My main use case for Rivery involves collecting data from different sources, and with Rivery, I am able to put them together and load the data directly in Snowflake.
I have been using Rivery for two years. My main use case for Rivery is data ingestion and transformation workflows with Snowflake. A quick, specific example of how I use Rivery with Snowflake in my workflows is managing pipelines, monitoring data loads, and the performance for larger data sets.
My main use case for Rivery is to target and source from various data sources. A specific example of how I use Rivery is that the advantage lies in the ability to approach every data project easily because it has so many connectors. I have used them for taking data from Facebook, Google, YouTube, Instagram, and other platforms. In addition to my main use case, I also take data from Monday and different databases. I knew that it would not be a problem when I needed to take data from different sources.
My company has started to use the Rivery extract data from Hive. It is like a project management sort of program, and we started to use Rivery to get the data from there over into Mavenlink, so we were just trying to find data solutions for what we did with the tool for a little while. The tool was a sort of ETL extracting data product, and getting it over to the other programs and using it as a sort of a data cleanup and flow sort of thing was needed.
Director of Business Intelligence at a consultancy with self employed
Real User
Top 20
Jul 19, 2021
Our use case of this solution is as an ETL tool, or in the category of an IPAS, integration platform as a service. We use this to ingest data. Rivery is also our scheduler to run all our data procedures on our ETL engine. I'm a machine learning and data engineer and we are customers of Rivery.
Rivery is a serverless, SaaS DataOps platform that empowers companies of all sizes around the world to consolidate, orchestrate, and manage internal and external data sources with ease and efficiency.
By offering comprehensive data solutions and partnering with complementary technology providers, including Google, Snowflake, Tableau, and Looker, Rivery enables data-driven companies to build the perfect ecosystems for all their data processes.
My main use case for Rivery involves collecting data from different sources, and with Rivery, I am able to put them together and load the data directly in Snowflake.
I have been using Rivery for two years. My main use case for Rivery is data ingestion and transformation workflows with Snowflake. A quick, specific example of how I use Rivery with Snowflake in my workflows is managing pipelines, monitoring data loads, and the performance for larger data sets.
My main use case for Rivery is to target and source from various data sources. A specific example of how I use Rivery is that the advantage lies in the ability to approach every data project easily because it has so many connectors. I have used them for taking data from Facebook, Google, YouTube, Instagram, and other platforms. In addition to my main use case, I also take data from Monday and different databases. I knew that it would not be a problem when I needed to take data from different sources.
My company has started to use the Rivery extract data from Hive. It is like a project management sort of program, and we started to use Rivery to get the data from there over into Mavenlink, so we were just trying to find data solutions for what we did with the tool for a little while. The tool was a sort of ETL extracting data product, and getting it over to the other programs and using it as a sort of a data cleanup and flow sort of thing was needed.
Our use case of this solution is as an ETL tool, or in the category of an IPAS, integration platform as a service. We use this to ingest data. Rivery is also our scheduler to run all our data procedures on our ETL engine. I'm a machine learning and data engineer and we are customers of Rivery.