Copy (ELT) Paddle data to Amazon Redshift in a few clicks and keep it up to date automatically.
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If needed, configure replication settings — exclude certain fields, 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 Amazon Redshift ability to quickly perform complex analytical queries over petabytes of data, and need to load their data from Paddle and other applications to the Amazon Redshift 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 Paddle objects (including Subscriptions, Transactions, etc.) to Amazon Redshift in just a few clicks and keep this copy up to date.
You don’t need to prepare the Amazon Redshift database — Skyvia creates the tables, corresponding to the Paddle objects, in the data warehouse automatically. Besides, it can automatically modify target Amazon Redshift tables according to new fields added in Paddle.
With Skyvia you can extract and load all the data from a Paddle object or disable loading for some Paddle object fields. 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 not just copies Paddle data to Amazon Redshift once, it can keep your Amazon Redshift database up-to-date with Paddle automatically, ensuring you always have fresh data for data analysis without any user intervention.
History mode allows you to store all versions of Paddle records in your Amazon Redshift database instead of keeping only the current versions. You can select the mode for each object separately.
By default, target tables are named in the same way as source objects. Skyvia offers settings that allow you to change target table names — for all Paddle objects in the integration or for each one separately. This is especially useful if you load several Paddle instances into the same Amazon Redshift database, and want to use separate tables for them.
Skyvia combines a high-performance optimized batch data loading into Amazon Redshift 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 Paddle to Amazon Redshift, you may use Skyvia's powerful ETL functionality for Paddle and Amazon Redshift integration. Skyvia's Import tool will correctly extract Paddle data, transform it, and load to Amazon Redshift when Amazon Redshift tables have different structure than Paddle objects. Besides, if you ever need to load data back from Amazon Redshift to Paddle, 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 Paddle and Amazon Redshift 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 Paddle and Amazon Redshift data with minimal effort with Skyvia.
