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MySQL VS Google Cloud Dataproc

Compare MySQL VS Google Cloud Dataproc and see what are their differences

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Contents:
  1. » Base Details
  2. » Videos
  3. » Reviews
  4. » Alternatives

MySQL logo MySQL

The world's most popular open source database

Google Cloud Dataproc logo Google Cloud Dataproc

Managed Apache Spark and Apache Hadoop service which is fast, easy to use, and low cost
  • MySQL Landing page
    Landing page //
    2022-06-17
  • Google Cloud Dataproc Landing page
    Landing page //
    2023-10-09

MySQL features and specs

  • Reliability
    MySQL is known for its reliability and durability, making it a solid choice for many businesses' database management needs.
  • Performance
    It offers robust performance, handling large databases and complex queries efficiently.
  • Open Source
    MySQL is an open-source database, making it freely available under the GNU General Public License (GPL).
  • Scalability
    MySQL supports large-scale applications and can handle high volumes of transactions.
  • Community Support
    There is a large, active MySQL community that offers extensive resources, documentation, and support.
  • Cross-Platform
    MySQL is compatible with various operating systems like Windows, Linux, and macOS.
  • Integrations
    MySQL integrates well with numerous development frameworks, including LAMP (Linux, Apache, MySQL, PHP/Python/Perl).
  • Security
    MySQL offers various security features, such as user account management, password policies, and encrypted connections.
  • Cost
    The open-source nature of MySQL means that it can be very cost-effective, especially for small to medium-sized businesses.

Possible disadvantages of MySQL

  • Support
    While community support is plentiful, official support from Oracle can be quite expensive.
  • Complexity
    More advanced features and configurations can be complex and may require a steep learning curve for new users.
  • Scalability Limitations
    While MySQL is scalable, very high-scale applications may run into limitations compared to some newer database technologies.
  • Plug-in Storage Engines
    The use of plug-in storage engines like InnoDB or MyISAM can cause inconsistencies and complicate backups and recovery processes.
  • ACID Compliance
    Although MySQL supports ACID compliance, certain configurations or storage engines may not fully adhere to ACID properties, affecting transaction reliability.
  • Concurrent Writes
    Handling a high number of concurrent writes can be less efficient compared to some other database systems designed specifically for high concurrency.
  • Feature Set
    Some advanced features found in other SQL databases (e.g., full-text indexing, rich analytics) may be less robust or absent.
  • Vendor Dependency
    With Oracle now owning MySQL, there can be concerns about licensing changes or other forms of vendor lock-in.
  • Replication Complexities
    Setting up replication and ensuring data consistency across distributed systems can be complex and error-prone.

Google Cloud Dataproc features and specs

  • Managed Service
    Google Cloud Dataproc is a fully managed service, which reduces the complexity of deploying, managing, and scaling big data clusters like Hadoop and Spark.
  • Integration with Google Cloud
    Seamlessly integrates with other Google Cloud services like Google Cloud Storage, BigQuery, and Google Cloud Pub/Sub, allowing for easy data handling and processing.
  • Scalability
    Can quickly scale resources up or down to meet the computing demands, making it flexible for different workload sizes and types.
  • Cost Efficiency
    Offers a pay-as-you-go pricing model, and can utilize preemptible VMs for reduced costs, making it a cost-effective option for running big data workloads.
  • Customizability
    Supports custom image management and initialization actions, allowing users to tailor clusters to meet specific needs.

Possible disadvantages of Google Cloud Dataproc

  • Complex Pricing
    Understanding and predicting costs can be challenging due to various pricing factors like cluster size, usage duration, and types of instances used.
  • Learning Curve
    Dataproc requires familiarity with Google Cloud and big data tools, which may present a steep learning curve for beginners.
  • Limited Customization Compared to Self-Managed
    While customizable, it may not offer as much flexibility and control as self-managed on-premises solutions, which can be limiting for highly specialized configurations.
  • Dependency on Google Cloud Ecosystem
    As a Google Cloud service, users are somewhat locked into the Google ecosystem, which may not be ideal for those using a multi-cloud strategy.
  • Potential Latency for Large Data Transfers
    Transferring large datasets between Dataproc and other services, especially across regions, might introduce latency issues.

