FLOWGRAPH-DRIVEN REPLICATION OPTIMIZING SAP HANA SDI FOR REAL-TIME DATA LAKE INGESTION ON AWS
Authors: Sivasankararao Kavuri
Country: USA
Abstract
SAP HANA Smart Data Integration (SDI) provides the connectivity and transformation layer many enterprises
rely on to move data from on-premises HANA systems into cloud environments. When the target is a real-time,
AWS-hosted data lake, the flowgraph layer, rather than the adapter or network layer, is frequently the dominant
factor determining whether a replication pipeline meets its latency target under production volume. This article
focuses specifically on flowgraph-level optimization techniques and their interaction with AWS target service
selection, sizing, and cost.
The article presents a reference architecture for bridging SDI with AWS-native data lake services, a structured set
of flowgraph optimization techniques, change data capture tuning guidance, a comparison of AWS landing targets,
a benchmarking methodology with illustrative results, sizing and cost models, and governance and monitoring
guidance specific to an AWS-hosted target environment. It is deliberately table-dense, since the underlying
optimization decisions are best communicated as structured comparisons rather than narrative alone.
Across the illustrative benchmarks presented in this article, disciplined flowgraph optimization, informed target
selection, and CDC tuning combined to reduce end-to-end replication latency by 40 to 65 percent relative to an
unoptimized baseline, at broadly comparable infrastructure cost.
Keywords
Not provided
References
1) Akidau, T., Chernyak, S., & Lax, R. (2018). Streaming Systems: The What, Where, When, and How of
Large-Scale Data Processing. O'Reilly Media.
2) Amazon Web Services. (2019). Streaming Data Solutions on AWS with Amazon Kinesis. AWS
Whitepaper.
3) Amazon Web Services. (2020). Amazon Simple Storage Service (S3) User Guide: Best Practices
Design Patterns. AWS Documentation.
4) Amazon Web Services. (2020). Building a Data Lake on AWS. AWS Whitepaper.
5) Amazon Web Services. (2021). Amazon Managed Streaming for Apache Kafka Developer Guide.
AWS Documentation.
6) Gartner, Inc. (2020). How to Build an Effective Data Lake Strategy. Gartner Research.
7) Gartner, Inc. (2020). Market Guide for Data Integration Tools. Gartner Research.
8) Heilman, R., Sanyal, J., Ravi, R., & Yang, J. (2017). SAP HANA Smart Data Integration and Smart
Data Quality. SAP PRESS.
9) IDC. (2020). Worldwide Data Integration and Intelligence Software Market Forecast, 2020 to 2024.
IDC.
10) Kleppmann, M. (2017). Designing Data-Intensive Applications: The Big Ideas Behind Reliable,
Scalable, and Maintainable Systems. O'Reilly Media.
11) Kreps, J., Narkhede, N., & Rao, J. (2011). Kafka: A Distributed Messaging System for Log Processing.
Proceedings of the NetDB Workshop.
12) SAP SE. (2018). SAP HANA Data Provisioning Guide. SAP Help Portal.
13) SAP SE. (2019). SAP HANA Smart Data Integration and Smart Data Quality: Administration Guide.
SAP Help Portal.
14) SAP SE. (2021, February). SAP HANA Smart Data Integration: Performance and Tuning Guide. SAP
Help Portal.