Software Alternatives, Accelerators & Startups
Register | Login

.NET VS Apache Spark

Compare .NET VS Apache Spark and see what are their differences

Promptaa
Prompt organization and AI enhancement. Engineer prompts with 1 click. featured
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.
Contents:
  1. » Base Details
  2. » Videos
  3. » Reviews
  4. » Alternatives

.NET logo .NET

.NET is a free, cross-platform, open source developer platform for building many different types of applications.

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.
  • .NET Landing page
    Landing page //
    2023-09-19
  • Apache Spark Landing page
    Landing page //
    2021-12-31

.NET features and specs

  • Cross-Platform
    The .NET platform supports Windows, macOS, and Linux, which allows for the development and deployment of applications across different operating systems.
  • Performance
    ASP.NET Core, a part of the .NET ecosystem, has high-performance benchmarks and is suitable for developing scalable and high-performance systems.
  • Large Ecosystem
    .NET has a vast library of pre-built components, frameworks, and APIs that speed up development and reduce the need for writing code from scratch.
  • Strong Community Support
    There is a large and active community of developers, providing resources such as forums, documentation, and third-party tools.
  • Integrated Development Environment (IDE)
    Visual Studio, the primary IDE for .NET, offers robust features like IntelliSense, debugging, and testing tools, making development easier and more efficient.
  • Security
    .NET provides a range of security features, including code access security, role-based security, and encryption, making it a reliable choice for secure applications.
  • Compatible with Modern Development
    .NET supports modern development practices like containerization with Docker and cloud-native applications, particularly with Azure.
  • Language Support
    .NET supports multiple programming languages like C#, F#, and VB.NET, allowing developers to choose the right one for their needs.

Possible disadvantages of .NET

  • Learning Curve
    Given its vast ecosystem and feature set, .NET can have a steep learning curve for beginners.
  • Memory Usage
    .NET applications can be more memory-intensive compared to applications built with some other frameworks, which can be a concern for resource-constrained environments.
  • Platform-Specific Issues
    While .NET is cross-platform, certain platform-specific issues can arise, requiring additional work to ensure compatibility.
  • Cost of Microsoft Tools
    Although .NET is open-source, some associated tools like Visual Studio Enterprise come with significant licensing costs.
  • Smaller Talent Pool
    Compared to more universally taught languages like Python or JavaScript, finding highly skilled .NET developers can be more challenging.

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 .NET

Overall verdict

  • Yes, .NET is considered a good and reliable choice for developers due to its robust features, cross-platform capabilities, and strong community support.

Why this product is good

  • Microsoft's .NET is a versatile and powerful open-source developer platform that supports building a wide range of applications, including web, mobile, desktop, gaming, cloud, and IoT applications. It offers strong language support for languages like C#, F#, and VB.NET and provides a rich ecosystem of libraries, tools, and frameworks such as ASP.NET for web development and Xamarin for mobile development. The platform is known for its performance, security, and the ability to work seamlessly across different operating systems, including Windows, macOS, and Linux.

Recommended for

  • Enterprise applications
  • Cross-platform development
  • Web developers using ASP.NET
  • Mobile app developers using Xamarin
  • Game developers utilizing Unity

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.

.NET videos

+ Add

.NET Design Review: DataFrame

More videos:

  • Review - Truetrader.net | Loophole EXPOSED
  • Review - .NET Design Review: .NET Core 3.1

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 .NET and Apache Spark)

.NET

Apache Spark

Ad Servers
100 100%
Ad Servers
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

Share your experience with using .NET and Apache Spark. For example, how are they different and which one is better?
Log in or Post with

Reviews

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

.NET Reviews

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

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

Apache Spark might be a bit more popular than .NET. We know about 70 links to it since March 2021 and only 51 links to .NET. 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.

.NET mentions (51)

  • The Delta Difference: Unleashing .NET, EF Core, and PostgreSQL Performance with Delta
    1.Dot net is the most performant framework 2.EF Core has gotten better and provides a slew of performance steps 3.PostgreSQL is a powerful, open source object-relational database that safely stores and scales the most complicated data workloads. 4.Delta An efficient approach to implementing a 304 Not Modified leveraging DB change tracking. - Source: dev.to / 2 days ago
  • How to Build a .NET PDF Editor (Developer Tutorial)
    Editing PDF files programmatically is a common requirement in enterprise applications — whether you're modifying invoices, generating reports, or enabling users to fill and save forms. The .NET ecosystem lacks native support for advanced PDF editing, which makes third-party libraries crucial. - Source: dev.to / about 1 month ago
  • dotnet cross-platform interop with C via Environment.ProcessId system call
    Dotnet (.NET 9 is used for this article) and C# decompiler. - Source: dev.to / 3 months ago
  • Why Does Everyone Forget Java and C# for Backend Development? Why Don’t Full-Stack Developers Learn Java and C#?
    C# was developed by Microsoft in the early 2000s as part of its .NET initiative, led by Anders Hejlsberg. Originally designed as an alternative to Java, C# evolved into a powerful language for Windows applications, backend services, game development (via Unity), and cloud computing. The introduction of .NET Core made C# fully cross-platform, allowing it to run on Windows, Linux, and macOS. - Source: dev.to / 4 months ago
  • Implementing Social Authentication in .NET Web API
    This blog post details how to implement social authentication and provide users with several social login options and how we can handle the users' data obtained as a result of these logins in our application. In this blog post, we’ll look at how we can integrate Google and Facebook login authentications. We will see how this can be implemented from the server side of an application using .NET 6; Microsoft's own... - Source: dev.to / 10 months ago
View more

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

What are some alternatives?

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

WPMU DEV - WPMU offers WordPress Plugins, WordPress Themes, WordPress Multisite and BuddyPress Plugins and Themes.

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

MAMP - MAMP is the abbreviation for Macintosh, Apache, MySQL, and PHP. It is a reliable application with its four components that allows you to access the local PHP server as well as the database server (SQL).

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

Firefox Developer Edition - Built for those who build the Web. The only browser made for developers.

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

Loading...