DBMS > Hive vs. Kinetica vs. Microsoft Azure Data Explorer vs. RDF4J vs. Tarantool
System Properties Comparison Hive vs. Kinetica vs. Microsoft Azure Data Explorer vs. RDF4J vs. Tarantool
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Name | Hive Xexclude from comparison | Kinetica Xexclude from comparison | Microsoft Azure Data Explorer Xexclude from comparison | RDF4J formerly known as Sesame Xexclude from comparison | Tarantool Xexclude from comparison | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Description | data warehouse software for querying and managing large distributed datasets, built on Hadoop | Fully vectorized database across both GPUs and CPUs | Fully managed big data interactive analytics platform | RDF4J is a Java framework for processing RDF data, supporting both memory-based and a disk-based storage. | In-memory computing platform with a flexible data schema for efficiently building high-performance applications | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Primary database model | Relational DBMS | Relational DBMS | Relational DBMS column oriented | RDF store | Document store Key-value store Relational DBMS | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Secondary database models | Spatial DBMS Time Series DBMS | Document store If a column is of type dynamic docs.microsoft.com/en-us/azure/kusto/query/scalar-data-types/dynamic then it's possible to add arbitrary JSON documents in this cell Event Store this is the general usage pattern at Microsoft. Billing, Logs, Telemetry events are stored in ADX and the state of an individual entity is defined by the arg_max(timestamps) Spatial DBMS Search engine support for complex search expressions docs.microsoft.com/en-us/azure/kusto/query/parseoperator FTS, Geospatial docs.microsoft.com/en-us/azure/kusto/query/geo-point-to-geohash-function distributed search -> ADX acts as a distributed search engine Time Series DBMS see docs.microsoft.com/en-us/azure/data-explorer/time-series-analysis | Spatial DBMS with Tarantool/GIS extension | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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Website | hive.apache.org | www.kinetica.com | azure.microsoft.com/services/data-explorer | rdf4j.org | www.tarantool.io | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Technical documentation | cwiki.apache.org/confluence/display/Hive/Home | docs.kinetica.com | docs.microsoft.com/en-us/azure/data-explorer | rdf4j.org/documentation | www.tarantool.io/en/doc | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Developer | Apache Software Foundation initially developed by Facebook | Kinetica | Microsoft | Since 2016 officially forked into an Eclipse project, former developer was Aduna Software. | VK | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Initial release | 2012 | 2012 | 2019 | 2004 | 2008 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Current release | 3.1.3, April 2022 | 7.1, August 2021 | cloud service with continuous releases | 2.10.0, May 2022 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
License Commercial or Open Source | Open Source Apache Version 2 | commercial | commercial | Open Source Eclipse Distribution License (EDL), v1.0. | Open Source BSD-2, source-available extensions (modules), commercial licenses for Tarantool Enterprise | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Cloud-based only Only available as a cloud service | no | no | yes | no | no | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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Implementation language | Java | C, C++ | Java | C and C++ | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Server operating systems | All OS with a Java VM | Linux | hosted | Linux OS X Unix Windows | BSD Linux macOS | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Data scheme | yes | yes | Fixed schema with schema-less datatypes (dynamic) | yes RDF Schemas | Flexible data schema: relational definition for tables with ability to store json-like documents in columns | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Typing predefined data types such as float or date | yes | yes | yes bool, datetime, dynamic, guid, int, long, real, string, timespan, double: docs.microsoft.com/en-us/azure/kusto/query/scalar-data-types | yes | string, double, decimal, uuid, integer, blob, boolean, datetime | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
XML support Some form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT. | no | yes | no | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Secondary indexes | yes | yes | all fields are automatically indexed | yes | yes | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
SQL Support of SQL | SQL-like DML and DDL statements | SQL-like DML and DDL statements | Kusto Query Language (KQL), SQL subset | no | Full-featured ANSI SQL support | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
