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Google Cloud Dataproc VS Timeplus

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

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Note: These products don't have any matching categories. If you think this is a mistake, please edit the details of one of the products and suggest appropriate categories.
Contents:
  1. ยป Base Details
  2. ยป Videos
  3. ยป Reviews
  4. ยป Alternatives

Google Cloud Dataproc logo Google Cloud Dataproc

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

Timeplus logo Timeplus

An innovative streaming SQL database and real-time analytics platform. Fast, powerful and intuitive
  • Google Cloud Dataproc Landing page
    Landing page //
    2023-10-09
  • Timeplus Landing page
    Landing page //
    2023-02-03

Ready to turn your real-time data into actions?

Timeplus Enterprise Self-Hosting: deploy on your data center or own cloud account Timeplus Proton: open-source core engine

It empowers developers to build powerful and reliable streaming analytics applications, at speed and scale, anywhere.

Google Cloud Dataproc

Website
cloud.google.com
Pricing URL
-
$ Details
-
Platforms
-
Release Date
-
Edit details

Timeplus

Website
timeplus.com
Pricing URL
Official Timeplus Pricing
$ Details
freemium $1 / Annually (Custom Quote)
Platforms
AWS Linux
Release Date
2022 March
Startup details
Country
United States
Edit details

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.

Timeplus features and specs

  • Unified streaming and historical data process
  • Tumble, hopping, session window
  • Materialized views
  • Realtime charts, dashboards, alerts

Google Cloud Dataproc videos

+ Add

Dataproc

Timeplus videos

+ Add

Timeplus 2min demo

Category Popularity

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

Google Cloud Dataproc

Timeplus

Data Dashboard
100 100%
Data Dashboard
0% 0
Real Time
0 0%
Real Time
100% 100
Big Data
100 100%
Big Data
0% 0
Databases
0 0%
Databases
100% 100

User comments

Share your experience with using Google Cloud Dataproc and Timeplus. For example, how are they different and which one is better?
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Social recommendations and mentions

Based on our record, Google Cloud Dataproc should be more popular than Timeplus. It has been mentiond 3 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: over 3 years ago
  • Why we donโ€™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 / over 4 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 4 years ago

Timeplus mentions (1)

  • Comparing Timeplus Proton and ksqlDB for stream processing
    * Proton is more developer friendly To explore Proton yourself, visit the [Proton GitHub repo](https://github.com/timeplus-io/proton). - Source: Hacker News / over 2 years ago

What are some alternatives?

When comparing Google Cloud Dataproc and Timeplus, 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.

Materialize - A Streaming Database for Real-Time Applications

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

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

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

KSQL - Confluent KSQL is the streaming SQL engine that enables real-time data processing against Apache Kafkaยฎ.

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