Limitations

Documentation

1. Tables with Primary Keys (PKs) of type TimeStamp and tables of type FileTable are seeded but not tracked for changes real-time. For those tables the work around is to add those tables to a schedule to reseed tables (a seamless automatic process, but not a real-time solution).

2. Real-Time replication of tables without Primary Key but with Unique Indexes is supported but need to select CDC (Change Data Capture) under [Advanced] options in UI during initial job configuration (specify CDC in API call parameters if creating new jobs over API).
3. Replication of tables without Primary Keys (PKs) and without Unique Indexes is supported but need to select CDC (Change Data Capture) under [Advanced] options in UI during initial job configuration (specify CDC in API call parameters if creating new jobs over API).
To lower latency, adding of basic LongInt Identity field Primary Key or adding of Unique Index is recommended.

Note: CDC (Change Data Capture) is not supported by SQL Server Web Edition and low end AzureSQL DB classes (below Standard S4).

4. Table truncate when the replication job is configured with CDC (Change Data Capture) is supported, but integration with CloudBasix API is required. Contact Support for more information.

5. Table partition truncate and partition switch is supported, but integration with CloudBasix API is required. Contact Support for more information.

6. Windows/Active Directory logins (user accounts) are not replicated.

7. MSSQL Server level logins/users (along with roles) are replicated, however for security reasons random passwords are assigned. Passwords need to be manually reset on the replica SQL Server after the initial database replication seeding completes. Password changes are also not automatically replicated as part of the schema replication process.

Justification: CloudBasix is designed to be lightweight and to handle MSSQL Server continuous replication over TCP/IP and to work in cross-region (over VPN or data encrypted in transit SSL connections), cross-aws-accounts (data/DR artifacts are stored in multiple AWS accounts, account level security breach would not affect business continuity), on-premise to AWS RDS or EC2 MSSQL Server, and even InterCloud replication scenarios.   

8. By design, for security reasons (default use case is DR), complex cases of multiple dropped or renamed table columns might not be automatically replicated in certain rare complex cases  (user approval is required if the case is determined to be ambiguous) as part of the continuous schema replication process. Complex DB schema changes are usually part of planned releases, and the semi-automatic handling of those rare ambiguous cases of schema change tracking is handled as part of the planned release rolls out.  In case of complex cases of multiple dropped or renamed column(s) the following ambiguous schema change detected error notification will be generated (and reported in runtime logs for each change tracking process run):

If follow the link in the emailed notification, will open respective error logs screen, 
where a button [Apply Schema Changes] ([Guide Me] in 12.82 and earlier versions) will be enabled:

The following will be reported in runtime logs:

>>ERROR:Ambiguous schema change detected. A column(s) been renamed or dropped in table(s): [dbo].[Managers].CompanyName2, [dbo].[Managers].CompanyName3 ? To resume change tracking, apply proper schema change(s): (1) Click [Apply Schema Changes] button and follow the instructions (note: if you do not see such a button then changes had already been applied) or (2) Execute below scripts, properly adjusted to account for actual schema changes, manually: If column1 was dropped, then you need to execute: alter table tablename drop column column1; (Applicable to redshift replications only): On the Redshift side, you need to execute 2 statements: (1) alter table tablename drop column column1 (2) alter table tablename_stage drop column column1 If column1 was renamed to column2, then you need to execute: EXEC sp_rename N'[tablename].[column1]',N'column2', 'OBJECT'; (Applicable to redshift replications only): On the Redshift side, you need to execute 2 statements: (1) alter table tablename rename column column1 to column2 (2) alter table tablename_stage rename column column1 to column2

When you click the [Apply Schema Changes] button ([Guide Me] in 12.82 and earlier versions), you will land on an interactive wizard which will allow you to drop and/or rename columns without the need to manually execute alter queries against the staging data store and, if applicable to the replication type,  against redshift:

DROP COLUMNS:

1.1 Selected columns to be dropped:

RENAME COLUMNS:

2.1 Map columns to be renamed (1 pair at a time - if more than 1 columns are renamed):