APIs and other access methods | JDBC ODBC Thrift | JDBC ODBC RESTful HTTP API | Microsoft SQL Server communication protocol (MS-TDS) RESTful HTTP API | Java API RIO RDF Input/Output Sail API SeRQL Sesame RDF Query Language Sesame REST HTTP Protocol SPARQL | Open binary protocol | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Supported programming languages | C++ Java PHP Python | C++ Java JavaScript (Node.js) Python | .Net Go Java JavaScript (Node.js) PowerShell Python R | Java PHP Python | C C# C++ Erlang Go Java JavaScript Lua Perl PHP Python Rust | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Server-side scripts Stored procedures | yes user defined functions and integration of map-reduce | user defined functions | Yes, possible languages: KQL, Python, R | yes | Lua, C and SQL stored procedures | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Triggers | no | yes triggers when inserted values for one or more columns fall within a specified range | yes see docs.microsoft.com/en-us/azure/kusto/management/updatepolicy | yes | yes, before/after data modification events, on replication events, client session events | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Partitioning methods Methods for storing different data on different nodes | Sharding | Sharding | Sharding Implicit feature of the cloud service | none | Sharding, partitioned with virtual buckets by user defined affinity key. Live resharding for scale up and scale down without maintenance downtime. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Replication methods Methods for redundantly storing data on multiple nodes | selectable replication factor | Source-replica replication | yes Implicit feature of the cloud service. Replication either local, cross-facility or geo-redundant. | none | Asynchronous replication with multi-master option Configurable replication topology (full-mesh, chain, star) Synchronous quorum replication (with Raft) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
MapReduce Offers an API for user-defined Map/Reduce methods | yes query execution via MapReduce | no | Spark connector (open source): github.com/Azure/azure-kusto-spark | no | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Consistency concepts Methods to ensure consistency in a distributed system | Eventual Consistency | Immediate Consistency or Eventual Consistency depending on configuration | Eventual Consistency Immediate Consistency | Casual consistency across sharding partitions Eventual consistency within replicaset partition when using asyncronous replication Immediate Consistency within single instance Sequential consistency including linearizable read within replicaset partition when using Raft | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Foreign keys Referential integrity | no | yes | no | yes | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Transaction concepts Support to ensure data integrity after non-atomic manipulations of data | no | no | no | ACID Isolation support depends on the API used | ACID, with serializable isolation and linearizable read (within partition); Configurable MVCC (within partition); No cross-shard distributed transactions | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Concurrency Support for concurrent manipulation of data | yes | yes | yes | yes | yes, cooperative multitasking | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Durability Support for making data persistent | yes | yes | yes | yes in-memory storage is supported as well | yes, write ahead logging | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
In-memory capabilities Is there an option to define some or all structures to be held in-memory only. | yes GPU vRAM or System RAM | no | yes, full featured in-memory storage engine with persistence | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
User concepts Access control | Access rights for users, groups and roles | Access rights for users and roles on table level | Azure Active Directory Authentication | no | Access Control Lists Mutual TLS authentication for Tarantol Enterprise Password based authentication Role-based access control (RBAC) and LDAP for Tarantol Enterprise Users and Roles | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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More resources | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Hive | Kinetica | Microsoft Azure Data Explorer | RDF4J formerly known as Sesame | Tarantool | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
DB-Engines blog posts | Why is Hadoop not listed in the DB-Engines Ranking? | Data processing speed and reliability: in-memory synchronous replication Apache Software Foundation Announces Apache® Hive 4.0 ASF Unveils the Next Evolution of Big Data Processing With the Launch of Hive 4.0 Run Apache Hive workloads using Spark SQL with Amazon EMR on EKS | Amazon Web Services Apache Hive 4.0 Launches, Revolutionizing Data Management and Analysis 18 Top Big Data Tools and Technologies to Know About in 2024 provided by Google News Kinetica Elevates RAG with Fast Access to Real-Time Data Kinetica Launches Generative AI Solution for Real-Time Inferencing Powered by NVIDIA AI Enterprise Kinetica ramps up RAG for generative AI, empowering enterprises with real-time operational data Kinetica Delivers Real-Time Vector Similarity Search Transforming spatiotemporal data analysis with GPUs and generative AI provided by Google News Public Preview: Azure Data Explorer connector for Apache Flink | Azure updates Providing modern data transfer and storage service at Microsoft with Microsoft Azure - Inside Track Blog Azure Data Explorer: Log and telemetry analytics benchmark Introducing Microsoft Fabric: The data platform for the era of AI | Microsoft Azure Blog Microsoft Introduces Azure Integration Environments and Business Process Tracking in Public Preview provided by Google News GraphDB Goes Open Source Ontotext's GraphDB 8.10 Makes Knowledge Graph Experience Faster and Richer provided by Google News In-Memory Showdown: Redis vs. Tarantool TaranHouse: New Big Data Warehouse Announced by Tarantool Deploying Tarantool Cartridge applications with zero effort (Part 1) Deploying Tarantool Cartridge applications with zero effort (Part 2) Тarantool Cartridge: Sharding Lua Backend in Three Lines provided by Google News |
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