Alternatives to BigchainDB logo

Alternatives to BigchainDB

Ethereum, MongoDB, IPFS , MultiChain, and Hyperledger Fabric are the most popular alternatives and competitors to BigchainDB.
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What is BigchainDB and what are its top alternatives?

It is designed to merge the best of two worlds: the “traditional” distributed database world and the “traditional” blockchain world. With high throughput, low latency, powerful query functionality, decentralized control, immutable data storage and built-in asset support.
BigchainDB is a tool in the Blockchain category of a tech stack.
BigchainDB is an open source tool with 3.7K GitHub stars and 754 GitHub forks. Here’s a link to BigchainDB's open source repository on GitHub

Top Alternatives to BigchainDB

  • Ethereum

    Ethereum

    A decentralized platform for applications that run exactly as programmed without any chance of fraud, censorship or third-party interference. ...

  • MongoDB

    MongoDB

    MongoDB stores data in JSON-like documents that can vary in structure, offering a dynamic, flexible schema. MongoDB was also designed for high availability and scalability, with built-in replication and auto-sharding. ...

  • IPFS

    IPFS

    It is a protocol and network designed to create a content-addressable, peer-to-peer method of storing and sharing hypermedia in a distributed file system. ...

  • MultiChain

    MultiChain

    It is a platform that helps users to establish a certain private Blockchains that can be used by the organizations for financial transactions. ...

  • Hyperledger Fabric

    Hyperledger Fabric

    It is a collaborative effort created to advance blockchain technology by identifying and addressing important features and currently missing requirements. It leverages container technology to host smart contracts called “chaincode” that comprise the application logic of the system. ...

  • MySQL

    MySQL

    The MySQL software delivers a very fast, multi-threaded, multi-user, and robust SQL (Structured Query Language) database server. MySQL Server is intended for mission-critical, heavy-load production systems as well as for embedding into mass-deployed software. ...

  • PostgreSQL

    PostgreSQL

    PostgreSQL is an advanced object-relational database management system that supports an extended subset of the SQL standard, including transactions, foreign keys, subqueries, triggers, user-defined types and functions. ...

  • Microsoft SQL Server

    Microsoft SQL Server

    Microsoft® SQL Server is a database management and analysis system for e-commerce, line-of-business, and data warehousing solutions. ...

BigchainDB alternatives & related posts

Ethereum logo

Ethereum

373
303
10
Open source platform to write and distribute decentralized applications
373
303
+ 1
10
PROS OF ETHEREUM
  • 6
    Decentralized blockchain, most famous platform for DApp
  • 2
    #2 on capitalization after Bitcoin
  • 1
    Resistant to hash power attacks
  • 1
    Rich smart contract execution environment
CONS OF ETHEREUM
    Be the first to leave a con

    related Ethereum posts

    MongoDB logo

    MongoDB

    64.3K
    53.8K
    4.1K
    The database for giant ideas
    64.3K
    53.8K
    + 1
    4.1K
    PROS OF MONGODB
    • 824
      Document-oriented storage
    • 591
      No sql
    • 546
      Ease of use
    • 465
      Fast
    • 406
      High performance
    • 256
      Free
    • 215
      Open source
    • 179
      Flexible
    • 142
      Replication & high availability
    • 109
      Easy to maintain
    • 41
      Querying
    • 37
      Easy scalability
    • 36
      Auto-sharding
    • 35
      High availability
    • 31
      Map/reduce
    • 26
      Document database
    • 24
      Easy setup
    • 24
      Full index support
    • 15
      Reliable
    • 14
      Fast in-place updates
    • 13
      Agile programming, flexible, fast
    • 11
      No database migrations
    • 7
      Easy integration with Node.Js
    • 7
      Enterprise
    • 5
      Enterprise Support
    • 4
      Great NoSQL DB
    • 3
      Aggregation Framework
    • 3
      Support for many languages through different drivers
    • 3
      Drivers support is good
    • 2
      Schemaless
    • 2
      Easy to Scale
    • 2
      Fast
    • 2
      Awesome
    • 2
      Managed service
    • 1
      Consistent
    CONS OF MONGODB
    • 5
      Very slowly for connected models that require joins
    • 3
      Not acid compliant
    • 1
      Proprietary query language

    related MongoDB posts

    Jeyabalaji Subramanian

    Recently we were looking at a few robust and cost-effective ways of replicating the data that resides in our production MongoDB to a PostgreSQL database for data warehousing and business intelligence.

