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DesignRevision VS Apache Spark

Compare DesignRevision VS Apache Spark and see what are their differences

Flagsmith
Flagsmith lets you manage feature flags and remote config across web, mobile and server side applications. Deliver true Continuous Integration. Get builds out faster. Control who has access to new features. We're Open Source. featured
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DesignRevision logo DesignRevision

Powerful tools for web professionals

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.
Not present
  • Apache Spark Landing page
    Landing page //
    2021-12-31

DesignRevision features and specs

  • Rich UI Components
    DesignRevision offers a wide variety of UI components, including buttons, forms, tables, and cards, which can save developers considerable time and effort in designing and implementing their UI.
  • Pre-built Templates
    The platform provides a selection of pre-built templates that can be easily customized. This helps in quickly prototyping or developing applications, especially useful for beginners or time-constrained projects.
  • Documentation
    Extensive documentation is available, which helps in understanding how to use various components, templates, and overall design principles. This is useful for both novices and experienced developers.
  • Customization Options
    The components and templates are highly customizable to fit the specific needs and branding requirements of a project. This flexibility enhances the utility of DesignRevision for a variety of projects.
  • Bootstrap-Compatible
    DesignRevision's components are compatible with Bootstrap, one of the most popular CSS frameworks. This ensures easy integration with existing projects that already use Bootstrap.

Possible disadvantages of DesignRevision

  • Cost
    While some resources on DesignRevision are free, full access to all templates and components comes at a cost. This could be a barrier for hobbyists, small businesses, or individual developers with limited budgets.
  • Learning Curve
    Despite the extensive documentation, there is still a learning curve involved in understanding and integrating the components effectively into projects, especially for those new to front-end development.
  • Limited Niche Components
    While the platform offers a wide range of general UI components, it may lack niche or specialized components that are sometimes required for specific business needs.
  • Dependency on Bootstrap
    Though compatibility with Bootstrap is generally a pro, it can also be a con for developers who prefer or are required to use a different framework, as this limits flexibility.
  • Performance Overhead
    Using a vast number of modular components can sometimes lead to performance overhead, especially in larger applications. This requires careful planning and optimization.

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 DesignRevision

Overall verdict

  • Yes, DesignRevision is generally considered a good resource for design professionals and enthusiasts. It offers functional and aesthetically pleasing UI kits that can significantly aid in web design projects.

Why this product is good

  • DesignRevision is well-regarded for offering high-quality design resources and UI kits that are versatile and easy to use. Their products are known for being responsive and customizable, catering to the needs of both novice and experienced designers. The site also provides comprehensive documentation and support, making it a reliable choice for users looking to streamline their design process.

Recommended for

    DesignRevision is recommended for web designers, UI/UX developers, and startups looking for cost-effective and time-efficient design resources. It is particularly beneficial for those who need ready-made, high-quality design components that can be easily integrated into various projects.

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.

DesignRevision videos

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Apache Spark videos

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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 DesignRevision and Apache Spark)

DesignRevision

Apache Spark

Design Tools
100 100%
Design Tools
0% 0
Databases
0 0%
Databases
100% 100
Developer Tools
100 100%
Developer Tools
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 DesignRevision and Apache Spark

DesignRevision 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 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.

DesignRevision mentions (0)

We have not tracked any mentions of DesignRevision yet. Tracking of DesignRevision recommendations started around Nov 2022.

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 DesignRevision and Apache Spark, you can also consider the following products

Mockuuups Studio - Fast and easy way to create product mockups on macOS, Windows and Linux.

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

lstore.graphic - Mockup Scene Creator

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

Flatlogic - AI-Powered Software Development for Startups and Businesses

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

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