Alternatives to NuoDB logo

Alternatives to NuoDB

MongoDB, VoltDB, Cassandra, CockroachDB, and Oracle are the most popular alternatives and competitors to NuoDB.
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What is NuoDB and what are its top alternatives?

NuoDB’s continuously available, ACID-compliant, SQL database delivers on-demand capacity on commodity hardware across multiple data centers.
NuoDB is a tool in the Databases category of a tech stack.

Top Alternatives to NuoDB

  • 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. ...

  • VoltDB

    VoltDB

    VoltDB is a fundamental redesign of the RDBMS that provides unparalleled performance and scalability on bare-metal, virtualized and cloud infrastructures. VoltDB is a modern in-memory architecture that supports both SQL + Java with data durability and fault tolerance. ...

  • Cassandra

    Cassandra

    Partitioning means that Cassandra can distribute your data across multiple machines in an application-transparent matter. Cassandra will automatically repartition as machines are added and removed from the cluster. Row store means that like relational databases, Cassandra organizes data by rows and columns. The Cassandra Query Language (CQL) is a close relative of SQL. ...

  • CockroachDB

    CockroachDB

    It allows you to deploy a database on-prem, in the cloud or even across clouds, all as a single store. It is a simple and straightforward bridge to your future, cloud-based data architecture. ...

  • Oracle

    Oracle

    Oracle Database is an RDBMS. An RDBMS that implements object-oriented features such as user-defined types, inheritance, and polymorphism is called an object-relational database management system (ORDBMS). Oracle Database has extended the relational model to an object-relational model, making it possible to store complex business models in a relational database. ...

  • 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. ...

  • MemSQL

    MemSQL

    MemSQL converges transactions and analytics for sub-second data processing and reporting. Real-time businesses can build robust applications on a simple and scalable infrastructure that complements and extends existing data pipelines. ...

  • InfluxDB

    InfluxDB

    InfluxDB is a scalable datastore for metrics, events, and real-time analytics. It has a built-in HTTP API so you don't have to write any server side code to get up and running. InfluxDB is designed to be scalable, simple to install and manage, and fast to get data in and out. ...

NuoDB alternatives & related posts

MongoDB logo

MongoDB

63K
52.3K
4.1K
The database for giant ideas
63K
52.3K
+ 1
4.1K
PROS OF MONGODB
  • 823
    Document-oriented storage
  • 590
    No sql
  • 546
    Ease of use
  • 465
    Fast
  • 405
    High performance
  • 255
    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
    Enterprise
  • 7
    Easy integration with Node.Js
  • 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
    Fast
  • 2
    Awesome
  • 2
    Managed service
  • 2
    Easy to Scale
  • 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
VoltDB logo

VoltDB

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In-memory relational DBMS capable of supporting millions of database operations per second
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PROS OF VOLTDB
  • 5
    SQL + Java
  • 4
    In-memory database
  • 4
    A brainchild of Michael Stonebraker
  • 3
    Very Fast
  • 2
    NewSQL
CONS OF VOLTDB
    Be the first to leave a con

    related VoltDB posts

    Cassandra logo

    Cassandra

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    A partitioned row store. Rows are organized into tables with a required primary key.
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    489
    PROS OF CASSANDRA
    • 113
      Distributed
    • 94
      High performance
    • 80
      High availability
    • 74
      Easy scalability
    • 52
      Replication
    • 26
      Reliable
    • 26
      Multi datacenter deployments
    • 8
      OLTP
    • 7
      Schema optional
    • 6
      Open source
    • 2
      Workload separation (via MDC)
    • 1
      Fast
    CONS OF CASSANDRA
    • 2
      Reliability of replication
    • 1
      Updates

    related Cassandra posts

    Thierry Schellenbach
    Shared insights
    on
    Redis
    Cassandra
    RocksDB
    at

    1.0 of Stream leveraged Cassandra for storing the feed. Cassandra is a common choice for building feeds. Instagram, for instance started, out with Redis but eventually switched to Cassandra to handle their rapid usage growth. Cassandra can handle write heavy workloads very efficiently.

    Cassandra is a great tool that allows you to scale write capacity simply by adding more nodes, though it is also very complex. This complexity made it hard to diagnose performance fluctuations. Even though we had years of experience with running Cassandra, it still felt like a bit of a black box. When building Stream 2.0 we decided to go for a different approach and build Keevo. Keevo is our in-house key-value store built upon RocksDB, gRPC and Raft.

    RocksDB is a highly performant embeddable database library developed and maintained by Facebook’s data engineering team. RocksDB started as a fork of Google’s LevelDB that introduced several performance improvements for SSD. Nowadays RocksDB is a project on its own and is under active development. It is written in C++ and it’s fast. Have a look at how this benchmark handles 7 million QPS. In terms of technology it’s much more simple than Cassandra.