    We set ourselves the following criteria for the optimal tool that would do this job: - The data replication must be near real-time, yet it should NOT impact the production database - The data replication must be horizontally scalable (based on the load), asynchronous & crash-resilient

    Based on the above criteria, we selected the following tools to perform the end to end data replication:

    We chose MongoDB Stitch for picking up the changes in the source database. It is the serverless platform from MongoDB. One of the services offered by MongoDB Stitch is Stitch Triggers. Using stitch triggers, you can execute a serverless function (in Node.js) in real time in response to changes in the database. When there are a lot of database changes, Stitch automatically "feeds forward" these changes through an asynchronous queue.

    We chose Amazon SQS as the pipe / message backbone for communicating the changes from MongoDB to our own replication service. Interestingly enough, MongoDB stitch offers integration with AWS services.

    In the Node.js function, we wrote minimal functionality to communicate the database changes (insert / update / delete / replace) to Amazon SQS.

    Next we wrote a minimal micro-service in Python to listen to the message events on SQS, pickup the data payload & mirror the DB changes on to the target Data warehouse. We implemented source data to target data translation by modelling target table structures through SQLAlchemy . We deployed this micro-service as AWS Lambda with Zappa. With Zappa, deploying your services as event-driven & horizontally scalable Lambda service is dumb-easy.

    In the end, we got to implement a highly scalable near realtime Change Data Replication service that "works" and deployed to production in a matter of few days!

    See more
    Robert Zuber

    We use MongoDB as our primary #datastore. Mongo's approach to replica sets enables some fantastic patterns for operations like maintenance, backups, and #ETL.

    As we pull #microservices from our #monolith, we are taking the opportunity to build them with their own datastores using PostgreSQL. We also use Redis to cache data we’d never store permanently, and to rate-limit our requests to partners’ APIs (like GitHub).

    When we’re dealing with large blobs of immutable data (logs, artifacts, and test results), we store them in Amazon S3. We handle any side-effects of S3’s eventual consistency model within our own code. This ensures that we deal with user requests correctly while writes are in process.

    See more
    IPFS  logo

    IPFS

    103
    117
    0
    Protocol for storing and sharing hypermedia in a distributed file system
    103
    117
    + 1
    0
    PROS OF IPFS
      Be the first to leave a pro
      CONS OF IPFS
        Be the first to leave a con

        related IPFS posts

        MultiChain logo

        MultiChain

        12
        29
        2
        Open platform for blockchain applications
        12
        29
        + 1
        2
        PROS OF MULTICHAIN
        • 2
          No Transaction Fees
        CONS OF MULTICHAIN
          Be the first to leave a con

          related MultiChain posts

          Hyperledger Fabric logo

          Hyperledger Fabric

          98
          110
          3
          An open source initiative to advance blockchain technology
          98
          110
          + 1
          3
          PROS OF HYPERLEDGER FABRIC
          • 2
            Flexible blockchain framework
          • 1
            Easily to developmenet
          CONS OF HYPERLEDGER FABRIC
            Be the first to leave a con

            related Hyperledger Fabric posts

            MySQL logo

            MySQL

            85K
            69.1K
            3.7K
            The world's most popular open source database
            85K
            69.1K
            + 1
            3.7K
            PROS OF MYSQL
            • 794
              Sql
            • 672
              Free
            • 556
              Easy
            • 527
              Widely used
            • 485
              Open source
            • 180
              High availability
            • 160
              Cross-platform support
            • 104
              Great community
            • 78
              Secure
            • 75
              Full-text indexing and searching
            • 25
              Fast, open, available
            • 14
              SSL support
            • 13
              Reliable
            • 13
              Robust
            • 8
              Enterprise Version
            • 7
              Easy to set up on all platforms
            • 2
              NoSQL access to JSON data type
            • 1
              Relational database
            • 1
              Easy, light, scalable
            • 1
              Sequel Pro (best SQL GUI)
            • 1
              Replica Support
            CONS OF MYSQL
            • 14
              Owned by a company with their own agenda
            • 1
              Can't roll back schema changes

            related MySQL posts

            Tim Abbott

            We've been using PostgreSQL since the very early days of Zulip, but we actually didn't use it from the beginning. Zulip started out as a MySQL project back in 2012, because we'd heard it was a good choice for a startup with a wide community. However, we found that even though we were using the Django ORM for most of our database access, we spent a lot of time fighting with MySQL. Issues ranged from bad collation defaults, to bad query plans which required a lot of manual query tweaks.

