Alternatives to Datomic Cloud logo

Alternatives to Datomic Cloud

Amazon DynamoDB, Cloud Firestore, Azure Cosmos DB, Google Cloud Datastore, and Google Cloud Bigtable are the most popular alternatives and competitors to Datomic Cloud.
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What is Datomic Cloud and what are its top alternatives?

A transactional database with a flexible data model, elastic scaling, and rich queries. Datomic is designed from the ground up to run on AWS. Datomic leverages AWS technology, including DynamoDB, S3, EFS, and CloudFormation to provide a fully integrated solution.
Datomic Cloud is a tool in the NoSQL Database as a Service category of a tech stack.

Top Alternatives to Datomic Cloud

  • Amazon DynamoDB

    Amazon DynamoDB

    With it , you can offload the administrative burden of operating and scaling a highly available distributed database cluster, while paying a low price for only what you use. ...

  • Cloud Firestore

    Cloud Firestore

    Cloud Firestore is a NoSQL document database that lets you easily store, sync, and query data for your mobile and web apps - at global scale. ...

  • Azure Cosmos DB

    Azure Cosmos DB

    Azure DocumentDB is a fully managed NoSQL database service built for fast and predictable performance, high availability, elastic scaling, global distribution, and ease of development. ...

  • Google Cloud Datastore

    Google Cloud Datastore

    Use a managed, NoSQL, schemaless database for storing non-relational data. Cloud Datastore automatically scales as you need it and supports transactions as well as robust, SQL-like queries. ...

  • Google Cloud Bigtable

    Google Cloud Bigtable

    Google Cloud Bigtable offers you a fast, fully managed, massively scalable NoSQL database service that's ideal for web, mobile, and Internet of Things applications requiring terabytes to petabytes of data. Unlike comparable market offerings, Cloud Bigtable doesn't require you to sacrifice speed, scale, or cost efficiency when your applications grow. Cloud Bigtable has been battle-tested at Google for more than 10 years—it's the database driving major applications such as Google Analytics and Gmail. ...

  • Firebase Realtime Database

    Firebase Realtime Database

    It is a cloud-hosted NoSQL database that lets you store and sync data between your users in realtime. Data is synced across all clients in realtime, and remains available when your app goes offline. ...

  • Cloudant

    Cloudant

    Cloudant’s distributed database as a service (DBaaS) allows developers of fast-growing web and mobile apps to focus on building and improving their products, instead of worrying about scaling and managing databases on their own. ...

  • Amazon SimpleDB

    Amazon SimpleDB

    Developers simply store and query data items via web services requests and Amazon SimpleDB does the rest. Behind the scenes, Amazon SimpleDB creates and manages multiple geographically distributed replicas of your data automatically to enable high availability and data durability. Amazon SimpleDB provides a simple web services interface to create and store multiple data sets, query your data easily, and return the results. Your data is automatically indexed, making it easy to quickly find the information that you need. There is no need to pre-define a schema or change a schema if new data is added later. And scale-out is as simple as creating new domains, rather than building out new servers. ...

Datomic Cloud alternatives & related posts

Amazon DynamoDB logo

Amazon DynamoDB

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Fully managed NoSQL database service
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PROS OF AMAZON DYNAMODB
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    Predictable performance and cost
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    Scalable
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    Native JSON Support
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    AWS Free Tier
  • 7
    Fast
  • 3
    No sql
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    To store data
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    Serverless
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    No Stored procedures is GOOD
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    ORM with DynamoDBMapper
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    Elastic Scalability using on-demand mode
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    Elastic Scalability using autoscaling
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    DynamoDB Stream
CONS OF AMAZON DYNAMODB
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    Only sequential access for paginate data

related Amazon DynamoDB posts

Julien DeFrance
Principal Software Engineer at Tophatter · | 16 upvotes · 2.2M views

Back in 2014, I was given an opportunity to re-architect SmartZip Analytics platform, and flagship product: SmartTargeting. This is a SaaS software helping real estate professionals keeping up with their prospects and leads in a given neighborhood/territory, finding out (thanks to predictive analytics) who's the most likely to list/sell their home, and running cross-channel marketing automation against them: direct mail, online ads, email... The company also does provide Data APIs to Enterprise customers.

I had inherited years and years of technical debt and I knew things had to change radically. The first enabler to this was to make use of the cloud and go with AWS, so we would stop re-inventing the wheel, and build around managed/scalable services.

For the SaaS product, we kept on working with Rails as this was what my team had the most knowledge in. We've however broken up the monolith and decoupled the front-end application from the backend thanks to the use of Rails API so we'd get independently scalable micro-services from now on.

Our various applications could now be deployed using AWS Elastic Beanstalk so we wouldn't waste any more efforts writing time-consuming Capistrano deployment scripts for instance. Combined with Docker so our application would run within its own container, independently from the underlying host configuration.

