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If the data is being persisted in a modern database then Change Data Capture is a simple matter of permissions. Two techniques are in common use: Tracking changes using database triggers; Reading the transaction log as, or shortly after, it is written. If the data is not in a modern database, CDC becomes a programming challenge.
Migration addresses the possible obsolescence of the data carrier, but does not address that certain technologies that use the data may be abandoned altogether, leaving migration useless. Time-consuming – migration is a continual process, which must be repeated every time a medium reaches obsolescence, for all data objects stored on a certain ...
Data reconciliation is a technique that targets at correcting measurement errors that are due to measurement noise, i.e. random errors.From a statistical point of view the main assumption is that no systematic errors exist in the set of measurements, since they may bias the reconciliation results and reduce the robustness of the reconciliation.
Schema migration allows for fixing mistakes and adapting the data as requirements change. They are an essential part of software evolution, especially in agile environments (see below). Applying a schema migration to a production database is always a risk. Development and test databases tend to be smaller and cleaner.
Migrations can be written in SQL (database-specific syntax such as PL/SQL, T-SQL, etc is supported) or Java (for advanced data transformations or dealing with LOBs). It has a command-line client, a Java API (also works on Android ) for migrating the database on application startup, a Maven plugin, and a Gradle plugin.
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SQL refers to Structured Query Language, a kind of language used to access, update and manipulate database. In SQL, ROLLBACK is a command that causes all data changes since the last START TRANSACTION or BEGIN to be discarded by the relational database management systems (RDBMS), so that the state of the data is "rolled back" to the way it was before those changes were made.
The following steps can structure migration planning: [60] Identify the data to be migrated. Determine the migration timing. Generate data migration templates for key data components; Freeze the toolset. Decide on the migration-related setup of key business accounts. Define data archiving policies and procedures.