Software Alternatives, Accelerators & Startups

Datahike VS Cloudback.it

Compare Datahike VS Cloudback.it and see what are their differences

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.

Datahike logo Datahike

A durable datalog database adaptable for distribution.

Cloudback.it logo Cloudback.it

Cloudback secures your GitHub repositories with recurrent data backup. With Cloudback you can use your favorite cloud storage provider or a built-in one.
  • Datahike Landing page
    Landing page //
    2023-08-22
  • Cloudback.it
    Image date //
    2024-03-04

Features

We offer the most comprehensive backup on the market. Cloudback lets you store all the information you need to restore the entire repository in the event of a disaster. Back up the GitHub repository code, issues, labels, comments, milestones, etc.

Flexible schedule

You can set up a flexible schedule for each repository. Cloudback then automatically backs up the repositories according to the schedule. You can also manually trigger a backup at any time.

Customer-managed storage providers

  • Microsoft Azure Blob Storage
  • Microsoft OneDrive
  • Amazon S3
  • Google Cloud Storage
  • Alibaba Cloud Object Storage
  • OpenStack Swift

Cloudback-managed storages

  • US, EU, UK, Sidney, Singapore

Data deduplication

Reduce storage costs while using your own storage. Learn more

And more

Learn more about Cloudback features in our documentation

Datahike

Website
github.com
Pricing URL
-
$ Details
-
Platforms
-
Release Date
-

Cloudback.it

$ Details
freemium $10.0 / Monthly (per repository monthly)
Platforms
GitHub AWS Azure Google Cloud Alibaba Cloud OpenStack Swift Wasabi GitLab
Release Date
2020 October

Datahike features and specs

  • Persistence
    Datahike is a persistent database, which means that it retains data across sessions and can be relied upon for storage that survives application restarts.
  • Datalog queries
    Datahike supports Datalog queries, a powerful and expressive query language that is similar to Prolog, allowing for complex querying of data relationships.
  • Schema flexibility
    Datahike provides schema flexibility that allows developers to define and evolve their data models without needing to perform migrations. This can significantly speed up development.
  • Immutable data structures
    By utilizing immutable data structures, Datahike allows safe concurrent reads and writes, reducing the risk of data corruption and improving application stability.
  • Transactional support
    Datahike offers ACID-compliant transactions, ensuring data integrity and consistent state even in the face of concurrent operations.
  • Integration with Datomic API
    Datahike is designed to be compatible with the Datomic API, making it easier for developers familiar with Datomic to transition and leverage their knowledge.
  • Off-the-shelf scalability
    The architecture of Datahike is conducive to scaling horizontally, providing flexibility to handle growing amounts of data and user load.

Possible disadvantages of Datahike

  • Relatively new ecosystem
    Being a lesser-known and newer alternative compared to databases like Datomic, Datahike may have a smaller community and fewer resources like documentation and third-party integrations.
  • Performance limitations
    While Datahike is designed to be lightweight and flexible, it may not match the performance of more mature databases, especially in very high-load or high-volume scenarios.
  • Limited features
    Datahike may lack some advanced features present in other databases, such as sophisticated indexing or native support for certain types of analytics, which could be necessary for specific applications.
  • Java Virtual Machine (JVM) requirement
    As it runs on the JVM, Datahike requires a Java runtime environment, which might not be ideal or convenient for projects seeking to minimize dependencies or employ lightweight deployment strategies.

Cloudback.it features and specs

  • Automated backups
    Scheduled GitHub repository backups
  • Configurable schedule
    Various backup schedules supported
  • Cloud storage providers
    Azure, AWS S3, AWS Glacier, Google Cloud, Alibaba Cloud, built-in Cloudback storage
  • Easy to use
    On click setup
  • Microsoft Azure Blob Container support
    Use your own Microsoft Azure Blob Container to save backups
  • Amazon S3 Bucket support
    Use your own Amazon S3 Bucket to save backups
  • Amazon S3 Glacier support
    Use your own Amazon S3 Glacier to save backups
  • Google Cloud Storage Bucket support
    Use your own Google Cloud Storage Bucket to save backups
  • Alibaba Cloud Object Storage Service support
    Use your own Alibaba Cloud Object Storage Service to save backups
  • Metadata backups
    The backups include the repository metadata such as issues, labels, milestones, and comments
  • Download backup
    Download entire backup: repository and metadata
  • Restore backups
    Easy to restore the backup as a new GitHub repository

Category Popularity

0-100% (relative to Datahike and Cloudback.it)
Databases
100 100%
0% 0
GitHub
0 0%
100% 100
NoSQL Databases
100 100%
0% 0
Backup And Disaster Recovery

Questions & Answers

As answered by people managing Datahike and Cloudback.it.

