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RAWGraphs VS Apache Spark

Compare RAWGraphs VS Apache Spark and see what are their differences

Grapple
Do-It-Yourself Data Analytics & Business Intelligence, Powered by AI featured
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Contents:
  1. ยป Base Details
  2. ยป Videos
  3. ยป Reviews
  4. ยป Alternatives

RAWGraphs logo RAWGraphs

RAWGraphs is an open source app built with the goal of making the visualization of complex data...

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.
  • RAWGraphs Landing page
    Landing page //
    2022-06-16
  • Apache Spark Landing page
    Landing page //
    2021-12-31

RAWGraphs features and specs

  • User-Friendly Interface
    RAWGraphs provides an intuitive drag and drop interface, making it accessible for users with various technical skills.
  • Open Source
    Being open source, RAWGraphs allows for customization and community contributions, promoting transparency and flexibility.
  • Supports Multiple Data Formats
    RAWGraphs supports a variety of input formats like CSV, TSV, JSON, etc., enhancing its adaptability to different data sources.
  • Extensive Visualization Types
    Offers a wide range of visualization types such as bar graphs, scatter plots, and network graphs, catering to diverse analytical needs.
  • No Installation Required
    As a web-based tool, it does not require any installation, making it easy to access and use anywhere with an internet connection.
  • Export Options
    Allows exporting visualizations in vector (SVG) and raster (PNG) formats, which is valuable for high-quality reporting and presentations.

Possible disadvantages of RAWGraphs

  • Limited Interactivity
    Visualizations created with RAWGraphs are generally static, lacking advanced interactive features found in other tools.
  • Performance with Large Datasets
    May struggle with performance issues when handling very large datasets, which can limit its use for extensive data analytics.
  • Learning Curve for Advanced Features
    While basic functionalities are user-friendly, leveraging advanced features and customizations may require a steeper learning curve.
  • Dependency on Internet
    As a web-based application, it requires an internet connection to function, which can be a limitation in restricted or offline environments.
  • Limited Data Manipulation
    Provides basic data manipulation features, but lacks the depth and complexity available in specialized data processing tools.
  • Support and Documentation
    As an open-source project, it may not have the extensive support and documentation available with commercial visualization tools.

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 RAWGraphs

Overall verdict

  • Yes, RAWGraphs is a good tool for creating data visualizations due to its ease of use, versatility, and robust support for different data types and outputs.

Why this product is good

  • RAWGraphs is considered a good data visualization tool because it is open-source, versatile, and easy to use. It allows users to create a wide variety of charts and visualizations without needing extensive coding knowledge. Its interface is intuitive and facilitates the quick transformation of data sets into visually compelling graphics. Furthermore, it supports multiple formats for data input and export, making it flexible for various project needs.

Recommended for

  • data analysts
  • journalists
  • researchers
  • educators
  • students
  • designers who need to create visualizations without in-depth coding skills.

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.

RAWGraphs videos

+ Add

RawGraphs Walkthrough

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 RAWGraphs and Apache Spark)

RAWGraphs

Apache Spark

Data Visualization
100 100%
Data Visualization
0% 0
Databases
0 0%
Databases
100% 100
Charting Libraries
100 100%
Charting Libraries
0% 0
Big Data
0 0%
Big Data
100% 100

User comments

Share your experience with using RAWGraphs and Apache Spark. For example, how are they different and which one is better?
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Reviews

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

RAWGraphs Reviews

Top 10 Tableau Open Source Alternatives: A Comprehensive List
RAWGraphs is an open-source Data Visualization tool designed to make visualizing complex data simple for everyone. The primary goal of RAWGraphs is to provide a tool that allows people who do not have the technical/coding expertise to create visualizations on their own. Originally designed to help graphic designers complete a set of tasks that were not available in other...
Source: hevodata.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 โ€“ 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 a lot more popular than RAWGraphs. While we know about 80 links to Apache Spark, we've tracked only 5 mentions of RAWGraphs. 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.

