemML Landing page
Rating
0 reviews
Pricing
Open source
Is it good? Alternatives
ASocks
Clear, Fast & Unlimited. Residential & Mobile Proxies For Best Price. sponsored
Note: These products don't have any matching categories. If you think this is a mistake, please edit the details of one of the products and suggest appropriate categories.

Which is more popular?

Based on our record, Bright Data seems to be more popular. It has been mentioned 45 times since March 2021.

social mentions
45 vs 0
Proxy popularity
100% vs 0%
alternatives listed
240+ vs 26

Base details

Website, pricing, platforms and company facts side by side.

Bright Data
Apache SystemML
Website brightdata.com systemds.apache.org
Pricing
Open source Official pricing
Open source
Company 2021 —
Listed in
Proxy Residential Proxies Private Proxy Data Collector Unblocker SERP Analysis +3
Data Science And Machine Learning Data Science Tools Python Tools Software Libraries +1

Features and specs

What each product offers, as listed by its team.

Bright Data 5 features
Apache SystemML 5 features
  • Extensive Proxy Network
    Bright Data offers a vast and diverse network of over 72 million IPs, ensuring high availability and reliability for users.
  • Wide Range of Services
    Provides various proxy solutions including data center, residential, mobile, and ISP proxies, catering to different user needs.
  • Geographical Targeting
    Allows users to target proxies based on specific countries, cities, and even ASN, which is beneficial for localized data scraping.
  • Advanced Tools and APIs
    Offers sophisticated tools and APIs for automation, data extraction, and optimized proxy management.
  • Customer Support
    Provides round-the-clock customer support and numerous resources such as detailed documentation and integration guides.

Possible disadvantages

  • Cost
    Bright Data's services are priced at a premium, which might be expensive for small businesses or individual users.
  • Complexity
    The extensive range of options and settings can be overwhelming and may require a steep learning curve for new users.
  • Ethical Concerns
    The use of residential and mobile proxies can raise ethical questions regarding user consent and data privacy.
  • Account Approval
    New accounts are subject to approval which can delay immediate access to the service.
  • Occasional IP Blocks
    Despite the large IP pool, users may still experience occasional blocks and captchas when accessing certain websites.
  • Scalability
    Apache SystemML is designed to scale seamlessly from a single laptop to large clusters, allowing for efficient processing of large datasets.
  • Flexibility
    SystemML provides a flexible way to express machine learning algorithms using its high-level DML and PyDML languages, allowing for easy customization and optimization.
  • Optimization
    The system automatically optimizes the execution of DML scripts based on the cluster configuration and characteristics of input data, providing efficient and high-performance computation.
  • Integration
    SystemML is well integrated with Apache Spark, enabling distributed machine learning on Spark's cluster computing framework.
  • Open Source
    As an Apache project, SystemML is open-source, ensuring it benefits from community support and continuous improvements.

Possible disadvantages

  • Complexity
    For users not familiar with DML or PyDML, there might be an initial learning curve to effectively utilize the system for developing machine learning algorithms.
  • Performance Overhead
    Despite its optimization, there could be performance overheads for certain workloads compared to hand-optimized native Spark, TensorFlow, or other specialized ML libraries.
  • Dependency Management
    Managing dependencies and ensuring compatibility with various versions of Spark and Hadoop ecosystems can be challenging.
  • Community Size
    The user and developer community around Apache SystemML might be smaller compared to other, more widely-adopted machine learning frameworks, potentially impacting the availability of community resources and third-party integrations.

Analysis

An editorial look at what each product does well and who it suits.

Bright Data
Apache SystemML

Overall verdict

  • Bright Data is generally considered a good choice for businesses and professionals who require reliable and scalable proxy services. It excels in offering a comprehensive set of features and a vast IP pool, although it might be considered expensive for individual or small-scale users.

Why this product is good

  • Bright Data, formerly known as Luminati Networks, is a well-regarded proxy service provider known for its vast network of IP addresses and wide range of proxy types. It offers residential, data center, and mobile proxies with a focus on reliability and scalability. The service is often praised for its high uptime, excellent customer support, and robust infrastructure, making it a popular choice for businesses needing large-scale data collection and web scraping solutions.