What makes your product unique?

Cloudback.it's answer:

Simplicity and user-friendliness, from the UX to the backup archive.

Why should a person choose your product over its competitors?

Cloudback.it's answer:

1) Pricing policy - per repository, not per seats. 2) User-friendly backup archives. End user can take an archive and restore even manually. 3) Easy to use, the simplified UI and UX, without heavy enterprise-focused routines (but still all features are in place).

Which are the primary technologies used for building your product?

Cloudback.it's answer:

It is Azure-hosted .NET core application, built with security in mind.

User comments

Share your experience with using Datahike and Cloudback.it. For example, how are they different and which one is better?
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Social recommendations and mentions

Based on our record, Datahike should be more popular than Cloudback.it. It has been mentiond 6 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.

Datahike mentions (6)

  • What if database branching was easy?
    It appears that Datahike [0] is a Datomic workalike that supports branching. I havenโ€™t tried it out myself (yet), but the documentation suggests itโ€™s possible [1]. That said, Iโ€™m adding xitdb to the list of tech to try out. Thank you for building it! Oh, and thanks for linking to my article :-) [0]: https://github.com/replikativ/datahike [1]: https://datahike.io/notes/the-git-model-for-databases/. - Source: Hacker News / 4 months ago
  • Show HN: Stratum โ€“ SQL that branches and beats DuckDB on 35/46 1T benchmarks
    Hey. Hybrid in which sense? I have integrated Stratum's columnar indices as a secondary index in the new query engine of https://github.com/replikativ/datahike itself, so for numerical data you will be able to use Datalog/SQL to have combined (OLTP, OLAP, ...) processing. Same for proximum (persistent HNSW vector index) and scriptum (persistent Lucene). Stratum already can be copy-on-write updated online with... - Source: Hacker News / 5 months ago
  • The Ten Rules of Schema Growth
    Datahike [0] provides similar functionality to datomic and is open source. It lacks some features however that Datomic does have [1]. [0]: https://github.com/replikativ/datahike. - Source: Hacker News / almost 3 years ago
  • Is Datomic right for my use case?
    You can also consider other durable Datalog options like datahike or datalevin which can work either as lib (SQLite style) or in a client-server setup; if you want to play with bi-temporality XTDB is a rock solid option with very good support and documentation. Source: about 3 years ago
  • Max Datom: Interactive Datomic Tutorial
    Oh really interesting. I didn't know about that. I was actually going threw the old Mendat code base and was considering using that. I would really like a pure Rust version of Datomic for embed use cases. There is all also Datahike, that is going in that direction too. It is maintained and actively developed. https://github.com/replikativ/datahike. - Source: Hacker News / over 4 years ago
View more

Cloudback.it mentions (1)

  • I have fucked up please please helpp
    If you were using GitHub Desktop, then the file should be in the recycle bin. See this SO answer for more details. You can also try using git restore command. Finally, you can use backup tools like Cloudback to protect your repositories in the future. This will not help you to restore the local changes from your computer, but still can be useful in case of any wrong changes done to your repository. Source: over 3 years ago

What are some alternatives?

When comparing Datahike and Cloudback.it, you can also consider the following products

Datomic - The fully transactional, cloud-ready, distributed database

GitProtect.io - Fully manageable, most professional GitHub and Bitbucket backup

MarkLogic Server - MarkLogic Server is a multi-model database that has both NoSQL and trusted enterprise data management capabilities.

Amazon S3 - Amazon S3 is an object storage where users can store data from their business on a safe, cloud-based platform. Amazon S3 operates in 54 availability zones within 18 graphic regions and 1 local region.

Valentina Server - Valentina Server is 3 in 1: Valentina DB Server / SQLite Server / Report Server

Active@ Disk Image - Data Backup Software helps you to backup whole disks and partitions and to restores them when needed