Hevo is mainly used for data integration purposes, and I use it to form a pipeline or a gateway where data is collected from various data collection platforms or sources. Hevo is a data integration platform that I use as a gateway and connector when data is collected from various sources and platforms such as CRM or sales and marketing platforms, and it helps transfer my data into various storage platforms such as Google BigQuery. It ensures that the integration of my data is efficient, allowing various users to utilize it effectively in an automated manner. A specific example I can share is when we hold events to map customer support or marketing trends, where we meet potential clients or participate in exhibitions, collecting their data and onboarding them with their preferences. With this information, we store it for future reference, enabling background checks when clients return or when engaging with them. Another key use case for Hevo is the ETL process, which simplifies how my teams handle data by providing prebuilt connectors and an easy interface for creating pipelines, allowing engineers or analysts to move data faster and efficiently execute data recovery processes.
My main use case for Hevo is moving data from a series of different databases into a Snowflake data warehouse.I can give you a specific example of how I use Hevo to move data between databases and Snowflake. We had one software application that had over 500 customers; however, that application, even though it was a SaaS app, had one Azure SQL Server database per customer, and in those databases, there were four different schemas. I needed to get that data from that application, as well as data from other applications using different databases, different public clouds, and different infrastructure into Snowflake. Using Hevo, I tested that it worked well to move data, made four pipelines for one database manually, and then scripted that using the Hevo API to create effectively 2,000 pipelines for this one application. We had all the data from 500 Azure SQL Server databases with a similar structure into a single Snowflake data warehouse, transforming some of the data to prevent it from overwriting itself from all the different customers. This specific example illustrates how I effectively used Hevo for that application with 500 Azure SQL Server databases, four different schemas in each database, making 2,000 Hevo pipelines, turning on change tracking in the source database, specifying the tables and fields needed, mapping it out to our destination data warehouse in Snowflake, and letting those pipelines run.
Data Integration facilitates the combination of data from diverse sources into a unified view, crucial for businesses to make informed decisions and enhance operational efficiency. With comprehensive solutions available, organizations can streamline their data workflows. Data Integration solutions are vital for businesses aiming to handle large volumes of data efficiently. These solutions help in synchronizing data from multiple sources, ensuring consistent data across platforms, and...
Hevo is mainly used for data integration purposes, and I use it to form a pipeline or a gateway where data is collected from various data collection platforms or sources. Hevo is a data integration platform that I use as a gateway and connector when data is collected from various sources and platforms such as CRM or sales and marketing platforms, and it helps transfer my data into various storage platforms such as Google BigQuery. It ensures that the integration of my data is efficient, allowing various users to utilize it effectively in an automated manner. A specific example I can share is when we hold events to map customer support or marketing trends, where we meet potential clients or participate in exhibitions, collecting their data and onboarding them with their preferences. With this information, we store it for future reference, enabling background checks when clients return or when engaging with them. Another key use case for Hevo is the ETL process, which simplifies how my teams handle data by providing prebuilt connectors and an easy interface for creating pipelines, allowing engineers or analysts to move data faster and efficiently execute data recovery processes.
My main use case for Hevo is moving data from a series of different databases into a Snowflake data warehouse.I can give you a specific example of how I use Hevo to move data between databases and Snowflake. We had one software application that had over 500 customers; however, that application, even though it was a SaaS app, had one Azure SQL Server database per customer, and in those databases, there were four different schemas. I needed to get that data from that application, as well as data from other applications using different databases, different public clouds, and different infrastructure into Snowflake. Using Hevo, I tested that it worked well to move data, made four pipelines for one database manually, and then scripted that using the Hevo API to create effectively 2,000 pipelines for this one application. We had all the data from 500 Azure SQL Server databases with a similar structure into a single Snowflake data warehouse, transforming some of the data to prevent it from overwriting itself from all the different customers. This specific example illustrates how I effectively used Hevo for that application with 500 Azure SQL Server databases, four different schemas in each database, making 2,000 Hevo pipelines, turning on change tracking in the source database, specifying the tables and fields needed, mapping it out to our destination data warehouse in Snowflake, and letting those pipelines run.