Storage-wise, we went with Amazon S3 and ditched any pre-existing local or network storage people used to deal with in our legacy systems. On the database side: Amazon RDS / MySQL initially. Ultimately migrated to Amazon RDS for Aurora / MySQL when it got released. Once again, here you need a managed service your cloud provider handles for you.

Future improvements / technology decisions included:

Caching: Amazon ElastiCache / Memcached CDN: Amazon CloudFront Systems Integration: Segment / Zapier Data-warehousing: Amazon Redshift BI: Amazon Quicksight / Superset Search: Elasticsearch / Amazon Elasticsearch Service / Algolia Monitoring: New Relic

As our usage grows, patterns changed, and/or our business needs evolved, my role as Engineering Manager then Director of Engineering was also to ensure my team kept on learning and innovating, while delivering on business value.

One of these innovations was to get ourselves into Serverless : Adopting AWS Lambda was a big step forward. At the time, only available for Node.js (Not Ruby ) but a great way to handle cost efficiency, unpredictable traffic, sudden bursts of traffic... Ultimately you want the whole chain of services involved in a call to be serverless, and that's when we've started leveraging Amazon DynamoDB on these projects so they'd be fully scalable.

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Dmitry Mukhin

Uploadcare has built an infinitely scalable infrastructure by leveraging AWS. Building on top of AWS allows us to process 350M daily requests for file uploads, manipulations, and deliveries. When we started in 2011 the only cloud alternative to AWS was Google App Engine which was a no-go for a rather complex solution we wanted to build. We also didn’t want to buy any hardware or use co-locations.

Our stack handles receiving files, communicating with external file sources, managing file storage, managing user and file data, processing files, file caching and delivery, and managing user interface dashboards.

At its core, Uploadcare runs on Python. The Europython 2011 conference in Florence really inspired us, coupled with the fact that it was general enough to solve all of our challenges informed this decision. Additionally we had prior experience working in Python.

We chose to build the main application with Django because of its feature completeness and large footprint within the Python ecosystem.

All the communications within our ecosystem occur via several HTTP APIs, Redis, Amazon S3, and Amazon DynamoDB. We decided on this architecture so that our our system could be scalable in terms of storage and database throughput. This way we only need Django running on top of our database cluster. We use PostgreSQL as our database because it is considered an industry standard when it comes to clustering and scaling.

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Cloud Firestore logo

Cloud Firestore

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NoSQL database built for global apps
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PROS OF CLOUD FIRESTORE
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    Cloud Storage
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    Easy to use
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    Easy setup
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    Realtime Database
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    Super fast
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    Realtime listeners
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    Authentication
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    Could Messaging
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    Google Analytics integration
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    Performance Monitoring
  • 3
    Hosting
  • 3
    Sharing App via invites
  • 3
    Adwords, Admob integration
  • 3
    Test Lab for Android
  • 3
    Crash Reporting
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    Dynamic Links (Deeplinking support)
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    Robust ALI
CONS OF CLOUD FIRESTORE
  • 5
    Doesn't support FullTextSearch natively

related Cloud Firestore posts

Fontumi focuses on the development of telecommunications solutions. We have opted for technologies that allow agile development and great scalability.

Firebase and Node.js + FeathersJS are technologies that we have used on the server side. Vue.js is our main framework for clients.

Our latest products launched have been focused on the integration of AI systems for enriched conversations. Google Compute Engine , along with Dialogflow and Cloud Firestore have been important tools for this work.

Git + GitHub + Visual Studio Code is a killer stack.

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We are building a social media app, where users will post images, like their post, and make friends based on their interest. We are currently using Cloud Firestore and Firebase Realtime Database. We are looking for another database like Amazon DynamoDB; how much this decision can be efficient in terms of pricing and overhead?

See more
Azure Cosmos DB logo

Azure Cosmos DB

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A fully-managed, globally distributed NoSQL database service
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PROS OF AZURE COSMOS DB
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    Best-of-breed NoSQL features
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    High scalability
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    Globally distributed
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    Automatic indexing over flexible json data model
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    Tunable consistency
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    Always on with 99.99% availability sla
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    Javascript language integrated transactions and queries
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    Predictable performance
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    High performance
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    Analytics Store
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    No Sql
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    Rapid Development
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    Auto Indexing
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    Ease of use
CONS OF AZURE COSMOS DB
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    Pricing
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    Poor No SQL query support

related Azure Cosmos DB posts

We have an in-house build experiment management system. We produce samples as input to the next step, which then could produce 1 sample(1-1) and many samples (1 - many). There are many steps like this. So far, we are tracking genealogy (limited tracking) in the MySQL database, which is becoming hard to trace back to the original material or sample(I can give more details if required). So, we are considering a Graph database. I am requesting advice from the experts.

