Webinar: Building a real-time analytics pipeline with BigQuery and Cloud Dataflow (EMEA)
42 172
31.8
Google Cloud Platform1.16 млн
Следующее
07.09.16 – 14 0430:54Популярные
6 дней – 7280:57
131 день – 15 81635:31Опубликовано 7 сентября 2016, 10:48
Join the live chat Q&A at: cloudwebinars.withgoogle.com/l...
Real-time ingestion and analysis of data streams is advantageous for organizations handling clickstreams, logs, or IOT data sources. However making the move from batch to stream processing has historically been difficult to do. No longer. In this webinar Eric Schmidt, Developer Advocate for Google Cloud Platform will explain how three key pieces of technology – BigQuery, Cloud Pub/Sub, and Cloud Dataflow – come together to make fast and scalable streaming data analytics possible in highly dynamic, distributed environments.
Eric will dive into the benefits of moving your ETL (extract, transform and load) processes into the cloud using these technologies and why it makes sense for your business. This webinar will set you up with the basics for how to get started with real time analytics.
Real-time ingestion and analysis of data streams is advantageous for organizations handling clickstreams, logs, or IOT data sources. However making the move from batch to stream processing has historically been difficult to do. No longer. In this webinar Eric Schmidt, Developer Advocate for Google Cloud Platform will explain how three key pieces of technology – BigQuery, Cloud Pub/Sub, and Cloud Dataflow – come together to make fast and scalable streaming data analytics possible in highly dynamic, distributed environments.
Eric will dive into the benefits of moving your ETL (extract, transform and load) processes into the cloud using these technologies and why it makes sense for your business. This webinar will set you up with the basics for how to get started with real time analytics.
Свежие видео
2 часа – 1 40911:33
4 дня – 119 6655:32
4 дня – 3108:48
10 дней – 13 8100:44
15 дней – 215 3040:26Случайные видео
235 дней – 334 4540:59
10.05.23 – 3433:55
09.01.23 – 1 9270:11
17.08.22 – 22 91737:27
30.03.21 – 1 838 98311:06
17.12.13 – 35 122 5541:31
7 часов – 30 32816:16
3 часа – 1570:51
6 часов – 601:52
3 дня – 13 1495:20
4 дня – 53510:56
4 дня – 424 1630:58