Alternatives to Neo4j logo

Alternatives to Neo4j

Titan, MongoDB, Cassandra, OrientDB, and JanusGraph are the most popular alternatives and competitors to Neo4j.
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What is Neo4j and what are its top alternatives?

Neo4j stores data in nodes connected by directed, typed relationships with properties on both, also known as a Property Graph. It is a high performance graph store with all the features expected of a mature and robust database, like a friendly query language and ACID transactions.
Neo4j is a tool in the Graph Databases category of a tech stack.
Neo4j is an open source tool with 13.5K GitHub stars and 2.4K GitHub forks. Here’s a link to Neo4j's open source repository on GitHub

Top Alternatives to Neo4j

  • Titan
    Titan

    Titan is a scalable graph database optimized for storing and querying graphs containing hundreds of billions of vertices and edges distributed across a multi-machine cluster. Titan is a transactional database that can support thousands of concurrent users executing complex graph traversals in real time. ...

  • 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. ...

  • 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. ...

  • OrientDB
    OrientDB

    It is an open source NoSQL database management system written in Java. It is a Multi-model database, supporting graph, document, key/value, and object models, but the relationships are managed as in graph databases with direct connections between records. ...

  • JanusGraph
    JanusGraph

    It is a scalable graph database optimized for storing and querying graphs containing hundreds of billions of vertices and edges distributed across a multi-machine cluster. It is a transactional database that can support thousands of concurrent users executing complex graph traversals in real time. ...

  • Dgraph
    Dgraph

    Dgraph's goal is to provide Google production level scale and throughput, with low enough latency to be serving real time user queries, over terabytes of structured data. Dgraph supports GraphQL-like query syntax, and responds in JSON and Protocol Buffers over GRPC and HTTP. ...

  • ArangoDB
    ArangoDB

    A distributed free and open-source database with a flexible data model for documents, graphs, and key-values. Build high performance applications using a convenient SQL-like query language or JavaScript extensions. ...

  • Neptune
    Neptune

    It brings organization and collaboration to data science projects. All the experiement-related objects are backed-up and organized ready to be analyzed, reproduced and shared with others. Works with all common technologies and integrates with other tools. ...

Neo4j alternatives & related posts

Titan logo

Titan

38
56
0
Distributed Graph Database
38
56
+ 1
0
PROS OF TITAN
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    CONS OF TITAN
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      related Titan posts

      MongoDB logo

      MongoDB

      93.5K
      80.7K
      4.1K
      The database for giant ideas
      93.5K
      80.7K
      + 1
      4.1K
      PROS OF MONGODB
      • 828
        Document-oriented storage
      • 593
        No sql
      • 553
        Ease of use
      • 464
        Fast
      • 410
        High performance
      • 255
        Free
      • 218
        Open source
      • 180
        Flexible
      • 145
        Replication & high availability
      • 112
        Easy to maintain
      • 42
        Querying
      • 39
        Easy scalability
      • 38
        Auto-sharding
      • 37
        High availability
      • 31
        Map/reduce
      • 27
        Document database
      • 25
        Easy setup
      • 25
        Full index support
      • 16
        Reliable
      • 15
        Fast in-place updates
      • 14
        Agile programming, flexible, fast
      • 12
        No database migrations
      • 8
        Easy integration with Node.Js
      • 8
        Enterprise
      • 6
        Enterprise Support
      • 5
        Great NoSQL DB
      • 4
        Support for many languages through different drivers
      • 3
        Schemaless
      • 3
        Aggregation Framework
      • 3
        Drivers support is good
      • 2
        Fast
      • 2
        Managed service
      • 2
        Easy to Scale
      • 2
        Awesome
      • 2
        Consistent
      • 1
        Good GUI
      • 1
        Acid Compliant
      CONS OF MONGODB
      • 6
        Very slowly for connected models that require joins
      • 3
        Not acid compliant
      • 2
        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
      Cassandra logo

      Cassandra

      3.6K
      3.5K
      507
      A partitioned row store. Rows are organized into tables with a required primary key.
      3.6K
      3.5K
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      507
      PROS OF CASSANDRA
      • 119
        Distributed
      • 98
        High performance
      • 81
        High availability
      • 74
        Easy scalability
      • 53
        Replication
      • 26
        Reliable
      • 26
        Multi datacenter deployments
      • 10
        Schema optional
      • 9
        OLTP
      • 8
        Open source
      • 2
        Workload separation (via MDC)
      • 1
        Fast
      CONS OF CASSANDRA
      • 3
        Reliability of replication
      • 1
        Size
      • 1
        Updates

      related Cassandra posts

      Thierry Schellenbach
      Shared insights
      on
      RedisRedisCassandraCassandraRocksDBRocksDB
      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

