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DBMS > Google Cloud Bigtable vs. Spark SQL vs. Tarantool vs. Yaacomo

System Properties Comparison Google Cloud Bigtable vs. Spark SQL vs. Tarantool vs. Yaacomo

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Editorial information provided by DB-Engines
NameGoogle Cloud Bigtable  Xexclude from comparisonSpark SQL  Xexclude from comparisonTarantool  Xexclude from comparisonYaacomo  Xexclude from comparison
Yaacomo seems to be discontinued and is removed from the DB-Engines ranking
DescriptionGoogle's NoSQL Big Data database service. It's the same database that powers many core Google services, including Search, Analytics, Maps, and Gmail.Spark SQL is a component on top of 'Spark Core' for structured data processingIn-memory computing platform with a flexible data schema for efficiently building high-performance applicationsOpenCL based in-memory RDBMS, designed for efficiently utilizing the hardware via parallel computing
Primary database modelKey-value store
Wide column store
Relational DBMSDocument store
Key-value store
Relational DBMS
Relational DBMS
Secondary database modelsSpatial DBMS infowith Tarantool/GIS extension
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score3.26
Rank#92  Overall
#13  Key-value stores
#8  Wide column stores
Score18.96
Rank#33  Overall
#20  Relational DBMS
Score1.72
Rank#144  Overall
#25  Document stores
#25  Key-value stores
#66  Relational DBMS
Websitecloud.google.com/­bigtablespark.apache.org/­sqlwww.tarantool.ioyaacomo.com
Technical documentationcloud.google.com/­bigtable/­docsspark.apache.org/­docs/­latest/­sql-programming-guide.htmlwww.tarantool.io/­en/­doc
DeveloperGoogleApache Software FoundationVKQ2WEB GmbH
Initial release2015201420082009
Current release3.5.0 ( 2.13), September 20232.10.0, May 2022
License infoCommercial or Open SourcecommercialOpen Source infoApache 2.0Open Source infoBSD-2, source-available extensions (modules), commercial licenses for Tarantool Enterprisecommercial
Cloud-based only infoOnly available as a cloud serviceyesnonono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageScalaC and C++
Server operating systemshostedLinux
OS X
Windows
BSD
Linux
macOS
Android
Linux
Windows
Data schemeschema-freeyesFlexible data schema: relational definition for tables with ability to store json-like documents in columnsyes
Typing infopredefined data types such as float or datenoyesstring, double, decimal, uuid, integer, blob, boolean, datetimeyes
XML support infoSome form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.nononono
Secondary indexesnonoyesyes
SQL infoSupport of SQLnoSQL-like DML and DDL statementsFull-featured ANSI SQL supportyes
APIs and other access methodsgRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)
JDBC
ODBC
Open binary protocolJDBC
ODBC
Supported programming languagesC#
C++
Go
Java
JavaScript (Node.js)
Python
Java
Python
R
Scala
C
C#
C++
Erlang
Go
Java
JavaScript
Lua
Perl
PHP
Python
Rust
Server-side scripts infoStored proceduresnonoLua, C and SQL stored procedures
Triggersnonoyes, before/after data modification events, on replication events, client session eventsyes
Partitioning methods infoMethods for storing different data on different nodesShardingyes, utilizing Spark CoreSharding, partitioned with virtual buckets by user defined affinity key. Live resharding for scale up and scale down without maintenance downtime.horizontal partitioning
Replication methods infoMethods for redundantly storing data on multiple nodesInternal replication in Colossus, and regional replication between two clusters in different zonesnoneAsynchronous replication with multi-master option
Configurable replication topology (full-mesh, chain, star)
Synchronous quorum replication (with Raft)
Source-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsyesno
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate consistency (for a single cluster), Eventual consistency (for two or more replicated clusters)Casual consistency across sharding partitions
Eventual consistency within replicaset partition infowhen using asyncronous replication
Immediate Consistency within single instance
Sequential consistency including linearizable read within replicaset partition infowhen using Raft
Immediate Consistency
Foreign keys infoReferential integritynonoyesyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataAtomic single-row operationsnoACID, with serializable isolation and linearizable read (within partition); Configurable MVCC (within partition); No cross-shard distributed transactionsACID
Concurrency infoSupport for concurrent manipulation of datayesyesyes, cooperative multitaskingyes
Durability infoSupport for making data persistentyesyesyes, write ahead loggingyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.nonoyes, full featured in-memory storage engine with persistenceyes
User concepts infoAccess controlAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)noAccess Control Lists
Mutual TLS authentication for Tarantol Enterprise
Password based authentication
Role-based access control (RBAC) and LDAP for Tarantol Enterprise
Users and Roles
fine grained access rights according to SQL-standard

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More resources
Google Cloud BigtableSpark SQLTarantoolYaacomo
DB-Engines blog posts

Data processing speed and reliability: in-memory synchronous replication
9 November 2021,  Vladimir Perepelytsya, Tarantool (sponsor) 

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