MySQL videos

+ Add

MySQL IN 10 MINUTES (2020) | Introduction to Databases, SQL, & MySQL

More videos:

  • Review - A Review of MySQL Open Source Software

Google Cloud Dataproc videos

+ Add

Dataproc

Category Popularity

0-100% (relative to MySQL and Google Cloud Dataproc)

MySQL

Google Cloud Dataproc

Databases
100 100%
Databases
0% 0
Data Dashboard
0 0%
Data Dashboard
100% 100
Relational Databases
100 100%
Relational Databases
0% 0
Big Data
0 0%
Big Data
100% 100

User comments

Share your experience with using MySQL and Google Cloud Dataproc. 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 MySQL and Google Cloud Dataproc

MySQL Reviews

MariaDB Vs MySQL In 2019: Compatibility, Performance, And Syntax
MySQL: MySQL is an open-source relational database management system (RDBMS). Just like all other relational databases, MySQL uses tables, constraints, triggers, roles, stored procedures and views as the core components that you work with. A table consists of rows, and each row contains a same set of columns. MySQL uses primary keys to uniquely identify each row (a.k.a...
Source: blog.panoply.io
20+ MongoDB Alternatives You Should Know About
MySQL® is another feasible replacement. MySQL 5.7 and MySQL 8 have great support for JSON, and it continues to get better with every maintenance release. You can also consider MySQL Cluster for medium size sharded environments. You can also consider MariaDB and Percona Server for MySQL
Source: www.percona.com

Google Cloud Dataproc Reviews

We have no reviews of Google Cloud Dataproc yet.
Be the first one to post

Social recommendations and mentions

MySQL might be a bit more popular than Google Cloud Dataproc. We know about 4 links to it since March 2021 and only 3 links to Google Cloud Dataproc. 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.

MySQL mentions (4)

  • I have a recurring issue with a MySQL DB where I continually run out of disk space due to logs being filled. I've tried everything I can think of. Can anyone think of anything else I should try?
    So, I did a quick read through the mysql reference and found a bunch of flush related commands. I tried:. Source: almost 2 years ago
  • MMORPG design resources
    MySQL: Any SQL or DB knock-off, really... mysql.com - mariadb.org - sqlite.org. Source: over 2 years ago
  • Probably a syntax error
    15 years and five strokes ago. I was a Unix sysadmin. ALthough I was never an actual programmer, I did maintenance/light enhancement for the organization's website, in php. Now, as self-administered cognative therapy, I'm going back to it. This is an evil HR application that uses the mysql.com employees sample database. The module below enables the evil HR end user to generate a list of the oldest workers so... Source: almost 4 years ago
  • An absolute nightmare with mysql 8.0.25
    I always use the packages from mysql.com, that way I don't have to deal with strange configuration stuff along those lines, but anyway, I'm afraid I'm out of ideas. Surely someone else would have run in to the same issue here though. Source: almost 4 years ago

Google Cloud Dataproc mentions (3)

  • Connecting IPython notebook to spark master running in different machines
    I have also a spark cluster created with google cloud dataproc. Source: about 2 years ago
  • Why we don’t use Spark
    Specifically, we heavily rely on managed services from our cloud provider, Google Cloud Platform (GCP), for hosting our data in managed databases like BigTable and Spanner. For data transformations, we initially heavily relied on DataProc - a managed service from Google to manage a Spark cluster. - Source: dev.to / about 3 years ago
  • Data processing issue
    With that, the best way to maximize processing and minimize time is to use Dataflow or Dataproc depending on your needs. These systems are highly parallel and clustered, which allows for much larger processing pipelines that execute quickly. Source: over 3 years ago

What are some alternatives?

When comparing MySQL and Google Cloud Dataproc, you can also consider the following products

PostgreSQL - PostgreSQL is a powerful, open source object-relational database system.

Amazon EMR - Amazon Elastic MapReduce is a web service that makes it easy to quickly process vast amounts of data.

Microsoft SQL - Microsoft SQL is a best in class relational database management software that facilitates the database server to provide you a primary function to store and retrieve data.

Google BigQuery - A fully managed data warehouse for large-scale data analytics.

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

HortonWorks Data Platform - The Hortonworks Data Platform is a 100% open source distribution of Apache Hadoop that is truly...

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