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Google Cloud Dataproc VS PostgreSQL

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

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Google Cloud Dataproc logo Google Cloud Dataproc

Managed Apache Spark and Apache Hadoop service which is fast, easy to use, and low cost

PostgreSQL logo PostgreSQL

PostgreSQL is a powerful, open source object-relational database system.
  • Google Cloud Dataproc Landing page
    Landing page //
    2023-10-09
  • PostgreSQL Landing page
    Landing page //
    2023-10-21

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.

PostgreSQL features and specs

  • Open Source
    PostgreSQL is an open-source database management system, which means it is free to use, modify, and distribute. This reduces the cost of database management for individuals and organizations.
  • ACID Compliance
    PostgreSQL is fully ACID (Atomicity, Consistency, Isolation, Durability) compliant, ensuring reliable transactions and data integrity.
  • Extensible
    PostgreSQL is highly extensible, allowing users to add custom functions, data types, and operators. This enables tailored solutions to specific requirements.
  • Advanced SQL Features
    PostgreSQL supports advanced SQL features like full-text search, JSON and XML data types, and complex queries, providing powerful tools for database operations.
  • Community Support
    There is a strong and active community around PostgreSQL, offering extensive documentation, forums, and collaborative support, which aids troubleshooting and development.
  • Multiple Indexing Techniques
    PostgreSQL offers a variety of indexing techniques such as B-tree, GIN, GiST, and BRIN, allowing for optimized query performance on various data types.
  • Cross-Platform Availability
    PostgreSQL runs on all major operating systems (Windows, MacOS, Linux, Unix), giving flexibility in deployment and development environments.

Possible disadvantages of PostgreSQL

  • Complex Configuration
    Setting up and configuring PostgreSQL can be complex and time-consuming, especially for beginners, requiring a good understanding of its parameters and best practices.
  • Heavy Resource Consumption
    PostgreSQL can be resource-intensive, consuming significant CPU and memory compared to other database systems, which may affect performance on lower-end hardware.
  • Backup and Restore Process
    The backup and restore process in PostgreSQL is not as straightforward as in some other database systems, requiring more manual intervention and understanding of tools like pg_dump and pg_restore.
  • Replication Complexity
    While PostgreSQL supports replication, setting it up can be more complex than some other databases. Advanced configurations like multi-master replication can be particularly challenging.
  • Steeper Learning Curve
    Due to its advanced features and extensive capabilities, PostgreSQL can have a steeper learning curve, making it harder for new users to get started compared to simpler database systems.
  • Less Third-Party Tool Support
    PostgreSQL has less support from third-party tools compared to more widely adopted databases like MySQL, which can limit options for auxiliary functions like administration, monitoring, and development.

Google Cloud Dataproc videos

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Dataproc

PostgreSQL videos

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Comparison of PostgreSQL and MongoDB

More videos:

  • Review - PostgreSQL Review
  • Review - MySQL vs PostgreSQL - Why you shouldn't use MySQL

Category Popularity

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

Google Cloud Dataproc

PostgreSQL

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

User comments

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

Google Cloud Dataproc Reviews

We have no reviews of Google Cloud Dataproc yet.
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PostgreSQL Reviews

Data Warehouse Tools
Peliqan acts as a bridge, allowing you to e.g. effortlessly pull your PostgreSQL data into Google Sheets for easy access and analysis using its one-click connector. Additionally, Peliqan’s platform provides a user-friendly environment for data exploration, transformation with Magical SQL, and visualization capabilities, all without needing to switch between multiple tools.
Source: peliqan.io
Top 5 BigQuery Alternatives: A Challenge of Complexity
For over three decades, the open-source object-relational database system PostgreSQL has maintained its reputation as a top SQL server due to its features, performance, and reliability. (Heck, Redshift is even based on Postgres!) It's the go-to database solution for large corporations and organizations across a variety of industries from ecommerce to gaming to...
Source: blog.panoply.io
10 Best Database Management Software Of 2022 [+ Examples]
Applications Manager offers out-of-the-box health and performance monitoring for 20 popular databases including RDBMS, NoSQL, in-memory, distributed, and big data stores. It supports both commercial databases such as Oracle, Microsoft SQL, IBM DB2, and MongoDB as well as open source ones like MySQL and PostgreSQL.
Source: theqalead.com
ClickHouse vs TimescaleDB
Recently, TimescaleDB published a blog comparing ClickHouse & TimescaleDB using timescale/tsbs, a timeseries benchmarking framework. I have some experience with PostgreSQL and ClickHouse but never got the chance to play with TimescaleDB. Some of the claims about TimescaleDB made in their post are very bold, that made me even more curious. I thought it’d be a great...
Source: pradeepchhetri.xyz
9 Best MongoDB alternatives in 2019
PostgreSQL is a widely popular open source database management system. It provides support for both SQL for relational and JSON for non-relational queries.
Source: www.guru99.com

Social recommendations and mentions

Based on our record, PostgreSQL should be more popular than Google Cloud Dataproc. It has been mentiond 16 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.

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 / almost 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

PostgreSQL mentions (16)

  • Convert insert mutation to upsert
    In this quick post, we’ll walk through implementing an Upsert operation in Hasura using PostgreSQL and GraphQL. - Source: dev.to / 8 months ago
  • Perfect Elixir: Environment Setup
    I’m on MacOS and erlang.org, elixir-lang.org, and postgresql.org all suggest installation via Homebrew, which is a very popular package manager for MacOS. - Source: dev.to / about 1 year ago
  • Rust & MySQL: connect, execute SQL statements and stored procs using crate sqlx.
    According to the documentation, crate sqlx is implemented in Rust, and it's database agnostic: it supports PostgreSQL, MySQL, SQLite, and MSSQL. - Source: dev.to / over 1 year ago
  • Really tired. Is PostgreSQL even runnable in Windows 10? pgAdmin4 stucks at Loading whatever I try.
    Solution is just downloading and installilng pgAdmin from official pgAdmin homepage version, not the one that is included in the postgresql.org package. Source: almost 2 years ago
  • Why SQL is right for Infrastructure Management
    SQL immediately stands out here because it was designed for making relational algebra, the other side of the Entity-Relationship model, accessible. There are likely more people who know SQL than any programming language (for IaC) or data format you could choose to represent your cloud infrastructure. Many non-programmers know it, as well, such as data scientists, business analysts, accountants, etc, and there is... - Source: dev.to / about 2 years ago
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What are some alternatives?

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

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

MySQL - The world's most popular open source database

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

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.

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

SQLite - SQLite Home Page

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