Copy (ELT) Azure MySQL data to Google BigQuery in a few clicks and keep it up to date automatically.
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If needed, configure replication settings — exclude certain columns, change table naming, apply data hashing, add filters for data.
Configure scheduling and run replication every month, week or day, at specific time or recurrently at any interval, up to once per minute.
View all the integration runs for any period, get email notifications and detailed per-record error logs in case of errors to help you be aware of the situation.
Many businesses want to benefit from the Google BigQuery ability to quickly perform complex analytical queries over petabytes of data, and need to load their data from Azure MySQL and other applications to the Google BigQuery service for centralized storing and data analysis. Skyvia's Replication is the perfect tool for secure replication to a data warehouse, as it loads data much faster than standard ETL tools and allows you to copy Azure MySQL data to Google BigQuery in just a few clicks and keep this copy up to date.
You don’t need to prepare the Google BigQuery database — Skyvia creates the tables, corresponding to the Azure MySQL tables, in the data warehouse automatically. Besides, it can automatically modify target Google BigQuery tables according to new column added in Azure MySQL.
With Skyvia you can extract and load all the data from a Azure MySQL table or disable loading for some Azure MySQL columns. You can also configure filters for data to replicate.
Skyvia allows you to hash sensitive data, like names and emails. You can conceal personally identifiable information for better compliance and preserve analytical value of replicated data at the same time.
Skyvia can keep your Google BigQuery database up-to-date with Azure MySQL automatically. It can detect changes using timestamp or autoincrement columns. If table does not have such columns, you can do full resync every time or on demand.
History mode allows you to store all versions of Azure MySQL records in your Google BigQuery database instead of keeping only the current versions. You can select the mode for each table separately.
By default, target tables are named in the same way as in the source. Skyvia offers settings that allow you to change target table names — for all Azure MySQL tables in the integration or for each one separately. This is especially useful if you load several Azure MySQL instances into the same Google BigQuery database, and want to use separate tables for them.
Skyvia combines a high-performance optimized batch data loading into Google BigQuery with a granular, per-record data insertion and error logging in case of any errors.
If you need more than just to copy data from Azure MySQL to Google BigQuery, you may use Skyvia's powerful ETL functionality for Azure MySQL and Google BigQuery integration. Skyvia's Import tool will correctly extract Azure MySQL data, transform it, and load to Google BigQuery when Google BigQuery tables have different structure than Azure MySQL tables. Besides, if you ever need to load data back from Google BigQuery to Azure MySQL, Skyvia Import allows loading data in any direction, supporting Reverse ETL scenario.
For more complex cases, you can use Data Flow and Control Flow tools. They are designer-based solutions for creating complex data pipelines, involving not only Azure MySQL and Google BigQuery 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, load them into multiple destinations at once and many more.
Use flexible scheduling settings to automate replication
You can find detailed logs for each execution in the package Run History.
Enable email notifications and always know if anything goes wrong.
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Integrate Azure MySQL and Google BigQuery data with minimal effort with Skyvia.
