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ArangoDB is a transactional native multi-model database supporting two major NoSQL data models (graph and document [1]) with one query language. Written in C++ and optimized for in-memory computing. In addition ArangoDB integrated RocksDB for persistent storage. ArangoDB supports Java, JavaScript, Python, PHP, NodeJS, C++ and Elixir.
CockroachDB is a source-available distributed SQL database management system developed by Cockroach Labs. [2] [3]The relational functionality is built on top of a distributed, transactional, consistent key-value store that can survive a variety of different underlying infrastructure failures, and is wire-compatible with PostgreSQL which means users can take advantage of a wide range of drivers ...
SingleStore (formerly MemSQL) is a distributed, relational, SQL database management system [2] (RDBMS) that features ANSI SQL support, it is known for speed in data ingest, transaction processing, and query processing. [3] [4] SingleStore stores relational data, JSON data, geospatial data, key-value vector data, and time series data.
A transactional database is a DBMS that provides the ACID properties for a bracketed set of database operations (begin-commit). Transactions ensure that the database is always in a consistent state, even in the event of concurrent updates and failures. [ 2 ]
These interpretations suggest different advantages, one being a database functionality. Recent advances in research, hardware, OLTP and OLAP capabilities, in-memory and cloud native database technologies, [8] scalable transactional management and products enable transactional processing and analytics, or HTAP, to operate on the same database ...
Cloud Spanner Booth at Google Cloud Summit. Spanner is a distributed SQL database management and storage service developed by Google. [1] It provides features such as global transactions, strongly consistent reads, and automatic multi-site replication and failover.
In-database processing, sometimes referred to as in-database analytics, refers to the integration of data analytics into data warehousing functionality. Today, many large databases, such as those used for credit card fraud detection and investment bank risk management, use this technology because it provides significant performance improvements over traditional methods.
Real-time databases may be modified to improve accuracy and efficiency and to avoid conflict, by providing deadlines and wait periods to insure temporal consistency. Real-time database systems offer a way of monitoring a physical system and representing it in data streams to a database. A data stream, like memory, fades over time.