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My Visual Database VS Apache Spark

Compare My Visual Database VS Apache Spark and see what are their differences

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  3. » Reviews
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My Visual Database logo My Visual Database

Using My Visual Database, you can create databases for invoicing, inventory, CRM, or any specific purpose.

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.
  • My Visual Database Landing page
    Landing page //
    2021-10-15
  • Apache Spark Landing page
    Landing page //
    2021-12-31

My Visual Database features and specs

  • User-Friendly Interface
    My Visual Database offers a graphical user interface that makes it easier for users to create and manage databases without needing extensive programming knowledge.
  • Rapid Development
    The software allows for quick application development, making it suitable for small to medium-sized projects that require fast deployment.
  • Customization
    Users have the ability to customize forms, queries, and reports, providing flexibility to adapt the database to specific needs.
  • Cost-Effective
    Being more affordable than many commercial database solutions, it offers good value for small businesses or individual developers.
  • Built-In Report Generator
    Integrated tools for creating reports directly within the application can save time and effort in generating necessary documentation.
  • Community Support
    An active community forum is available, where users can seek help and share knowledge about the software.

Possible disadvantages of My Visual Database

  • Limited Scalability
    The software may not be suitable for very large or highly complex database applications, potentially limiting its use for enterprise-level solutions.
  • Windows-Only
    My Visual Database is designed to run on Windows OS, which may not be suitable for organizations using other operating systems like macOS or Linux.
  • Limited Integrations
    There are fewer options for integrating with other third-party applications or services compared to more established database management systems.
  • Learning Curve
    Despite its graphical interface, there is still a learning curve involved, especially for users who are not familiar with database concepts.
  • Performance Issues
    Users may experience performance issues as the database size grows, affecting the speed and efficiency of operations.
  • Lack of Advanced Features
    The software lacks some advanced features available in more comprehensive database management solutions, limiting its use in more demanding applications.

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.

My Visual Database videos

+ Add

01 Lesson - simple database of employees using My Visual Database.

More videos:

  • Review - Download My Visual Database Full version Free
  • Review - 02 Lesson - creating phone reference book using My Visual Database.

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 My Visual Database and Apache Spark)

My Visual Database

Apache Spark

Databases
37 37%
Databases
63% 63
NoSQL Databases
100 100%
NoSQL Databases
0% 0
Big Data
0 0%
Big Data
100% 100
Development
100 100%
Development
0% 0

User comments

Share your experience with using My Visual Database 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 My Visual Database and Apache Spark

My Visual Database Reviews

We have no reviews of My Visual Database 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

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.

My Visual Database mentions (0)

We have not tracked any mentions of My Visual Database yet. Tracking of My Visual Database recommendations started around Mar 2021.

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 / 2 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
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What are some alternatives?

When comparing My Visual Database and Apache Spark, you can also consider the following products

Microsoft Office Access - Access is now much more than a way to create desktop databases. It’s an easy-to-use tool for quickly creating browser-based database applications.

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

LibreOffice - Base - Base, database, database frontend, LibreOffice, ODF, Open Standards, SQL, ODBC

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

MongoDB - MongoDB (from "humongous") is a scalable, high-performance NoSQL database.

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

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