High-Volume Agent SAP ECC on SQL Server Beta

SAP ECC on SQL Server is an enterprise resource planning and analytics platform built on top of SQL Server. Fivetran replicates data from your SQL Server source database and loads it into your destination using High-Volume Agent connector.

Supported services

Fivetran supports the Generic SQL Server database service.

Supported configurations

Fivetran supports the following SQL Server configurations:

IMPORTANT: We do not support single-user mode.

Limitations

We do not support single-user mode.

Features

Setup guide

Follow our step-by-step High-Volume Agent SAP ECC on SQL Server setup guide for specific instructions on how to set up your SQL Server database with the High-Volume Agent connector.

Sync overview

Once connected to your database, the Fivetran connector runs an initial sync, pulling a full dump of selected data from your database and sending it to your destination. After a successful initial sync, the connector runs in an incremental sync mode. In this mode, Fivetran automatically detects new or updated data, such as new tables or data type changes, and persists these changes into your destination. We use log-based capture to extract your database's change data, then process and load these changes at regular intervals, ensuring a consistently updated synchronization between your database and destination.

NOTE: Choosing a 1-minute sync frequency does not guarantee that your sync finishes within one minute.

Syncing empty tables and columns

Fivetran can sync empty tables for your HVA SAP ECC on SQL Server connector.

We can also sync empty columns in most cases. However, if you don't add rows after you create a new column, we cannot sync that new column. We need at least one row to see a new column because we learn of changes to a table's column cardinality when we see a row with a new or removed column during an update.

For more information, see our Features documentation.

Schema information

Fivetran tries to replicate the exact schema and tables from your database to your destination.

Fivetran-generated columns

Fivetran adds the following columns to every table in your destination:

• _fivetran_deleted (BOOLEAN) marks rows that were deleted in the source table
• _fivetran_id (STRING) is a unique ID that Fivetran uses to avoid duplicate rows in tables that do not have a primary key
• _fivetran_synced (UTC TIMESTAMP) indicates the time when Fivetran last successfully synced the row

We add these columns to give you insight into the state of your data and the progress of your data syncs. For more information about these columns, see our System Columns and Tables documentation.

Type transformation and mapping

As we extract your data, we match SQL Server data types to data types that Fivetran supports. If we don't support a certain data type, we automatically change that type to the closest supported type or, for some types, don't load that data at all. Our system automatically skips columns with data types that we don't accept or transform.

The following table illustrates how we transform your SQL Server data types into Fivetran supported types:

If we are missing an important type that you need, reach out to support.

In some cases, when loading data into your destination, we may need to convert Fivetran data types into data types that are supported by the destination. For more information, see the individual data destination pages.

Excluding source data

If you don’t want to sync all the data from your database, you can exclude schemas, tables, or columns from your syncs on your Fivetran dashboard. To do so, go to your connector details page and uncheck the objects you would like to omit from syncing. For more information, see our Data Blocking documentation.

Alternatively, you can change the permissions of the Fivetran user you created and restrict its access to certain tables or columns.

How to allow only a subset of tables

In your primary database, you can grant SELECT permissions to the Fivetran user on all tables in a given schema:

GRANT SELECT on SCHEMA::<schema> to fivetran;

or only grant SELECT permissions for a specific table:

GRANT SELECT ON [<schema>].[<table>] TO fivetran;

How to allow only a subset of columns

You can restrict the column access of your database's Fivetran user in two ways:

1. Grant SELECT permissions only on certain columns:

   GRANT SELECT ON [<schema>].[<table>] ([<column 1>], [<column 2>], ...) TO fivetran;
   

2. Deny SELECT permissions only on certain columns:

   GRANT SELECT ON [<schema>].[<table>] TO fivetran;
   DENY SELECT ON [<schema>].[<table>] ([<column X>], [<column Y>], ...) TO fivetran;
   

Initial Sync

Once connected to your database, the Fivetran connector copies all rows from every table in every schema for which a Fivetran user has SELECT permissions (except for those you have excluded in your Fivetran dashboard) and sends them to your destination. Additionally, we add Fivetran-generated columns to every table in your destination offering visibility into the state of your data during the syncs.

Updating data

Fivetran performs incremental updates by extracting new or modified data from your source database's transaction log files using one of the following proprietary capture methods:

• Direct Capture: This method captures changes directly from SQL Server's online transaction logs.
• Archive Log Only: This method captures changes from SQL Server's transaction log backups. We do not read anything directly from the online transaction logs, therefore, High-Volume Agent can reside on a separate machine from the SQL Server DBMS.

NOTE: The Archive Log Only capture method generally exhibits higher latency than the Direct Capture method because changes can only be captured when the transaction log backup file is created. While this capture method enables high-performance log-based Change Data Capture (CDC) with minimal operating system and database privileges, it comes at the cost of higher capture latency.

Tables with a primary key

We merge changes to tables with primary keys into the corresponding tables in your destination:

• An INSERT in the source table generates a new row in the destination with _fivetran_deleted = FALSE
• A DELETE in the source table updates the corresponding row in the destination with _fivetran_deleted = TRUE
• An UPDATE in the source table updates the corresponding row in the destination

Tables without a primary key

We handle changes to tables without a primary key differently:

• An INSERT in the source table generates a new row in the destination with _fivetran_deleted = FALSE.
• The _fivetran_id column helps us handle DELETE operations:
  • If there is a row in the destination that has a corresponding _fivetran_id value, that row is updated with _fivetran_deleted = TRUE.
  • If there is not a row in the destination that has a corresponding _fivetran_id value, a new row is added with _fivetran_deleted = TRUE.
• An UPDATE in the source table is treated as a DELETE followed by an INSERT, so it results in two rows in the destination:
  • A row containing the old values with _fivetran_deleted = TRUE
  • A row containing the new values with _fivetran_deleted = FALSE

As a result, one record in your source database may have several corresponding rows in your destination. For example, suppose you have a products table in your source database with no primary key:

You load this table into your destination during your initial sync, creating this destination table:

You then update a row:

UPDATE products SET quantity = 4 WHERE description = 'Cookie robot';

After your UPDATE operation, your destination table will look like this:

You then delete a row:

DELETE FROM products WHERE description = 'Boogie robot';

After your DELETE operation, your destination table will look like this:

So, while there may be just one record in your source database where description = Cookie robot, there are two in your destination - an old version where _fivetran_deleted = TRUE, and a new version where _fivetran_deleted = FALSE.

We also de-duplicate rows before we load them into your destination. We use the _fivetran_id field, which is the hash of the non-Fivetran values in every row, to avoid creating multiple rows with identical contents. If, for example, you have the following table in your source:

Then your destination table will look like this:

Deleted rows

We do not delete rows from the destination, though the way we process deletes differs for tables with primary keys and tables without a primary key.

Deleted columns

We do not delete columns from your destination. When a column is deleted from the source table, we replace the existing values in the corresponding destination column with NULL values.

Table truncation

We don't support table truncation. The SQL Server source database forbids truncation on any table tracked by Change Data Capture (CDC).

To truncate a table, you must disable CDC, which also disables the logging that records the truncate event. As there is no history of the truncation in any logs that we can use, we can’t replicate the table truncation operation.

Migrating service providers

If you want to migrate service providers, you need to do a full re-sync of your data because the new service provider won't retain the same change tracking data as your original SQL Server database.