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Supabase VS Apache Spark

Compare Supabase VS Apache Spark and see what are their differences

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Supabase logo Supabase

An open source Firebase alternative

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.
  • Supabase Landing page
    Landing page //
    2023-05-27
  • Apache Spark Landing page
    Landing page //
    2021-12-31

Supabase features and specs

  • Real-time capabilities
    Supabase offers real-time database features that allow you to subscribe to database changes and sync data with your frontend seamlessly.
  • PostgreSQL foundation
    Supabase is built on PostgreSQL, a robust, mature, and highly extensible SQL database, providing strong data integrity and reliability.
  • Open-source
    Supabase is open-source, which means you can inspect, modify, and contribute to the source code. This fosters community engagement and transparency.
  • Ease of use
    Supabase provides an intuitive dashboard and auto-generated APIs, making it easy for developers to manage databases without extensive backend knowledge.
  • Authentication and Authorization
    Supabase includes pre-built authentication and authorization modules, supporting various sign-in methods like email, OAuth, and more, simplifying user management.
  • Scalability
    Supabase is designed to scale with your application, offering plans that can handle from small to large-scale traffic and data operations.

Possible disadvantages of Supabase

  • New and evolving
    As a relatively new platform, Supabase is still evolving, which means it might lack some features found in more mature solutions and could have occasional bugs or stability issues.
  • Limited integration
    Currently, Supabase has fewer third-party integrations compared to other established backend-as-a-service (BaaS) providers, which might limit its utility in diverse tech stacks.
  • Learning curve
    Despite its user-friendly interface, there could be a learning curve for those unfamiliar with PostgreSQL or real-time database concepts.
  • Pricing for advanced features
    While Supabase offers a free tier, advanced features, and higher usage plans come with a cost. This might be limiting for startups or hobby projects with tight budgets.
  • Limited geographic presence
    Supabase's infrastructure might have limited geographic data centers compared to larger cloud providers, potentially affecting latency and performance for users in certain regions.

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 Supabase

Overall verdict

  • Supabase is a strong choice for developers looking for an affordable, open-source solution to manage their application's back-end with real-time data and user authentication.

Why this product is good

  • Supabase is an open-source alternative to Firebase, providing a robust back-end platform for web and mobile applications.
  • It offers real-time capabilities, authentication, and auto-generated APIs with PostgreSQL, making it versatile and efficient.
  • The platform is developer-friendly with excellent documentation and an active community.
  • Being open-source allows for greater flexibility and control over your projects.

Recommended for

  • Developers seeking an open-source alternative to Firebase.
  • Teams that require real-time data synchronization.
  • Projects needing a scalable and easy-to-use back-end solution.
  • Individuals or teams working with PostgreSQL.

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.

Supabase videos

+ Add

Basic demo

More videos:

  • Review - Supabase in 100 Seconds by Fireship

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 Supabase and Apache Spark)

Supabase

Apache Spark

Developer Tools
100 100%
Developer Tools
0% 0
Databases
0 0%
Databases
100% 100
Realtime Backend / API
100 100%
Realtime Backend / API
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 Supabase and Apache Spark

Supabase Reviews

10 Top Firebase Alternatives to Ignite Your Development in 2024
Supabase makes it incredibly easy to migrate from Firebase. Its data structure and APIs are designed to feel familiar, so you can switch without a major learning curve. Plus, the open-source nature means you have complete control over your code and data.
Source: genezio.com
Top 7 Firebase Alternatives for App Development in 2024
Community Support and Longevity: Investigate the size and activity of the platform's community. A larger, more active community can provide better support and resources. Platforms like Parse and Supabase have strong community support.
Source: signoz.io
5 Best Vercel Alternatives for Next.js & App Router
Supabase distinguishes itself through its focus on data and community-driven development. Self-hosting capabilities allow you to deploy Supabase's suite of products within your own infrastructure. This maintains data ownership while still leveraging Supabase's tools.
Source: il.ly
Best Serverless Backend Tools of 2023: Pros & Cons, Features & Code Examples
Create an account, a project, and a database. Unlike a NoSQL database like Firebase’s, you need to have a structure ready to be able to manipulate data. But once this step is done―and you’ll have ready-to-use templates to help speed up this part―you can call Supabase like so:
Source: www.rowy.io
2023 Firebase Alternatives: Top 10 Open-Source & Free
Supabase is another trusted platform in our list that calls itself an open-source alternative to Firebase. You can also name it one of the newest cloud service providers similar to Firebase because it launched in 2020. Indeed, with great scalability and documentation support, Supabase could be an ideal option.
Source: digitalcruch.com

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, Supabase should be more popular than Apache Spark. It has been mentiond 504 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.

Supabase mentions (504)

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

Firebase - Firebase is a cloud service designed to power real-time, collaborative applications for mobile and web.

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

Next.js - A small framework for server-rendered universal JavaScript apps

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

AppWrite - Appwrite provides web and mobile developers with a set of easy-to-use and integrate REST APIs to manage their core backend needs.

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

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