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Data Connector Description:

Data Connector Type: Event

Data Connector Documentation:

[SAP S/4 , PRIVATE PREVIEW, SAP S/4, is an enterprise resource planning system that is built for businesses of all sizes ‚Äì small, midsize, and large. SAP S/4 has modules specific to industries ‚Äì from manufacturing to retail. The software supports and integrates almost every functional area of a business process from procurement of goods and services, sale and distribution, finance, accounting, human resource, manufacturing, production planning to logistics and warehouse management., Features, Feature Name, Supported, Notes, Capture Deletes, check, All tables, Custom Data, check, All tables and fields, Data Blocking, check, Column level, table level, and schema level, Column Hashing, check, Re-sync, check, Table level, History, check, API Configurable, check, Priority-first sync, dbt Package, Setup Guide, Follow our step-by-step , SAP S/4 setup guide, to connect SAP S/4 with your destination using Fivetran connectors., Sync overview, You can use this connector to sync data from your SAP S/4 ERP and SAP HANA database., After you grant us access, Fivetran connects to your SAP HANA database. , We check your source tables for primary key columns. If a source table has primary key(s), we create a one-time shadow table and four types of triggers for the source table in your , FIVETRAN_DB, schema. We use the shadow table to store changes of the source table. We incrementally update these source tables in the destination. We also capture the deleted records for incremental tables in each sync using the shadow tables., We create the shadow table using all the primary keys of the source table and two extra columns. The two extra columns are:, optype, – The column represents the Data Manipulation Language (DML) operation on the record. We use the following three values for the different types of operations:, 1 – DELETE , 2 – UPDATE , 3 – INSERT , updatedat, – A current timestamp column to track the last updated datetime of a record., We name the shadow table using the format HADP_SHADOW_SCHEMANAME_SELECTED_TABLE_FIVETRAN, . For example, if your table name is , T503K, and your schema name is , SAPHANADB then the name of the shadow table will be , HADP_SHADOW_SAPHANADB_T503K_FIVETRAN, ., We create four types of triggers on the table to track the three DML operations, based on the event type , BEFORE, or , AFTER, . We name the triggers using the format HADP_SCHEMANAME_SELECTED_TABLE_TYPE_OF_DML_OPERATION_EVENT_TYPE, . For example, if your table name is , T503K, and your schema name is , SAPHANADB then the name of the four triggers will be: , HADP_SHADOW_SAPHANADB_T503K_INSERT_AFTER, HADP_SHADOW_SAPHANADB_T503K_UPDATE_BEFORE, HADP_SHADOW_SAPHANADB_T503K_UPDATE_AFTER, HADP_SHADOW_SAPHANADB_T503K_DELETE_BEFORE, We periodically flush the last seven days data from the shadow tables. We pull a full dump of all existing data from your SAP HANA database. Using a change data capture process, we pull incremental updates at regular intervals., Once you deselect a source table from the connector dashboard, we remove the shadow table and the corresponding triggers from your database in the next sync., “If a source table doesnt have any primary key column, we mark the table as “, Reimported, and we completely import the whole table during each sync. We automatically capture the deletes for the re-imported tables based on their sync time. , If data in the source changes (for example, you add new columns, new tables, or change a data type), Fivetran automatically detects and persists these changes into your destination. For every schema within the SAP HANA databases that you connect, we create a different schema in your destination that maps directly to its native schema, so that the data is in a familiar format to work with., Schema information, Fivetran tries to replicate the exact schema and tables from your source SAP HANA to your destination. You can include tables from the S/4 ERP system and tables that reside in your HANA database., We retrieve all the available tables from your database except the database tables with the following delivery classes: , Delivery class L: Table for storing temporary data, Delivery class E: System table with its own namespace for customer entries, Delivery class S: System table, data changes have the status of program changes, Delivery class W: System table, whose data is transported with its own transport objects, We do not retrieve tables if the table names contain the slash (/) character. , The following ERDs illustrate how important tables in different modules relate to one another., Schema for the Sales & Distribution Module:n, To zoom, open ERD in new window., Schema for the Financial Management Module:n, To zoom, open ERD in new window., Schema for the Material Management Module:n, To zoom, open ERD in new window., Schema for the Quality Management Module:n, To zoom, open ERD in new window., Schema for the Human Resource Management Module:n, To zoom, open ERD in new window., Schema for the Production Planning Module:n, To zoom, open ERD in new window., Schema for the Plant Maintenance/Project System Module:n, To zoom, open ERD in new window., Fivetran generated columns, Fivetran adds one extra column to every table that is added to your destination to give you insight into the progress and changes of your connector: , Column, Type, Description, _fivetran_synced, INSTANT, (UTC timestamp) to keep track of when the row was synced by Fivetran, Fivetran also adds one extra column to every table that does not have a primary key column to keep records unique:, Column, Type, Description, _fivetran_id, STRING, ID to keep records unique when there is no primary key, Type transformation and mapping, “As we extract your data, we always do our best to match SAP HANA data types to types supported by Fivetran. In some cases, we dont accept source types, so we automatically change it to the closest type possible or sometimes dont load that data at all. Our system automatically skips columns of data types that we dont accept or transform, such as BLOB. “, The following table shows how your SAP HANA data types get transformed to a destination type:, SAP HANA Type, Destination Type, Fivetran Supported, DATE, LOCALDATE, Yes, TIME, STRING, Yes, SECONDDATE, INSTANT, Yes, TIMESTAMP, INSTANT, Yes, TINYINT, SHORT, Yes, SMALLINT, SHORT, Yes, INTEGER, INTEGER, Yes, BIGINT, LONG, Yes, SMALLDECIMAL, BIGDECIMAL, Yes, DECIMAL, BIGDECIMAL, Yes, REAL, FLOAT, Yes, DOUBLE, DOUBLE, Yes, BOOLEAN, BOOLEAN, Yes, VARCHAR, STRING, Yes, NVARCHAR, STRING, Yes, ALPHANUM, STRING, Yes, SHORTTEXT, STRING, Yes, VARBINARY, BINARY, Yes, BLOB, No, CLOB, No, NCLOB, No, TEXT, STRING, Yes, ARRAY, STRING, Yes, ST_GEOMETRY, No, ST_GEOMETRY, No, Fivetran data types not supported by your destination may also get converted when we load the data into your destination. For more information about how the data is transformed from an internal Fivetran type to a type supported by your destination, visit the individual destination pages. , Contact , support, if you need a data type that we are missing., Excluding source data, On your Fivetran dashboard, you can exclude tables and columns from syncing. To do so, go to the , Schema, tab on the connector details page and uncheck the columns or tables you would like to omit from syncing., Initial sync, When a new source is connected, we first create the shadow tables and triggers for all the source tables on you database. , Then, we copy every row from every table in the schema that you have connected (except for the ones that you have excluded through the dashboard) and add Fivetran generated columns. Rows are copied by performing a , SELECT, statement on each table. , Updating data, Once the initial sync is complete, we start performing incremental updates at regular intervals using one of the following ways:, Incremental: Tables that have a corresponding shadow table are incrementally updated. We save the sync state, so during the next sync, we start syncing from the point where we left.n, “Re-import: Tables that dont have a corresponding shadow table are re-imported. We re-import tables using the following conditions:”, Table Import Duration, Import Frequency, 3 seconds or less, Every sync, Between 3 seconds and 5 minutes, Twice a day, More than 5 minutes, Once a week, Deleting data, We track deleted records for all tables., Tables have a , _fivetran_deleted, column in the destination. After each re-import, any row that no longer exists in the source table is marked as deleted in the destination., n]

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