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DBMS > Apache Impala vs. Databend vs. Google Cloud Bigtable

System Properties Comparison Apache Impala vs. Databend vs. Google Cloud Bigtable

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Editorial information provided by DB-Engines
NameApache Impala  Xexclude from comparisonDatabend  Xexclude from comparisonGoogle Cloud Bigtable  Xexclude from comparison
DescriptionAnalytic DBMS for HadoopAn open-source, elastic, and workload-aware cloud data warehouse designed to meet businesses' massive-scale analytics needs at low cost and with low complexityGoogle's NoSQL Big Data database service. It's the same database that powers many core Google services, including Search, Analytics, Maps, and Gmail.
Primary database modelRelational DBMSRelational DBMSKey-value store
Wide column store
Secondary database modelsDocument store
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score13.77
Rank#40  Overall
#24  Relational DBMS
Score0.30
Rank#287  Overall
#130  Relational DBMS
Score3.26
Rank#92  Overall
#13  Key-value stores
#8  Wide column stores
Websiteimpala.apache.orggithub.com/­datafuselabs/­databend
www.databend.com
cloud.google.com/­bigtable
Technical documentationimpala.apache.org/­impala-docs.htmldocs.databend.comcloud.google.com/­bigtable/­docs
DeveloperApache Software Foundation infoApache top-level project, originally developed by ClouderaDatabend LabsGoogle
Initial release201320212015
Current release4.1.0, June 20221.0.59, April 2023
License infoCommercial or Open SourceOpen Source infoApache Version 2Open Source infoApache Version 2.0commercial
Cloud-based only infoOnly available as a cloud servicenonoyes
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageC++Rust
Server operating systemsLinuxhosted
Linux
macOS
hosted
Data schemeyesyesschema-free
Typing infopredefined data types such as float or dateyesyesno
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.nonono
Secondary indexesyesnono
SQL infoSupport of SQLSQL-like DML and DDL statementsyesno
APIs and other access methodsJDBC
ODBC
CLI Client
JDBC
RESTful HTTP API
gRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)
Supported programming languagesAll languages supporting JDBC/ODBCGo
Java
JavaScript (Node.js)
Python
Rust
C#
C++
Go
Java
JavaScript (Node.js)
Python
Server-side scripts infoStored proceduresyes infouser defined functions and integration of map-reducenono
Triggersnonono
Partitioning methods infoMethods for storing different data on different nodesShardingnoneSharding
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication factornoneInternal replication in Colossus, and regional replication between two clusters in different zones
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes infoquery execution via MapReducenoyes
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)
Foreign keys infoReferential integritynonono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoyesAtomic single-row operations
Concurrency infoSupport for concurrent manipulation of datayesyesyes
Durability infoSupport for making data persistentyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.nono
User concepts infoAccess controlAccess rights for users, groups and roles infobased on Apache Sentry and KerberosUsers with fine-grained authorization concept, user rolesAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

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More resources
Apache ImpalaDatabendGoogle Cloud Bigtable
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