dataproc-templates | Dataproc Serverless templates and pipelines for solving simple in-Cloud data tasks | Continuous Backup library

by GoogleCloudPlatform Java Version: v0.3.0-beta License: Apache-2.0

X-Ray Key Features Code Snippets Community Discussions(3)Vulnerabilities Install Support

kandi X-RAY | dataproc-templates Summary

dataproc-templates is a Java library typically used in Backup Recovery, Continuous Backup applications. dataproc-templates has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. However dataproc-templates build file is not available. You can install using 'pip install dataproc-templates' or download it from GitHub, PyPI.

Dataproc templates are an effort to solve simple, but large, in-Cloud data tasks, including data import/export/backup/restore and bulk API operations. The technology under the hood which makes these operations possible is the serverless spark functionality based on Google Cloud Dataproc service. Google is providing this collection of pre-implemented Dataproc templates as a reference and to provide easy customization for developers wanting to extend their functionality.

Support

Quality

Security

License

Reuse

Support

dataproc-templates has a low active ecosystem.

It has 87 star(s) with 64 fork(s). There are 18 watchers for this library.

It had no major release in the last 12 months.

There are 70 open issues and 331 have been closed. On average issues are closed in 75 days. There are 6 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of dataproc-templates is v0.3.0-beta

Quality

dataproc-templates has no bugs reported.

Security

dataproc-templates has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

dataproc-templates is licensed under the Apache-2.0 License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

dataproc-templates releases are available to install and integrate.

Deployable package is available in PyPI.

dataproc-templates has no build file. You will be need to create the build yourself to build the component from source.

Installation instructions are not available. Examples and code snippets are available.

Top functions reviewed by kandi - BETA

kandi's functional review helps you automatically verify the functionalities of the libraries and avoid rework.
Currently covering the most popular Java, JavaScript and Python libraries. See a Sample of dataproc-templates

Get all kandi verified functions for this library.

dataproc-templates Key Features

No Key Features are available at this moment for dataproc-templates.

dataproc-templates Examples and Code Snippets

No Code Snippets are available at this moment for dataproc-templates.

Community Discussions

Trending Discussions on Continuous Backup

How to disable azure cosmos db continious backup

Consistency of Continuous backup of Azure Cosmos DB

Mongo atlas recommends cloud provider snaphots for backup - Is it effective?

QUESTION

How to disable azure cosmos db continious backup

Asked 2022-Feb-22 at 10:59

I enabled the Azure cosmos DB continuous backup for one of my Cosmos DBs.
How can I disable it? It just says you have successfully enrolled in continuous backup.

...

ANSWER

Answered 2022-Feb-22 at 10:59

I am not sure if you have seen this message in the portal when you created the account/also mentioned in the doc

"You will not be able to switch between the backup policies after the account has been created"

since you need to select either "Periodic" or "Continuous" at the creation of Cosmos Account, it becomes mandatory.

Update:

You will not see the above in portal anymore, you can Switch from "Periodic" to "Continous" on an existing account and that cannot be reverted. You can read more here.

Source https://stackoverflow.com/questions/69347197

QUESTION

Consistency of Continuous backup of Azure Cosmos DB

Asked 2021-Nov-25 at 17:15

What would be the consistency of the continuous backup of the write region if the database is using bounded staleness consistency? Will it be equivalent to strong consistent data assuming no failovers happened?

Thanks Guru

...

ANSWER

Answered 2021-Nov-25 at 17:15

Backups made from any secondary region will have data consistency defined by the guarantees provided by the consistency level chosen. In the case of strong consistency, all secondary region backups will have completely consistent data.

Bounded staleness will have data that may have stale or inconsistent data inside the defined staleness window (minimum 300 seconds or 100k writes). Outside of that staleness window the data will be consistent.

Data for the weaker consistency levels will have no guarantees for consistency from backups in secondary regions.

Source https://stackoverflow.com/questions/70099953

QUESTION

Mongo atlas recommends cloud provider snaphots for backup - Is it effective?

Asked 2020-May-19 at 10:12

MongoDB has deprecated the continuous back up of data. It has recommended using CPS (Cloud provider snapshots). As far as I understood, snapshots isn't really going to be effective compared to continuous backup coz, if system breaks, then we can only be able to restore the data till the previous snapshot which isn't gonna make the database up-to-date or close to it atleast.

Am I missing something here in my understanding?

...

ANSWER

Answered 2020-May-19 at 10:12

Cloud provider snapshots can be combined with point in time restore to give the recovery point objective you require. With oplog based restores you can get granularity of one second.

Source https://stackoverflow.com/questions/61886736

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install dataproc-templates

You can install using 'pip install dataproc-templates' or download it from GitHub, PyPI.
You can use dataproc-templates like any standard Java library. Please include the the jar files in your classpath. You can also use any IDE and you can run and debug the dataproc-templates component as you would do with any other Java program. Best practice is to use a build tool that supports dependency management such as Maven or Gradle. For Maven installation, please refer maven.apache.org. For Gradle installation, please refer gradle.org .