Название: Building Real-Time Analytics Applications: Operational Workflows with Apache Druid
Автор: Darin Briskman
Издательство: O’Reilly Media, Inc.
Год: 2023-02-03
Язык: английский
Формат: pdf, epub, mobi
Размер: 10.2 MB
This report will introduce you to the real-time analytics applications that organizations are building to power new operational workflows. You’ll learn about the purposes of these applications, the value they provide, and the technologies needed to create them. Once you’ve completed this report, you’ll know how real-time analytics applications can help your organization, and you’ll know what you’ll need to create a solution that works for you. Speed is critical for real-time analytics applications: delivering data in milliseconds isn’t useful unless data queries also execute in milliseconds. A real-time analytics database must be able to both ingest incoming events and process queries with subsecond performance, even for large data sets of hundreds of terabytes or petabytes. How quickly can data be added to the database? All databases support moving sets of records from files into the database, usually known as batch ingestion. For many analytics databases, such as Snowflake and Amazon Redshift, this is the only method of data ingestion. Only a few databases designed for real-time data analytics, such as Apache Druid, can perform both batch ingestion and stream ingestion, with each event becoming immediately available for queries as soon as it arrives.
Автор: Darin Briskman
Издательство: O’Reilly Media, Inc.
Год: 2023-02-03
Язык: английский
Формат: pdf, epub, mobi
Размер: 10.2 MB
This report will introduce you to the real-time analytics applications that organizations are building to power new operational workflows. You’ll learn about the purposes of these applications, the value they provide, and the technologies needed to create them. Once you’ve completed this report, you’ll know how real-time analytics applications can help your organization, and you’ll know what you’ll need to create a solution that works for you. Speed is critical for real-time analytics applications: delivering data in milliseconds isn’t useful unless data queries also execute in milliseconds. A real-time analytics database must be able to both ingest incoming events and process queries with subsecond performance, even for large data sets of hundreds of terabytes or petabytes. How quickly can data be added to the database? All databases support moving sets of records from files into the database, usually known as batch ingestion. For many analytics databases, such as Snowflake and Amazon Redshift, this is the only method of data ingestion. Only a few databases designed for real-time data analytics, such as Apache Druid, can perform both batch ingestion and stream ingestion, with each event becoming immediately available for queries as soon as it arrives.