Mission-critical we are including
Posted: Wed Feb 19, 2025 8:40 am
AlwaysOn” technology, offering a solution to manage high availability groups for clusters , mirroring , log s hipping and diagnostics , offering multiple secondary servers in active mode and multiple databases for fault tolerance, scalability on demand and distributing workloads across secondary servers. Offering a performance of more than 57,000 transactions per second and 100,000,000 per day, with improved speed thanks to ColumnStore Index technology.
Hadoop is an open source software project that provides a framework to enable Argentina Mobile Database distributed processing of large data sets on clusters built with commodity hardware. At its core, Hadoop consists of two building blocks: a distributed file system (Hadoop Distributed File System, HDFS) and a data processing engine that implements the Map/Reduce model (Hadoop MapReduce). However, as it has gained adoption and maturity, technologies have also been created to complement it and expand its usage scenarios, so that today the name “Hadoop” does not refer to a single tool but to a family of tools around HDFS and MapReduce.
In this article I provide a map of these technologies with the aim of explaining how a technological stack for Big Data based on Hadoop is formed—at least today.
To explain the different components, I have classified them into different layers: data storage and access, processing, scheduling, serialization, data management and integration. Below I explain the purpose of each one, and the technologies behind them.
Hadoop is an open source software project that provides a framework to enable Argentina Mobile Database distributed processing of large data sets on clusters built with commodity hardware. At its core, Hadoop consists of two building blocks: a distributed file system (Hadoop Distributed File System, HDFS) and a data processing engine that implements the Map/Reduce model (Hadoop MapReduce). However, as it has gained adoption and maturity, technologies have also been created to complement it and expand its usage scenarios, so that today the name “Hadoop” does not refer to a single tool but to a family of tools around HDFS and MapReduce.
In this article I provide a map of these technologies with the aim of explaining how a technological stack for Big Data based on Hadoop is formed—at least today.
To explain the different components, I have classified them into different layers: data storage and access, processing, scheduling, serialization, data management and integration. Below I explain the purpose of each one, and the technologies behind them.