RAWGraphs mentions (5)

  • Interview synthesis tools?
    Go back through a second time Code themes / pull insights/ double check for keywords tag accuracy Use Dovetailโ€™s โ€œchartsโ€ to review various tags (it will show you how many tags per word in various chart options, none are great.) Export desired csvโ€™s from Dovetail Charts to free online data viz software like https://rawgraphs.io Boom. Iโ€™m sure there are better ways but thatโ€™s what I got! Source: over 4 years ago
  • What type/style of chart is this?
    Sankey is probably the most common name (after Captain Matthew Henry Phineas Riall Sankey who apparently made them to study energy flows in steam engines). But I've also heard it referred to as an alluvial diagram, for example in https://rawgraphs.io/. Source: over 4 years ago
  • Show HN: I made a data visualization desktop app
    This seems quite similar to RawGraphs: https://rawgraphs.io/ Both seem to provide a similar interface for dragging in a CSV file and constructing a chart, but RawGraphs is open-source, and can be used in the browser without installing anything (or the code can be downloaded and served locally). The main advantage of Daigo over RawGraphs seems to be that it supports publishing multiple charts as a dashboard.... - Source: Hacker News / over 4 years ago
  • [OC] Latin Americaโ€™s biggest airports had been growing steadily. With Covid, it all changed.
    Tools: Excel, Rawgraphs, Affinity Designer. Source: over 4 years ago
  • Self-hosted solution for easy data visualization?
    Take a look at https://rawgraphs.io/. Source: about 5 years ago

Apache Spark mentions (80)

  • MLOps Lifecycle: Stages, Workflow, and Best Practices
    Feature transformations should be deterministic: The same input should produce the same output when the same feature definition and configuration are applied. This is what allows training, backtesting, and live inference to remain aligned. Tools such as Pandas, Spark, or feature platforms such as Feast can be used to implement that logic. - Source: dev.to / about 2 months ago
  • 7 Free Tools for Data Pipeline Reconciliation and Cross-Source Validation
    Apache Spark provides distributed in-memory data processing and is the appropriate tool when the data set to be reconciled does not fit in a single machine's memory, or when parallelizing the comparison across a cluster would reduce runtime from hours to minutes. - Source: dev.to / 3 months ago
  • Why Apache IoTDB Is Written in Java: A Decade of Engineering Trade-offs
    When IoTDB was initiated in 2011, almost all influential distributed systems and databases were built in Java or on the JVMโ€”such as Hadoop, HBase, Spark (Scala on JVM), Cassandra, Kafka, and Flink. To integrate deeply with the big data ecosystem, choosing Java was a natural decision. - Source: dev.to / 4 months ago
  • I Scraped 47M+ Hacker News Items Into Parquet Files โ€“ Here's What I Discovered About HN's Hidden Data Patterns
    For handling even larger datasets or building production applications, Apache Spark provides excellent Parquet support with distributed processing capabilities. - Source: dev.to / 4 months ago
  • Show HN: Spark โ€“ Zero-config IoT deployment tool written in Rust
    You may want to consider renaming this project. The name "Spark" already refers to: A popular data analytics framework of the Apache Foundation: https://spark.apache.org/ A subset of the Ada programming language used for formal verification: https://learn.adacore.com/courses/intro-to-spark/chapters/01_Overview.html An Nvidia AI development system: https://www.nvidia.com/en-us/products/workstations/dgx-spark/. - Source: Hacker News / 7 months ago
View more

What are some alternatives?

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

Plotly - Low-Code Data Apps

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

D3.js - D3.js is a JavaScript library for manipulating documents based on data. D3 helps you bring data to life using HTML, SVG, and CSS.

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

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.

Apache Kafka - Apache Kafka is an open-source message broker project developed by the Apache Software Foundation written in Scala.

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