Software Alternatives, Accelerators & Startups

Datahike VS Memgraph

Compare Datahike VS Memgraph and see what are their differences

Datahike logo Datahike

A durable datalog database adaptable for distribution.

Memgraph logo Memgraph

Memgraph is an open source graph database built for real-time streaming and compatible with Neo4j. Whether you're a developer or a data scientist with interconnected data, Memgraph will get you the immediate actionable insights fast.
  • Datahike Landing page
    Landing page //
    2023-08-22
  • Memgraph Landing page
    Landing page //
    2021-08-26

Memgraph is a streaming graph application platform that helps you wrangle your streaming data, build sophisticated models that you can query in real-time, and develop applications you never thought possible in days, not months.

Memgraph directly connects to your streaming infrastructure, so you and your team don’t spend countless hours building and maintaining complex data pipelines. You can ingest data from sources like Kafka, SQL, or plain CSV files. Memgraph provides a standard interface to query your data with Cypher, a widely-used and declarative query language that is easy to write, understand and optimize for performance. This is achieved by using the property graph data model, which stores data in terms of objects, their attributes, and the relationships that connect them. This is a natural and effective way to model many real-world problems without relying on complex SQL schemas.

Memgraph is implemented in C/C++ and leverages an in-memory first architecture to ensure that you’re getting the best possible performance consistently and without surprises. It’s also ACID-compliant and highly available.

Datahike

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

Memgraph

$ Details
freemium
Platforms
Cross Platform Windows Mac OSX Linux Docker Web AWS
Release Date
2017 January

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.

Memgraph features and specs

  • Cypher
  • API
  • Authentication
  • Authorization
  • Data Import/Export
  • Visualizations
  • Real-time Monitoring
  • Audit Log
  • High Availibility
  • Graph DB

Datahike videos

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Memgraph videos

What is Memgraph? | Office Hours #1

More videos:

  • Review - Getting started with Memgraph | LIVE

Category Popularity

0-100% (relative to Datahike and Memgraph)
Databases
64 64%
36% 36
Relational Databases
100 100%
0% 0
Developer Tools
0 0%
100% 100
Network & Admin
100 100%
0% 0

User comments

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Reviews

These are some of the external sources and on-site user reviews we've used to compare Datahike and Memgraph

Datahike Reviews

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Memgraph Reviews

  1. Great experience

    The product is very robust and easy to use. I highly recommend it to anyone who needs to analyze streaming data in real-time.

Social recommendations and mentions

Based on our record, Memgraph should be more popular than Datahike. It has been mentiond 23 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 (4)

  • 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 / over 1 year 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: almost 2 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 / about 3 years ago
  • Show HN: Matrix-CRDT – real-time collaborative apps using Matrix as backend
    Having an Datomic like store backed by something like this. https://github.com/replikativ/datahike Is an Open Source variant of Datomic. Lambdaforge wants to eventually have this work with CRDTs. Using the Matrix ecosystem for this is quite interesting as it solves many problems for you already. - Source: Hacker News / over 3 years ago

Memgraph mentions (23)

  • Show HN: FastGraphRAG – Better RAG using good old PageRank
    Suggestion: check out Memgraph for graph db storage - https://memgraph.com/. I work at Memgraph as DX Engineer so feel free to ping me in case you have questions about it: https://memgraph.com/office-hours Your solution looks interesting and I would love to hear more about it. I haven't seen that many PageRank-based graph exploration tools. - Source: Hacker News / 7 months ago
  • List of 45 databases in the world
    Memgraph — Real-time graph database for streaming data. - Source: dev.to / 11 months ago
  • Ask HN: Who is hiring? (March 2024)
    Memgraph | Staff C++ Database Engineer | REMOTE (Central/Western Europe, LatAm, or North America) https://memgraph.com/ Memgraph is a Seed stage, open source graph database vendor. Graph DBs are a great solution for GenAI, logistics, cybersecurity and fintech so we are looking to grow aggressively this year. We're looking for a staff-level engineer to set technical direction, mentor junior team members, and solve... - Source: Hacker News / over 1 year ago
  • Ask HN: Were Graph Databases a Mirage?
    Relational databases have a much longer history of development, and much more engineering time has went into designing RDBMS. It is not a surprise that they are mature on more levels. By looking at the age of a product, you can get a sense of how mature RDBMS systems are compared to most GraphDB projects. Horizontal scaling is hard in GraphDBs due to the nature of how the graph is structured and how you interact... - Source: Hacker News / over 1 year ago
  • When to Use a NoSQL Database
    NoSQL databases are non-relational databases with flexible schema designed for high performance at a massive scale. Unlike traditional relational databases, which use tables and predefined schemas, NoSQL databases use a variety of data models. There are 4 main types of NoSQL databases - document, graph, key-value, and column-oriented databases. NoSQL databases generally are well-suited for unstructured data,... - Source: dev.to / almost 2 years ago
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What are some alternatives?

When comparing Datahike and Memgraph, you can also consider the following products

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

neo4j - Meet Neo4j: The graph database platform powering today's mission-critical enterprise applications, including artificial intelligence, fraud detection and recommendations.

Oracle TimesTen - TimesTen is an in-memory, relational database management system with persistence and...

FalkorDB - Build Fast and Accurate GenAI Apps with GraphRAG at Scale

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

TigerGraph DB - Application and Data, Data Stores, and Graph Database as a Service