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DBMS > Apache Impala vs. dBASE vs. etcd vs. Google Cloud Bigtable vs. IRONdb

System Properties Comparison Apache Impala vs. dBASE vs. etcd vs. Google Cloud Bigtable vs. IRONdb

Editorial information provided by DB-Engines
NameApache Impala  Xexclude from comparisondBASE  Xexclude from comparisonetcd  Xexclude from comparisonGoogle Cloud Bigtable  Xexclude from comparisonIRONdb  Xexclude from comparison
IRONdb seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.
DescriptionAnalytic DBMS for HadoopdBase was one of the first databases with a development environment on PC's. Its latest version dBase V is still sold as dBase classic, which needs a DOS Emulation. The up-to-date product is dBase plus.A distributed reliable key-value storeGoogle's NoSQL Big Data database service. It's the same database that powers many core Google services, including Search, Analytics, Maps, and Gmail.A distributed Time Series DBMS with a focus on scalability, fault tolerance and operational simplicity
Primary database modelRelational DBMSRelational DBMSKey-value storeKey-value store
Wide column store
Time Series DBMS
Secondary database modelsDocument store
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score12.45
Rank#40  Overall
#24  Relational DBMS
Score9.70
Rank#44  Overall
#28  Relational DBMS
Score7.03
Rank#54  Overall
#5  Key-value stores
Score3.15
Rank#95  Overall
#14  Key-value stores
#8  Wide column stores
Websiteimpala.apache.orgwww.dbase.cometcd.io
github.com/­etcd-io/­etcd
cloud.google.com/­bigtablewww.circonus.com/solutions/time-series-database/
Technical documentationimpala.apache.org/­impala-docs.htmlwww.dbase.com/­support/­knowledgebaseetcd.io/­docs
github.com/­etcd-io/­etcd/­tree/­master/­Documentation
cloud.google.com/­bigtable/­docsdocs.circonus.com/irondb/category/getting-started
DeveloperApache Software Foundation infoApache top-level project, originally developed by ClouderaAsthon TateGoogleCirconus LLC.
Initial release2013197920152017
Current release4.1.0, June 2022dBASE 2019, 20193.4, August 2019V0.10.20, January 2018
License infoCommercial or Open SourceOpen Source infoApache Version 2commercialOpen Source infoApache Version 2.0commercialcommercial
Cloud-based only infoOnly available as a cloud servicenononoyesno
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageC++GoC and C++
Server operating systemsLinuxDOS infodBase Classic
Windows infodBase Pro
FreeBSD
Linux
Windows infoexperimental
hostedLinux
Data schemeyesyesschema-freeschema-freeschema-free
Typing infopredefined data types such as float or dateyesyesnonoyes infotext, numeric, histograms
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 indexesyesyesnonono
SQL infoSupport of SQLSQL-like DML and DDL statementsnononoSQL-like query language (Circonus Analytics Query Language: CAQL)
APIs and other access methodsJDBC
ODBC
none infoThe IDE can access other DBMS or ODBC-sources.gRPC
JSON over HTTP
gRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)
HTTP API
Supported programming languagesAll languages supporting JDBC/ODBCdBase proprietary IDE.Net
C
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby
Rust
Scala
Tcl
C#
C++
Go
Java
JavaScript (Node.js)
Python
.Net
C
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript
JavaScript (Node.js)
Lisp
Lua
Perl
PHP
Python
R
Ruby
Rust
Scala
Server-side scripts infoStored proceduresyes infouser defined functions and integration of map-reduceno infoThe IDE can access stored procedures in other database systems.nonoyes, in Lua
Triggersnonoyes, watching key changesnono
Partitioning methods infoMethods for storing different data on different nodesShardingnoneShardingAutomatic, metric affinity per node
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication factornoneUsing Raft consensus algorithm to ensure data replication with strong consistency among multiple replicas.Internal replication in Colossus, and regional replication between two clusters in different zonesconfigurable replication factor, datacenter aware
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes infoquery execution via MapReducenonoyesno
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual ConsistencyImmediate ConsistencyImmediate consistency (for a single cluster), Eventual consistency (for two or more replicated clusters)Immediate consistency per node, eventual consistency across nodes
Foreign keys infoReferential integritynoyesnonono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanono infonot for dBase internal data, but IDE does support transactions when accessing external DBMSnoAtomic single-row operationsno
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes
Durability infoSupport for making data persistentyesyesyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.nononono
User concepts infoAccess controlAccess rights for users, groups and roles infobased on Apache Sentry and KerberosAccess rights for users and rolesnoAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)no

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More resources
Apache ImpaladBASEetcdGoogle Cloud BigtableIRONdb
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