      Trying to establish a data lake(or maybe puddle) for my org's Data Sharing project. The idea is that outside partners would send cuts of their PHI data, regardless of format/variables/systems, to our Data Team who would then harmonize the data, create data marts, and eventually use it for something. End-to-end, I'm envisioning:

      1. Ingestion->Secure, role-based, self service portal for users to upload data (1a. bonus points if it can preform basic validations/masking)
      2. Storage->Amazon S3 seems like the cheapest. We probably won't need very big, even at full capacity. Our current storage is a secure Box folder that has ~4GB with several batches of test data, code, presentations, and planning docs.
      3. Data Catalog-> AWS Glue? Azure Data Factory? Snowplow? is the main difference basically based on the vendor? We also will have Data Dictionaries/Codebooks from submitters. Where would they fit in?
      4. Partitions-> I've seen Cassandra and YARN mentioned, but have no experience with either
      5. Processing-> We want to use SAS if at all possible. What will work with SAS code?
      6. Pipeline/Automation->The check-in and verification processes that have been outlined are rather involved. Some sort of automated messaging or approval workflow would be nice
      7. I have very little guidance on what a "Data Mart" should look like, so I'm going with the idea that it would be another "experimental" partition. Unless there's an actual mart-building paradigm I've missed?
      8. An end user might use the catalog to pull certain de-identified data sets from the marts. Again, role-based access and self-service gui would be preferable. I'm the only full-time tech person on this project, but I'm mostly an OOP, HTML, JavaScript, and some SQL programmer. Most of this is out of my repertoire. I've done a lot of research, but I can't be an effective evangelist without hands-on experience. Since we're starting a new year of our grant, they've finally decided to let me try some stuff out. Any pointers would be appreciated!
      See more
      OrientDB logo

      OrientDB

      75
      107
      14
      An open source NoSQL database management system
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      107
      + 1
      14
      PROS OF ORIENTDB
      • 4
        Great graphdb
      • 2
        Great support
      • 2
        Open source
      • 1
        Multi-Model/Paradigm
      • 1
        ACID
      • 1
        Highly-available
      • 1
        Performance
      • 1
        Embeddable
      • 1
        Rest api
      CONS OF ORIENTDB
      • 4
        Unstable

      related OrientDB 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.

      See more
      JanusGraph logo

      JanusGraph

      43
      96
      0
      Open-source, distributed graph database
      43
      96
      + 1
      0
      PROS OF JANUSGRAPH
        Be the first to leave a pro
        CONS OF JANUSGRAPH
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          related JanusGraph posts

          Dgraph logo

          Dgraph

          125
          220
          9
          Fast, Distributed Graph DB
          125
          220
          + 1
          9
          PROS OF DGRAPH
          • 3
            Graphql as a query language is nice if you like apollo
          • 2
            Easy set up
          • 2
            Low learning curve
          • 1
            Open Source
          • 1
            High Performance
          CONS OF DGRAPH
            Be the first to leave a con

            related Dgraph posts

            ArangoDB logo

            ArangoDB

            276
            442
            192
            A distributed open-source database with a flexible data model for documents, graphs, and key-values.
            276
            442
            + 1
            192
            PROS OF ARANGODB
            • 37
              Grahps and documents in one DB
            • 26
              Intuitive and rich query language
            • 25
              Good documentation
            • 25
              Open source
            • 21
              Joins for collections
            • 15
              Foxx is great platform
            • 14
              Great out of the box web interface with API playground
            • 6
              Good driver support
            • 6
              Low maintenance efforts
            • 6
              Clustering
            • 5
              Easy microservice creation with foxx
            • 4
              You can write true backendless apps
            • 2
              Managed solution available
            • 0
              Performance
            CONS OF ARANGODB
            • 3
              Web ui has still room for improvement
            • 2
              No support for blueprints standard, using custom AQL

            related ArangoDB 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.

            See more

            Hello All, I'm building an app that will enable users to create documents using ckeditor or TinyMCE editor. The data is then stored in a database and retrieved to display to the user, these docs can contain image data also. The number of pages generated for a single document can go up to 1000. Therefore by design, each page is stored in a separate JSON. I'm wondering which database is the right one to choose between ArangoDB and PostgreSQL. Your thoughts, advice please. Thanks, Kashyap

            See more
            Neptune logo

            Neptune

            15
            37
            2
            The most lightweight experiment tracking tool for machine learning
            15
            37
            + 1
            2
            PROS OF NEPTUNE
            • 1
              Aws managed services
            • 1
              Supports both gremlin and openCypher query languages
            CONS OF NEPTUNE
            • 1
              Doesn't have much support for openCypher clients
            • 1
              Doesn't have proper clients for different lanuages
            • 1
              Doesn't have much community support

            related Neptune posts