    This translates into reduced maintenance overhead, improved performance and, most importantly, more consistent performance. It’s interesting to note that LinkedIn also uses RocksDB for their feed.

    #InMemoryDatabases #DataStores #Databases

    See more
    Umair Iftikhar
    Technical Architect at Vappar · | 3 upvotes · 124K views

    Developing a solution that collects Telemetry Data from different devices, nearly 1000 devices minimum and maximum 12000. Each device is sending 2 packets in 1 second. This is time-series data, and this data definition and different reports are saved on PostgreSQL. Like Building information, maintenance records, etc. I want to know about the best solution. This data is required for Math and ML to run different algorithms. Also, data is raw without definitions and information stored in PostgreSQL. Initially, I went with TimescaleDB due to PostgreSQL support, but to increase in sites, I started facing many issues with timescale DB in terms of flexibility of storing data.

    My major requirement is also the replication of the database for reporting and different purposes. You may also suggest other options other than Druid and Cassandra. But an open source solution is appreciated.

    See more
    CockroachDB logo

    CockroachDB

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    225
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    A cloud-native SQL database for building global, scalable cloud services that survive disasters.
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    + 1
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    PROS OF COCKROACHDB
      Be the first to leave a pro
      CONS OF COCKROACHDB
        Be the first to leave a con

        related CockroachDB posts

        Oracle logo

        Oracle

        1.5K
        1.3K
        107
        An RDBMS that implements object-oriented features such as user-defined types, inheritance, and polymorphism
        1.5K
        1.3K
        + 1
        107
        PROS OF ORACLE
        • 42
          Reliable
        • 31
          Enterprise
        • 15
          High Availability
        • 5
          Hard to maintain
        • 4
          Expensive
        • 4
          Maintainable
        • 3
          High complexity
        • 3
          Hard to use
        CONS OF ORACLE
        • 13
          Expensive

        related Oracle posts

        Hi. We are planning to develop web, desktop, and mobile app for procurement, logistics, and contracts. Procure to Pay and Source to pay, spend management, supplier management, catalog management. ( similar to SAP Ariba, gap.com, coupa.com, ivalua.com vroozi.com, procurify.com

        We got stuck when deciding which technology stack is good for the future. We look forward to your kind guidance that will help us.

        We want to integrate with multiple databases with seamless bidirectional integration. What APIs and middleware available are best to achieve this? SAP HANA, Oracle, MySQL, MongoDB...

        ASP.NET / Node.js / Laravel. ......?

        Please guide us

        See more
        MySQL logo

        MySQL

        83.3K
        67.1K
        3.7K
        The world's most popular open source database
        83.3K
        67.1K
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        PROS OF MYSQL
        • 791
          Sql
        • 673
          Free
        • 557
          Easy
        • 525
          Widely used
        • 485
          Open source
        • 180
          High availability
        • 158
          Cross-platform support
        • 103
          Great community
        • 78
          Secure
        • 75
          Full-text indexing and searching
        • 25
          Fast, open, available
        • 14
          SSL support
        • 13
          Robust
        • 13
          Reliable
        • 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
        MemSQL logo

        MemSQL

        72
        147
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        Database for real-time transactions and analytics.
        72
        147
        + 1
        18
        PROS OF MEMSQL
        • 5
          Distributed
        • 3
          Realtime
        • 2
          Sql
        • 2
          JSON
        • 2
          Concurrent
        • 2
          Columnstore
        • 1
          Scalable
        • 1
          Ultra fast
        CONS OF MEMSQL
          Be the first to leave a con

          related MemSQL posts

          InfluxDB logo

          InfluxDB

          867
          891
          163
          An open-source distributed time series database with no external dependencies
          867
          891
          + 1
          163
          PROS OF INFLUXDB
          • 51
            Time-series data analysis
          • 28
            Easy setup, no dependencies
          • 24
            Fast, scalable & open source
          • 21
            Open source
          • 18
            Real-time analytics
          • 6
            Continuous Query support
          • 5
            Easy Query Language
          • 4
            HTTP API
          • 4
            Out-of-the-box, automatic Retention Policy
          • 1
            Offers Enterprise version
          • 1
            Free Open Source version
          CONS OF INFLUXDB
          • 4
            Instability
          • 1
            HA or Clustering is only in paid version

          related InfluxDB posts

          Juan Felipe

          Hi everyone. I'm trying to create my personal syslog monitoring.

          1. To get the logs, I have an uncertainty to choose the way: 1.1 Use Logstash like a TCP server. 1.2 Implement a Go TCP server.

          2. To store and plotting data. 2.1 Use Elasticsearch tools. 2.2 Use InfluxDB and Grafana.

          I would like to know... Which is more cheaper and scalable solution?

          Or even if there is a better way to do it.

          See more