  1. Is a graph database the right choice, or can we manage with RDBMS?
  2. If RDBMS, which RDMS, which feature, or which approach could make this manageable or sustainable
  3. If Graph database(Neo4j, OrientDB, Azure Cosmos DB, Amazon Neptune, ArangoDB), which one is good, and what are the best practices?

I am sorry that this might be a loaded question.

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Hi Mohamad, out of these two options, I'd recommend starting with MongoDB (on MongoDB Atlas) for a few reasons:

• Open Source & Portability - With MongoDB being open source, you have transparency into how your system will work. Not only can you see how it works, but you later have the option to migrate to self-hosted versions of the platform (decreasing costs and avoiding vendor lock-in) or move to a Mongo-compatible hosted database like Amazon DocumentDB or Azure Cosmos DB.

• Querying & Aggregation - MongoDB has been around a few years longer than Firebase, and in my opinion, that is evident from the great design and flexibility of APIs you have for querying and aggregating data.

• Tooling - MongoDB Atlas monitoring tools and the Compass GUI are great for understanding and interacting with the data in your database as you're growing your platform.

I hope this helps!

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Google Cloud Datastore logo

Google Cloud Datastore

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A Fully Managed NoSQL Data Storage Service
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PROS OF GOOGLE CLOUD DATASTORE
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    High scalability
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    Serverless
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    Ability to query any property
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    Pay for what you use
CONS OF GOOGLE CLOUD DATASTORE
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    related Google Cloud Datastore posts

    Google Cloud Bigtable logo

    Google Cloud Bigtable

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    The same database that powers Google Search, Gmail and Analytics
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    PROS OF GOOGLE CLOUD BIGTABLE
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      High performance
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      Fully managed
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      High scalability
    CONS OF GOOGLE CLOUD BIGTABLE
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      related Google Cloud Bigtable posts

      Context: I wanted to create an end to end IoT data pipeline simulation in Google Cloud IoT Core and other GCP services. I never touched Terraform meaningfully until working on this project, and it's one of the best explorations in my development career. The documentation and syntax is incredibly human-readable and friendly. I'm used to building infrastructure through the google apis via Python , but I'm so glad past Sung did not make that decision. I was tempted to use Google Cloud Deployment Manager, but the templates were a bit convoluted by first impression. I'm glad past Sung did not make this decision either.

      Solution: Leveraging Google Cloud Build Google Cloud Run Google Cloud Bigtable Google BigQuery Google Cloud Storage Google Compute Engine along with some other fun tools, I can deploy over 40 GCP resources using Terraform!

      Check Out My Architecture: CLICK ME

      Check out the GitHub repo attached

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      Firebase Realtime Database logo

      Firebase Realtime Database

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      Store and sync data in real time
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      PROS OF FIREBASE REALTIME DATABASE
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        Very fast
      CONS OF FIREBASE REALTIME DATABASE
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        Poor query

      related Firebase Realtime Database posts

      We are building a social media app, where users will post images, like their post, and make friends based on their interest. We are currently using Cloud Firestore and Firebase Realtime Database. We are looking for another database like Amazon DynamoDB; how much this decision can be efficient in terms of pricing and overhead?

      See more
      Cloudant logo

      Cloudant

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      Distributed database-as-a-service (DBaaS) for web & mobile apps
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      PROS OF CLOUDANT
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        JSON
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        REST interface
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        Cheap
      • 3
        JavaScript support
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        Great syncing
      CONS OF CLOUDANT
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        related Cloudant posts

        Josh Dzielak
        Co-Founder & CTO at Orbit · | 5 upvotes · 145.8K views

        As a side project, I was building a note taking app that needed to synchronize between the client and the server so that it would work offline. At first I used Firebase to store the data on the server and wrote my own code to cache Firebase data in local storage and synchronize it. This was brittle and not performant. I figured that someone else must have solved this in a better way so I went looking for a better solution.

        I needed a tool where I could write the data once and it would write to client and server, and when clients came back on line they would automatically catch the client up. I also needed conflict resolution. I was thrilled to discover Pouchdb and its server-side counterpart CouchDB. Together, they met nearly all of my requirements and were very easy to implement - I was able to remove a ton of custom code and have found the synchronization to be very robust. Pouchdb 7 has improved mobile support too, so I can run the app on iOS or Android browsers.

        My Couchdb instance is actually a Cloudant instance running on IBM Bluemix. For my fairly low level of API usage, it's been totally free, and it has a decent GUI for managing users and replications.

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        Amazon SimpleDB logo

        Amazon SimpleDB

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        Highly available and flexible non-relational data store
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        PROS OF AMAZON SIMPLEDB
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          CONS OF AMAZON SIMPLEDB
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            related Amazon SimpleDB posts