28 apr

Point-in-Time Recovery


E-invoicing in Germany: How to implement the obligation with SAP Business One

Point-in-Time Recovery (PITR) is the ability of a Database, to reset their state to a freely selectable point in the past – not just to the last full backup point. The goal is to restore a faulty state (accidental deletion, erroneous bulk booking, data corruption) as close as possible before the incident, without losing all the hours or days afterwards.

Context

For SAP Business One, PITR builds on two cornerstones: One Full Backup as a starting point and ongoing Log-backups (Transaction Log in MS SQL, Log Segments in HANA), which have since logged all changes without temporal gaps. During restore, the full backup is applied and the logs are rolled forward up to the desired timestamp (e.g. 15:43:12 on 03.02.2026). The smaller the log backup interval, the more precise the restorable point in time – 15–30 minutes are common in B1 production environments. PITR is typically needed when a mass action corrupts data (e.g. faulty DTW import, incorrect DATEV import, wrong script) and a clean prior state is required. On HANA, the log-mode setting must be Normal stand (not Overwrite), on MS SQL, the recovery model must be set to Full stehen — both are standard in productive B1 installations.

Demarcation

PITR is not identical to a SnapshotSnapshots freeze a state once, PITR allows continuous rollback. It is also not the same as High availability (HA) – HA maintains operations during hardware failures, PITR repairs logical errors. Compared to an hourly full backup, PITR is significantly more efficient because only the logs, not the complete database, need to be continuously backed up. The prerequisite is a disciplined backup strategy – no PITR without backed-up logs.


AI in the company

Why companies are hesitant about AI in ERP

Artificial intelligence in the ERP context raises high expectations, as significant productivity gains, far-reaching automation and more informed decisions are on the cards. Nevertheless ...
Predictive maintenance

Predictive maintenance: how to turn SMEs into smart factories

In today's intelligent world, the ability to solve problems before they even arise is no longer a futuristic scenario, but ...
RPA

RPA in the ERP environment: increasing efficiency through digital process assistants

Many ERP systems run processes on a daily basis that are necessary but do not add value. Employees spend valuable time processing orders ...
Generative AI in ERP

Generative AI in ERP: How LLMs are changing the role of ERP systems

With the advent of generative AI and large language models (LLMs), the role of ERP systems is changing fundamentally. Instead of ...
ERP FUTURE

Preparing the ERP future with APIs and microservices

Many medium-sized companies are still working with ERP monoliths that have grown over the years. The modules of these systems are closely ...
DATA-QUALITY

Data quality & AI : AI can only be as good as your data

Companies today are investing heavily in AI technologies, intelligent automation and modern ERP architectures. Despite this, many modernisation projects fail in the early stages because ...
Wird geladen …