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Apache Spark VS Superset

Compare Apache Spark VS Superset 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.

Superset logo Superset

Superset is a platform that enables colleges to automate campus placements end-to-end, helps employers hire young talent from across colleges in the country, and empowers students to access opportunities democratically.
  • Apache Spark Landing page
    Landing page //
    2021-12-31
  • Superset Landing page
    Landing page //
    2023-07-21

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.

Superset features and specs

  • User-Friendly Interface
    Superset provides a user-friendly and intuitive interface, which makes it easy for users to create and share dashboards without the need for extensive technical expertise.
  • Open Source
    As an open-source platform, Superset allows customization and integration with other tools and systems, providing flexibility and the ability to tailor the tool to specific organizational needs.
  • Extensive Visualization Options
    Superset offers a wide range of visualization options, including bar charts, line graphs, pie charts, and more, enabling users to effectively present data in various formats.
  • Scalability
    Designed to handle large volumes of data, Superset can scale effectively, making it a suitable choice for organizations with growing data analysis needs.
  • SQL Editor
    The included SQL editor allows for direct querying of databases, providing experienced users the ability to perform complex data analyses and transformations.

Possible disadvantages of Superset

  • Steep Learning Curve
    Despite its user-friendly design, new users may face a steep learning curve, particularly if they are unfamiliar with SQL or data visualization principles.
  • Performance Issues
    Some users have reported performance issues, especially with very large datasets or complex queries, which can hinder effective data analysis.
  • Limited Advanced Analytics
    While Superset is great for basic visualizations and dashboarding, it lacks more advanced analytics features that other BI tools might offer, such as predictive analytics or machine learning integrations.
  • Requires Technical Expertise
    Setting up and maintaining Superset can require significant technical expertise, particularly for installation, configuration, and integrating with data sources.
  • Community Support
    Being an open-source project, support is reliant predominantly on community forums and documentation, which might not be as comprehensive or timely as commercial support services.

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

Superset videos

+ Add

SUPERSET PRE WORKOUT REVIEW | BEYOND YOURSELF

More videos:

  • Review - Open Source Data Visualization: What is Apache Superset? Part 2/2 - Why / when to use Superset
  • Review - FITNESS CREATORS NEED TO CHECK THIS OUT! - SuperSet Review

Category Popularity

0-100% (relative to Apache Spark and Superset)

Apache Spark

Superset

Databases
100 100%
Databases
0% 0
Business Intelligence
0 0%
Business Intelligence
100% 100
Big Data
100 100%
Big Data
0% 0
Data Dashboard
0 0%
Data Dashboard
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 Superset

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

Superset Reviews

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

Social recommendations and mentions

Based on our record, Apache Spark seems to be a lot more popular than Superset. While we know about 70 links to Apache Spark, we've tracked only 1 mention of Superset. 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 1 month 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 1 month 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 / 3 months ago
View more

Superset mentions (1)

  • Revolutionize Your Campus Recruitment with Superset's Advanced Software Solutions
    Superset's virtual campus placement and campus hiring software redefines the landscape of campus recruitment. By embracing automation, optimizing processes, and fostering meaningful connections, Superset empowers colleges and employers to embark on a transformative journey towards successful campus placements and strategic hiring of young talent. Elevate your campus recruitment game with Superset's advanced... Source: over 1 year ago

What are some alternatives?

When comparing Apache Spark and Superset, 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.

Tableau - Tableau can help anyone see and understand their data. Connect to almost any database, drag and drop to create visualizations, and share with a click.

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

Metabase - Metabase is the easy, open source way for everyone in your company to ask questions and learn from...

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

Microsoft Power BI - BI visualization and reporting for desktop, web or mobile

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