Version 22.0.8483


Performance is critical to ensure that business teams can access data in a timely fashion. When Sync moves large amounts of data from sources that might have slow-responding APIs, the application uses the capabilities that are explained in the following sections to improve data integrity and to maximize job performance.

Best-in-Class Connectors

Demands that are created by analytics and cognitive computing mean that applications must be able to retrieve and process massive data sets at speeds that were once inconceivable. Sync meets that challenge with best-in-class connectors that are over twice as fast as other software-as-a-service (SaaS), NoSQL, and big-data connectivity solutions. Sync gives you the speed and reliability that you need to operate at peak efficiency in the era of big data.

Batch-Insert Process

Sync uses a batch-insert process to insert multiple rows. Rather than inserting one row at a time, Sync uses a single statement to insert a batch of rows simultaneously into the database. This method greatly reduces network inefficiency and increases the speed with which jobs are processed.

MERGE Action

When Sync moves data from the source to the destination, the application uses a MERGE action to insert the data first into a temporary table before the data is integrated into the destination table.

Using a temporary table greatly improves the application’s efficiency in moving data. Unlike an upsert process, which requires you to read the target table each time per row, the MERGE action is executed within a single transaction. That is, the process reads the target one time and determines which rows should be updated and which rows should be inserted.

This process improves performance and prevents Sync from locking the production table during the job run. In the event of an error, Sync can simply drop the temporary table instead of potentially corrupting the production table.

Parallel Processing

You can configure Sync jobs to use parallel processing, which means that the application uses multiple worker threads to process one job. With parallel processing, Sync can divide its workload into multiple processes, allowing it to move more than one table simultaneously. As a result, more data is moved in less time, greatly increasing job efficiency. With Sync, you can assign as many workers as you want, on a per-job basis.

Multiple Concurrent Readers

For source connections that allow it, Sync creates multiple concurrent readers to read data from the source API. Multiple readers enable Sync to read data concurrently from the same table, which further improves performance in moving data through your data pipeline.


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