            We ended up getting so frustrated that we tried out PostgresQL, and the results were fantastic. We didn't have to do any real customization (just some tuning settings for how big a server we had), and all of our most important queries were faster out of the box. As a result, we were able to delete a bunch of custom queries escaping the ORM that we'd written to make the MySQL query planner happy (because postgres just did the right thing automatically).

            And then after that, we've just gotten a ton of value out of postgres. We use its excellent built-in full-text search, which has helped us avoid needing to bring in a tool like Elasticsearch, and we've really enjoyed features like its partial indexes, which saved us a lot of work adding unnecessary extra tables to get good performance for things like our "unread messages" and "starred messages" indexes.

            I can't recommend it highly enough.

            See more
            Conor Myhrvold
            Tech Brand Mgr, Office of CTO at Uber · | 21 upvotes · 1.1M views

            Our most popular (& controversial!) article to date on the Uber Engineering blog in 3+ yrs. Why we moved from PostgreSQL to MySQL. In essence, it was due to a variety of limitations of Postgres at the time. Fun fact -- earlier in Uber's history we'd actually moved from MySQL to Postgres before switching back for good, & though we published the article in Summer 2016 we haven't looked back since:

            The early architecture of Uber consisted of a monolithic backend application written in Python that used Postgres for data persistence. Since that time, the architecture of Uber has changed significantly, to a model of microservices and new data platforms. Specifically, in many of the cases where we previously used Postgres, we now use Schemaless, a novel database sharding layer built on top of MySQL (https://eng.uber.com/schemaless-part-one/). In this article, we’ll explore some of the drawbacks we found with Postgres and explain the decision to build Schemaless and other backend services on top of MySQL:

            https://eng.uber.com/mysql-migration/

            See more
            PostgreSQL logo

            PostgreSQL

            64.7K
            52.3K
            3.5K
            A powerful, open source object-relational database system
            64.7K
            52.3K
            + 1
            3.5K
            PROS OF POSTGRESQL
            • 755
              Relational database
            • 508
              High availability
            • 436
              Enterprise class database
            • 380
              Sql
            • 302
              Sql + nosql
            • 171
              Great community
            • 145
              Easy to setup
            • 129
              Heroku
            • 128
              Secure by default
            • 111
              Postgis
            • 48
              Supports Key-Value
            • 46
              Great JSON support
            • 32
              Cross platform
            • 30
              Extensible
            • 26
              Replication
            • 24
              Triggers
            • 22
              Rollback
            • 21
              Multiversion concurrency control
            • 20
              Open source
            • 17
              Heroku Add-on
            • 14
              Stable, Simple and Good Performance
            • 13
              Powerful
            • 12
              Lets be serious, what other SQL DB would you go for?
            • 9
              Good documentation
            • 7
              Intelligent optimizer
            • 7
              Scalable
            • 6
              Reliable
            • 6
              Transactional DDL
            • 6
              Modern
            • 5
              Free
            • 5
              One stop solution for all things sql no matter the os
            • 4
              Relational database with MVCC
            • 3
              Faster Development
            • 3
              Full-Text Search
            • 3
              Developer friendly
            • 2
              Excellent source code
            • 2
              Great DB for Transactional system or Application
            • 2
              search
            • 1
              Free version
            • 1
              Open-source
            • 1
              Full-text
            • 1
              Text
            CONS OF POSTGRESQL
            • 9
              Table/index bloatings

            related PostgreSQL posts

            Jeyabalaji Subramanian

            Recently we were looking at a few robust and cost-effective ways of replicating the data that resides in our production MongoDB to a PostgreSQL database for data warehousing and business intelligence.

            We set ourselves the following criteria for the optimal tool that would do this job: - The data replication must be near real-time, yet it should NOT impact the production database - The data replication must be horizontally scalable (based on the load), asynchronous & crash-resilient

            Based on the above criteria, we selected the following tools to perform the end to end data replication:

            We chose MongoDB Stitch for picking up the changes in the source database. It is the serverless platform from MongoDB. One of the services offered by MongoDB Stitch is Stitch Triggers. Using stitch triggers, you can execute a serverless function (in Node.js) in real time in response to changes in the database. When there are a lot of database changes, Stitch automatically "feeds forward" these changes through an asynchronous queue.

