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Google Cloud Dataproc VS SQLite

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

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  3. » Reviews
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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

SQLite logo SQLite

SQLite Home Page
  • Google Cloud Dataproc Landing page
    Landing page //
    2023-10-09
  • SQLite 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.

SQLite features and specs

  • Zero Configuration
    SQLite does not require any server setup or configuration, allowing for easy integration and deployment in applications.
  • Lightweight
    It is extremely lightweight, with a small footprint, making it ideal for embedded systems and mobile applications.
  • Self-Contained
    SQLite is self-contained, meaning it has minimal external dependencies, which simplifies its distribution and usage.
  • File-Based Storage
    Data is stored in a single file, which makes it easy to manage and transfer databases as simple files.
  • ACID Compliance
    SQLite supports Atomicity, Consistency, Isolation, and Durability (ACID) properties, ensuring reliable transactions.
  • Cross-Platform
    SQLite is available on numerous platforms, including Windows, MacOS, Linux, iOS, and Android, providing a broad compatibility range.
  • Public Domain
    SQLite operates under the public domain, allowing for unrestricted use in commercial and non-commercial applications.

Possible disadvantages of SQLite

  • Limited Scalability
    SQLite is not designed to handle high levels of concurrency and large-scale databases, making it less suitable for large, high-traffic applications.
  • Write Performance
    Write operations can be slower compared to server-based databases, especially under heavy write loads.
  • Lack of Certain Features
    SQLite lacks some advanced features offered by other RDBMS like stored procedures, user-defined functions, and full-text search indexing.
  • Security
    As SQLite is file-based, it might lack some of the security features present in server-based databases, such as sophisticated access control.
  • Concurrency
    SQLite uses a locking mechanism to control access to the database, which can lead to contention and performance bottlenecks in highly concurrent environments.
  • Backup and Restore
    While it's straightforward to copy SQLite database files, it lacks the advanced backup and restore features found in more complex RDBMS.

Google Cloud Dataproc videos

+ Add

Dataproc

SQLite videos

+ Add

SQLite | What, Why , Where

More videos:

  • Review - W20 PROG1442 3.3 UWP sqLite Review
  • Tutorial - How To Create SQLite Databases From Scratch For Beginners - Full Tutorial

Category Popularity

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

Google Cloud Dataproc

SQLite

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 SQLite. For example, how are they different and which one is better?
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Social recommendations and mentions

Based on our record, SQLite should be more popular than Google Cloud Dataproc. It has been mentiond 18 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 / 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

SQLite mentions (18)

  • Can I have my Lightroom catalogue pointing at two sources...?
    Yes. A Lightroom catalog file is, after all, just a SQLite database. (Srsly, make a copy of your catalog file, rename it whatever.sqlite and use your favorite SQLite GUI to rip it open and look at the tables and fields). It's just storing the pathame to the RAW file for that file's record in the database. Source: almost 2 years ago
  • Building a database to search Excel files
    I use visidata with a playback script I recorded to open the sheet to a specific Excel tab, add a column, save the sheet as a csv file. Then I have a sqlite script that takes the csv file and puts it in a database, partitioned by monthYear. Source: about 2 years ago
  • Saw this on my friends Snapchat story, this hurts my heart
    Use the most-used database in the world: https://sqlite.org/index.html. Source: over 2 years ago
  • "Managing" a SQLite Database with J (Part 2)
    With this in mind, I wrote a few versions of this post, but I hated them all. Then I realized that jodliterate PDF documents mostly do what I want. So, instead of rewriting MirrorXref.pdf, I will make a few comments about jodliterate group documents in general. If you're interested in using SQLite with J, download the self-contained GitHub files MirrorXref.ijs and MirrorXref.pdf and have a look. - Source: dev.to / almost 3 years ago
  • "Managing" a SQLite Database with J (Part 1)
    SQLite, by many estimates, is the most widely deployed SQL database system on Earth. It's everywhere. It's in your phone, your laptop, your cameras, your car, your cloud, and your breakfast cereal. SQLite's global triumph is a gratifying testament to the virtues of technical excellence and the philosophy of "less is more.". - Source: dev.to / almost 3 years ago
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What are some alternatives?

When comparing Google Cloud Dataproc and SQLite, 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.

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

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

MySQL - The world's most popular open source database

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

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.

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