GitHub and Azure PostgreSQL Integration

Data Sync in Any Direction

Skyvia Import allows you to create ETL pipelines to automate data loading in any direction. It supports both ETL scenarios — loading data from GitHub to Azure PostgreSQL, and reverse ETL from Azure PostgreSQL to GitHub tables such as entities. For extracting data, Import has simple mode that queries data from a single object/table and advanced mode extracting data via SQL or visually built queries. It supports powerful pre-load data transformations, including data splitting, lookups, relation mapping, and custom expression language including various string, mathematical, datetime functions, etc. All ETL scenarios can be scheduled for automatic execution.

Skyvia can both load all the legacy data using bulk operations and only new and updated data. It supports all DML operation, including the UPSERT operation that allows you to load data without creating duplicates. Ability to load only new and updated data together with the UPSERT operation support makes Skyvia Import a perfect tool for data synchronization.

Bidirectional Synchronization

Import allows you to sync data in any directions, so you can configure two import integrations to load data from GitHub to Azure PostgreSQL, and from Azure PostgreSQL to GitHub respectfully. However, Skyvia also provides a dedicated tool to sync GitHub and Azure PostgreSQL in both directions. Synchronization supports all the advanced mapping features of Import — lookups, expressions, etc., and it is best suited for cases when one of the data sources is empty. If the data sources are already partially synced, use Import.

Initial synchronization run loads all the data. Subsequent synchronizations load only changed data and apply the necessary changes from source to target and vice versa, reducing the number of billable records. So it's up to you whether to choose Import or Sync for your data synchronization tasks.

GitHub Data Replication to Azure PostgreSQL

Replicate GitHub entities to Azure PostgreSQL and keep it up-to-date automatically! Combine raw data from GitHub and other cloud app connectors in your Azure PostgreSQL database and turn Azure PostgreSQL into a single source of truth.

Data Replication is an easy-to-use ELT tool that helps you configure data ingestion from GitHub in Azure PostgreSQL in just a few clicks. Skyvia automatically creates tables for your cloud data. All you need is to specify the connections to GitHub and Azure PostgreSQL and select data to replicate, and Skyvia does the rest. It will import GitHub data to Azure PostgreSQL and maintain this copy up-to-date automatically with incremental updates. Configure data pipelines once, and you'll always have fresh cloud data in Azure PostgreSQL, to which you can connect your BI tools, analyze your data, and do reporting.

Advanced Data Pipelines

For even more advanced scenarios and ETL pipelines, you can use Data Flow and Control Flow tools. They are designer-based solutions for creating complex data pipelines, involving not only GitHub and Azure PostgreSQL connectors, but any number of connectors in one integration. With these tools you can automate data workflows, involving custom logic, complex multistage transformations, combine data from different sources and load them into multiple destinations at once and many more.

Workflow Automation

Automate workflows between GitHub and Azure PostgreSQL with Skyvia Automation. Automation is a designer-based solution that allows you to perform actions in data sources when the specified trigger event happen. The trigger event can be a new record in a data source, a manual run, or a scheduled event.

Automation supports advanced data mapping. It allows you to include not only GitHub and Azure PostgreSQL, but any number of data sources. You can perform actions in multiple data sources on one trigger or enrich data from other sources on the way.

Not only Automation allows you to automate your data-related tasks without coding, it provides such development tools as version control and debug mode. These tools surely help designing automations with no effort. Besides, there is a library of predefined examples that can help you to get started.