            We chose Amazon SQS as the pipe / message backbone for communicating the changes from MongoDB to our own replication service. Interestingly enough, MongoDB stitch offers integration with AWS services.

            In the Node.js function, we wrote minimal functionality to communicate the database changes (insert / update / delete / replace) to Amazon SQS.

            Next we wrote a minimal micro-service in Python to listen to the message events on SQS, pickup the data payload & mirror the DB changes on to the target Data warehouse. We implemented source data to target data translation by modelling target table structures through SQLAlchemy . We deployed this micro-service as AWS Lambda with Zappa. With Zappa, deploying your services as event-driven & horizontally scalable Lambda service is dumb-easy.

            In the end, we got to implement a highly scalable near realtime Change Data Replication service that "works" and deployed to production in a matter of few days!

            See more
            Tim Abbott

            We've been using PostgreSQL since the very early days of Zulip, but we actually didn't use it from the beginning. Zulip started out as a MySQL project back in 2012, because we'd heard it was a good choice for a startup with a wide community. However, we found that even though we were using the Django ORM for most of our database access, we spent a lot of time fighting with MySQL. Issues ranged from bad collation defaults, to bad query plans which required a lot of manual query tweaks.

            We ended up getting so frustrated that we tried out PostgresQL, and the results were fantastic. We didn't have to do any real customization (just some tuning settings for how big a server we had), and all of our most important queries were faster out of the box. As a result, we were able to delete a bunch of custom queries escaping the ORM that we'd written to make the MySQL query planner happy (because postgres just did the right thing automatically).

            And then after that, we've just gotten a ton of value out of postgres. We use its excellent built-in full-text search, which has helped us avoid needing to bring in a tool like Elasticsearch, and we've really enjoyed features like its partial indexes, which saved us a lot of work adding unnecessary extra tables to get good performance for things like our "unread messages" and "starred messages" indexes.

            I can't recommend it highly enough.

            See more
            Microsoft SQL Server logo

            Microsoft SQL Server

            12.9K
            9.6K
            535
            A relational database management system developed by Microsoft
            12.9K
            9.6K
            + 1
            535
            PROS OF MICROSOFT SQL SERVER
            • 137
              Reliable and easy to use
            • 101
              High performance
            • 94
              Great with .net
            • 65
              Works well with .net
            • 56
              Easy to maintain
            • 21
              Azure support
            • 17
              Always on
            • 17
              Full Index Support
            • 10
              Enterprise manager is fantastic
            • 9
              In-Memory OLTP Engine
            • 2
              Security is forefront
            • 1
              Columnstore indexes
            • 1
              Great documentation
            • 1
              Faster Than Oracle
            • 1
              Decent management tools
            • 1
              Easy to setup and configure
            • 1
              Docker Delivery
            CONS OF MICROSOFT SQL SERVER
            • 4
              Expensive Licensing
            • 2
              Microsoft

            related Microsoft SQL Server posts

            We initially started out with Heroku as our PaaS provider due to a desire to use it by our original developer for our Ruby on Rails application/website at the time. We were finding response times slow, it was painfully slow, sometimes taking 10 seconds to start loading the main page. Moving up to the next "compute" level was going to be very expensive.

            We moved our site over to AWS Elastic Beanstalk , not only did response times on the site practically become instant, our cloud bill for the application was cut in half.

            In database world we are currently using Amazon RDS for PostgreSQL also, we have both MariaDB and Microsoft SQL Server both hosted on Amazon RDS. The plan is to migrate to AWS Aurora Serverless for all 3 of those database systems.

            Additional services we use for our public applications: AWS Lambda, Python, Redis, Memcached, AWS Elastic Load Balancing (ELB), Amazon Elasticsearch Service, Amazon ElastiCache

            See more

            I am a Microsoft SQL Server programmer who is a bit out of practice. I have been asked to assist on a new project. The overall purpose is to organize a large number of recordings so that they can be searched. I have an enormous music library but my songs are several hours long. I need to include things like time, date and location of the recording. I don't have a problem with the general database design. I have two primary questions:

            1. I need to use either MySQL or PostgreSQL on a Linux based OS. Which would be better for this application?
            2. I have not dealt with a sound based data type before. How do I store that and put it in a table? Thank you.
            See more