Collect

Extraction Methods for End-to-End Integration Solution

This topic describes data extraction methods used in the end-to-end integration solution. There is appreciable effort required in configuring, testing and tuning data extraction methods to achieve optimal performance. Therefore, there are few hard-and-fast rules as to when a particular method should be used. The selection of a method is often determined on a table-by-table basis and may change over time. This section identifies some of the variables to be considered for improving the overall performance of data extraction and replication, the pros and cons of each extraction method and examples of their usage. 

This content consists of the following sections:

System Considerations

Before data is extracted, there are hardware and software considerations that impact its efficiency.  Many of these are described in the Syniti Solutions Software Requirements and Hardware Sizing Guide. However, it is important to remember these requirements can vary significantly based on the type and scope of a project.  Considerations include:

  • Network hardware and bandwidth (LAN and WAN)
  • Proximity to the data center
  • Number of clients and usage patterns
  • System configuration
  • Database design

Data Considerations

With a system in place, there are additional scope and data-related variables to consider:

  • The type and quantity of data – Are there clustered or pooled tables in scope for extraction?  If so, the options available for extraction are limited. Consider the number of records and the width of tables. For example, with S/4 HANA, SAP has been able to remove several tables. However, in some cases, existing tables have become wider (300+ columns). 
  • Frequency of change – Is the data relatively static (e.g., a system configuration table) or changing rapidly (e.g., open items)?
  • Rate of refresh – How close to real-time does the data need to be to achieve the project’s goals?  For example, are there meaningful benefits to the business if data is refreshed every 8 hours as opposed to every 12 hours?  Data refresh should be scheduled for times when the system is not being heavily used.
  • Security permissions and available connection types – Permissions must be granted to connect to and extract data from other systems. The level of access that can be granted to these systems affects the design of a solution and the methods for data extraction.

Connection Types

The methods for extraction considered in this section rely upon two connection types:

  1. Open Database Protocol (ODBC) – The most common and universally used connection type and an industry standard. ODBC can connect to any database management system for which an ODBC driver is installed or available.
  2. Remote Function Call (RFC) – The protocol used to call functions within SAP and the standard interface for communication between SAP Systems.

Extraction Methods and Tools

The following diagram illustrates how the various extraction/replication methods might fall and interact within a Stewardship Tier solution:

SAP RFC & BOA RFC

RFC allows for the extraction of SAP pooled and clustered tables. The RFC extraction method calls an RFC on the SAP application server to run a SQL command. Data is returned in a Binary Large Object (BLOB) in blocks (e.g., 10,000 records) that are then parsed by using SAP RFC, BOA RFC or BODS RFC. The RFC is called again to extract the next block of records (in this example, the next 10,000 records). The process repeats until the entire table has been extracted. Refer to the Knowledge Base Article "SAP RFC Extraction Overview" for more information.

NOTE: If the WHERE clause for a SAP RFC or a BOA RFC is greater than 72 characters, the WHERE clause is broken down into several lines based on the last white space character before the 72-character limit on each line. If there is no white spaces, a new WHERE clause line after the 72 characters is created. To troubleshoot, enable debug logging for Collect to see how the WHERE clause was split into several lines.

NOTE: When extracting data for package types SAP RFC and BOA RFC, any trailing, empty spaces in columns are removed by default. This option is set on the Vertical View of the Tables page.

Assemble

Assemble is a tool within the Stewardship Tier that creates and executes packages to transfer data between systems. The tool uses an ODBC connection with a non individual-specific account with read-only access. Once a connection is established to a source, packages to refresh data are relatively easy to create and process in the Stewardship Tier.  Assemble runs multiple threads and is the Syniti preferred extraction method for all but the largest tables.  Additional configuration and tuning can be required for the largest tables that are over several million records (refer to the Stewardship Tier Collect Delta Configuration image).

SSIS (SQL Server Integration Services)

SSIS is typically faster than Assemble because a single program is both reading the source and loading the target. Depending on variables like hardware, connections and table width, SISS can achieve extraction speeds of several million records per minute. However, using SSIS to extract too many tables simultaneously can require additional CPUs and memory on the application server. Consequently, SSIS should be used judiciously to extract tables that cannot be efficiently extracted with other methods. The same delta configuration that can be implemented for Assemble can also be for SSIS when needed (refer to the Stewardship Tier Collect Delta Configuration diagram).