Recommended for

  • Large enterprises needing mass data collection
  • Businesses engaged in web scraping and analysis
  • Companies requiring high uptime and reliability
  • Professionals interested in diverse proxy options, including residential and mobile

Overall verdict

  • Apache SystemDS (formerly SystemML) is a solid, research-backed machine learning system optimized for end-to-end data science workflows, particularly strong for organizations already invested in big data infrastructure like Hadoop and Spark, though it has a smaller community and steeper learning curve compared to mainstream ML frameworks like TensorFlow or scikit-learn.

Why this product is good

  • Provides declarative, R-like and Python-like syntax (DML/PyDML) that automatically optimizes execution plans for distributed computing
  • Scales efficiently from single-machine to large Hadoop/Spark clusters without requiring code changes
  • Originated from IBM Research with strong academic backing and peer-reviewed optimization techniques
  • Supports algorithm customization and allows data scientists to write custom ML algorithms with automatic optimization
  • Integrates well with existing big data ecosystems (HDFS, Spark, Hadoop)
  • Open-source under Apache Foundation, ensuring transparency and community-driven development
  • Cost-based optimizer automatically decides between local and distributed execution for performance efficiency

Recommended for

  • Enterprises already using Hadoop or Spark clusters for big data processing
  • Data scientists needing to prototype and deploy custom ML algorithms at scale
  • Organizations requiring seamless integration between data engineering and machine learning pipelines
  • Research teams exploring novel ML algorithm implementations with automatic performance optimization
  • Users who prefer R or Python-like syntax but need distributed computing capabilities
  • Companies with large-scale structured data requiring efficient matrix operations and linear algebra computations

Videos

Walkthroughs and reviews on video.

Bright Data 1 video + Add
Apache SystemML 1 video + Add

Rotating Residential Network | Proxy Network Types | Bright Data (Formerly Luminati Networks)

SDS 2016 Apache SystemML-Declarative Large Scale Machine Learning

Category popularity

How often each product is chosen within a category, 0–100% relative to the other.

Score bands 0–20 21–40 41–50 51–60 61–100
Bright Data
Apache SystemML
100% 100%
Proxy
0% 0%
0% 0%
Python Tools
100% 100%
100% 100%
Residential Proxies
0% 0%
0% 0%
Data Science And Machine Learning
100% 100%

User comments

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

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Reviews and articles

External articles and on-site reviews we used to compare the two products.

Bright Data 4.0 · 1 review
Apache SystemML no reviews yet
  • Proxy Service Awards 2024
    proxyway.com · May 2024

    And if there’s one thing that defines Bright Data in an industry where all gaps are closing, it’s the platform. We’ve criticized it for complexity and opaqueness; but after all these years, we have to admit that...

  • Mixed feelings
    SaaSHub review
    · Mar 2024

    We used their DC proxies and Residential proxies. Resi proxies were having quite low success rate. We had to use resi solution from other proxy providers. Unblocker didn't work well either also it was way too expensive.

  • Top 10 Alternatives to Bright Data (formerly Luminati Proxy Networks)
    www.bestproxyreviews.com · Dec 2022

    Oxylabs remains the number aggressive competitor of Bright Data – they have even had a case to settle in the court in the past. If you wouldn’t want to use Bright Data proxies, then you might as well avoid Oxylabsas...

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We have no reviews of Apache SystemML yet. Be the first one to post

Social recommendations and mentions

Recommendations tracked on public social media and blogs since March 2021.

Bright Data 45 mentions
Apache SystemML 0 mentions
  • Precursor
    Happy to offer a counter of some great products for anti-bot defeat: https://brightdata.com/ https://www.zenrows.com/ https://www.capsolver.com/ https://scrapfly.io/ hundreds of millions of residential ips, human browser fingerprints,... - Source: Hacker News / about 2 months ago
  • Best Web Scraping Tools in 2026: A Hands-On Comparison of the Top 10
    The best web scraping tools 2026 leaderboard hasn't changed; the gap has narrowed. Bright Data remains the safest bet for any team that wants to spend time on the data, not on the scraping. The 660-scraper library, 400M-IP network,... - Source: dev.to / 4 months ago
  • The Economics of Web Scraping: How Consultancies Price Data Extraction and Manage Scope Creep
    Infrastructure Pass-Through (OpEx) Data extraction at scale is infrastructure-heavy. Bypassing modern Web Application Firewalls (WAFs) requires high-quality residential proxies, CAPTCHA solvers, and substantial browser-automation compute... - Source: dev.to / 5 months ago

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Tracking Apache SystemML since Mar 2021.

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