{"id":9551,"date":"2022-09-30T14:33:16","date_gmt":"2022-09-30T07:33:16","guid":{"rendered":"https:\/\/gcloudvn.com\/?p=9551"},"modified":"2023-07-12T14:32:16","modified_gmt":"2023-07-12T07:32:16","slug":"introducing-datastream-for-bigquery","status":"publish","type":"post","link":"https:\/\/gcloudvn.com\/en\/kienthuc\/introducing-datastream-for-bigquery\/","title":{"rendered":"Introducing Datastream for Google BigQuery"},"content":{"rendered":"
\"Datastream
Datastream for Google BigQuery<\/figcaption><\/figure>\n

In today\u2019s competitive environment, organizations need to quickly and easily make decisions based on real-time data. That\u2019s why we\u2019re announcing <\/span>Datastream for BigQuery<\/strong>, <\/span>now available in preview, featuring seamless replication from active database sources such as AlloyDB for PostgreSQL, PostgreSQL, MySQL and Oracle, directly into BigQuery<\/a> \u2013 Google Cloud serverless data warehouse. Datastream for BigQuery is Google's next big step toward realizing our vision of a unified data cloud, bringing together database, analytics, and machine learning into a single platform that delivers at scale. , speed, security and simplicity that modern businesses need. With a serverless autoscaling architecture, Datastream allows you to easily set up an ELT (Extract, Load, Transform) pipeline to replicate data with low latency allowing for insights on real time.\u00a0<\/span><\/p>\n

Consider the case of a large supermarket chain, with 100\u2019s of stores spread out across the region. Each individual store runs its own local point-of-sale and stock management systems, collecting data throughout the day about transactions and stock-levels in the store. To provide visibility and help streamline the chain\u2019s daily operations, the IT department set up a nightly batch process to collect and consolidate all of the data from the stores into a central data warehouse, so that reports on the stores\u2019 performance could be ready for review in the morning. Maintaining this process took time and resources from the data engineering team, and as the chain grows and more data needs to be processed, this process ended up taking so long that reports would only be ready late into the day. Organizations like this are looking for a modern solution that allows effortless replication of operational data to their data warehouse, enabling real-time decision making; Datastream for BigQuery is that solution.<\/span><\/p>\n

> Reference:\u00a0<\/strong><\/p>\n