Syniti Data Replication

Syniti Data Replication functionality (which is housed in Collect) that uses an ODBC connection to download data in any of three different ways: Refresh, Change Data Capture (or Mirroring) and Synchronization.

In Refresh mode, an entire table is extracted. A one-time full-table refresh is required for any tables that will be set up for change data capture or synchronization. Change data capture mirrors data changes made in a source system to a target system. Synchronization pushes data changes both directions between two systems. 

When performing change data capture or synchronization, Syniti Data Replication leverages native change logs within the source to identify the subset of records that have been changed and need to be updated in the target system. If the source system does not contain native or accessible change logs, then Syniti Data Replication uses triggers on the source system to push changes.

Choose the Right Extraction Method

To aid in selecting the right extraction methods for the project, refer to the following table:

Connection Type

Extraction Method

Pros

Cons

Examples

RFC
  • BOA RFC
  • SAP RFC
  • BODS RFC
  • Ability to extract pooled tables, clustered tables and long text
  • Standard SAP protocol
  • Refresh can be scheduled within Collect
  • Slower than ODBC
  • Single thread to limit the number of jobs running at one time

Material Long Text

ODBC

Assemble
  • Multi-threaded for fast extraction of all but the largest tables (Syniti preferred method)
  • Not tied to a specific version of SQL Database Management System (DBMS)
  • Supports fixed and delimited widths, and Excel.
  • Can configure batch size, select execution by field (e.g., change dates) and the number of parallel threads
  • Query can be optimized to be executed on source database
  • Very quick to initially build in the Stewardship Tier
  • Refresh can be scheduled within Collect
  • For use only with transparent tables
  • Diminished performance when pulling very large record sets (> several million)
  • Material Master
  • Customer
  • Product Hierarchy
  • Check & Configuration tables

 

SSIS

  • Improved performance over Assemble for many system types. Can extract millions of records per minute, depending on hardware, software, table width, etc.
  • Filters can be applied to tables that maintain create and update dates in order to limit the data extracted. In this case, the logic and overhead to perform the delta on the full table copy is done in the Stewardship Tier.
  • Refresh can be scheduled in Collect
  • For use only with transparent tables
  • Running many table extractions simultaneously with SSIS can require additional CPUs and memory
  • Requires additional configuration in Stewardship Tier to set up as a delta update

Largest master data or transactional tables where Assemble extraction is not sufficient. Historical examples include:

  • Open Items
  • Bill History

 

Syniti Data Replication(Collect)
  • Can extract data through Refresh, Change Data Capturing (mirroring) or Synchronization modes
  • Change data capture and synchronization leverage a source system’s native change logs or use triggers to identify the subset of data that has changed, thereby reducing the amount of data being extracted
  • Small footprint
  • Highly configurable through GUI

If trigger-driven (no native change logs), changes to the source system are required (triggers and trigger tables for the tables that are going to be replicated with Syniti Data Replication)

Largest master data or transactional tables where Assemble extraction is not sufficient. Historical examples include:

  • Open Items
  • Bill History

Collect Delta Configuration

For large tables with requisite date fields, a delta extraction process can be built in within Stewardship Tier, as conceptualized in the following diagram:

Improve Data Extraction Efficiency

Use the following tips to improve efficiency when using one of the outlined extraction methods:

  • Dedicated hardware – The fewer applications and users sharing the same hardware, the better and more consistent the performance.
  • High speed connectivity – Bandwidth is often a bottleneck.
  • Extract only relevant tables – Clearly define the tables that are in scope for extraction.
  • Extract only relevant records/and or columns from tables – Excluding fields that are not being used effectively makes a narrower table. In some cases, the client may request that certain sensitive fields be excluded from extraction altogether. Adding relevancy (for example, pulling only active customers) also reduces the amount of data being extracted.
  • Create a development environment with comparable performance to production environment – The testing and configuration process is critical. Testing in an environment that closely mimics that of development provides a much more accurate picture of performance and behavior.
  • Baseline and monitor extraction performance – Variables that impact efficiency change over time and it is important to monitor performance. Such variables include record volumes, changes in hardware or software, changes in business processes or protocols, and load on the network.

Decision Tree

Click the following decision tree thumbnail to determine which extraction method is best to use based on the type of data being extracted: