Best Data Modeling Tools for JanusGraph

Find and compare the best Data Modeling tools for JanusGraph in 2024

Use the comparison tool below to compare the top Data Modeling tools for JanusGraph on the market. You can filter results by user reviews, pricing, features, platform, region, support options, integrations, and more.

  • 1
    Hackolade Reviews

    Hackolade

    Hackolade

    €100 per month
    Hackolade is the pioneer for data modeling of NoSQL and multi-model databases, providing a comprehensive suite of data modeling tools for various NoSQL databases and APIs. Hackolade is the only data modeling tool for MongoDB, Neo4j, Cassandra, ArangoDB, BigQuery, Couchbase, Cosmos DB, Databricks, DocumentDB, DynamoDB, Elasticsearch, EventBridge Schema Registry, Glue Data Catalog, HBase, Hive, Firebase/Firestore, JanusGraph, MariaDB, MarkLogic, MySQL, Oracle, PostgreSQL, Redshift, ScyllaDB, Snowflake, SQL Server, Synapse, TinkerPop, YugabyteDB, etc. It also applies its visual design to Avro, JSON Schema, Parquet, Protobuf, Swagger and OpenAPI, and is rapidly adding new targets for its physical data modeling engine. The software is user-friendly and simple to use yet provides powerful visuals and graphic data modeling to smooth the onboarding of NoSQL technology. Its software tools help functional analysts, designers, architects, and DBAs involved with NoSQL technology achieve greater transparency and control, resulting in reduced development time, increased application quality, and lower execution risks across the enterprise.
  • 2
    Apache Spark Reviews

    Apache Spark

    Apache Software Foundation

    Apache Spark™, a unified analytics engine that can handle large-scale data processing, is available. Apache Spark delivers high performance for streaming and batch data. It uses a state of the art DAG scheduler, query optimizer, as well as a physical execution engine. Spark has over 80 high-level operators, making it easy to create parallel apps. You can also use it interactively via the Scala, Python and R SQL shells. Spark powers a number of libraries, including SQL and DataFrames and MLlib for machine-learning, GraphX and Spark Streaming. These libraries can be combined seamlessly in one application. Spark can run on Hadoop, Apache Mesos and Kubernetes. It can also be used standalone or in the cloud. It can access a variety of data sources. Spark can be run in standalone cluster mode on EC2, Hadoop YARN and Mesos. Access data in HDFS and Alluxio.
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