Using AWS Schema Conversion Tool in an offline mode | Amazon Web Services
Using AWS Schema Conversion Tool in an offline mode | Amazon Web Services
This video shows how to use AWS Schema Conversion Tool (AWS SCT) in an offline mode. AWS SCT converts your on-premises database schema and code objects to a format compatible with a target cloud database. When disconnected from your source database, AWS SCT performs such operations as migration assessment and database schema conversion.
For more information, see the AWS SCT user guide at:
https://docs.aws.amazon.com/SchemaCon…
Subscribe:
More AWS videos - http://bit.ly/2O3zS75
More AWS events videos - http://bit.ly/316g9t4
ABOUT AWS
Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud platform, offering over 200 fully featured services from data centers globally. Millions of customers — including the fastest-growing startups, largest enterprises, and leading government agencies — are using AWS to lower costs, become more agile, and innovate faster.
#AWS #AmazonWebServices #CloudComputing
Content
0.248 -> (upbeat music)
5.76 -> - [Narrator] Our customers
use AWS Schema Conversion Tool
8.71 -> to migrate their on-premises
databases to the AWS cloud.
12.9 -> AWS SCT automatically converts
the source database schema
16.98 -> and code objects to a format compatible
19.3 -> with the target database.
21.35 -> After you connect to your source database
23.33 -> and load the source
metadata into your project,
25.84 -> you can use AWS SCT in an offline mode.
29.54 -> When disconnected from
your source database,
31.84 -> you can use AWS SCT to perform
the following operations:
36.6 -> add mapping rules,
37.89 -> create database migration
assessment reports,
40.64 -> convert database schemas and code,
43.16 -> edit your source and converted code,
45.14 -> save your source and converted code
46.94 -> as SQL scripts in a text file.
49.32 -> This video shows how to work with AWS SCT
52.55 -> in an offline mode.
57.89 -> We create a new a AWS SCT project,
62.39 -> add a source Oracle database,
and connect to this database.
73.7 -> In the main view, we
choose two database schemas
75.943 -> that we want to migrate to the AWS cloud.
81.5 -> In the source metadata tree,
83.12 -> we choose the Schemas
node and load metadata
85.43 -> for the selected schemas.
87.66 -> Make sure that you select only the schemas
89.61 -> you need to migrate
because loading metadata
91.99 -> for all database schemes may take hours.
95.35 -> Now, we save the AWS SCT project
98.7 -> and disconnect from the source database.
104.572 -> AWS SCT now works in an offline mode.
111.1 -> We add mapping rules,
112.57 -> which define a target database platform
114.81 -> for every source database schema.
117.13 -> For the Chinook schema,
118.43 -> we choose Amazon Aurora
PostgreSQL-Compatible Edition
121.65 -> as a migration target.
123.25 -> For the LargeDB schema,
125.06 -> we choose Amazon Redshift
as a migration target.
133.13 -> We switch to the main view to create
134.86 -> a database migration assessment report.
137.63 -> This assessment report provides a summary
139.72 -> of your schema conversion
tasks and the details
142.08 -> for items that AWS SCT
can't automatically convert.
146.632 -> AWS SCT uses the loaded metadata
149.24 -> of Chinook and LargeDB schemas,
151.38 -> which is stored in the
project to create this report.
155.39 -> You can see that AWS SCT generated
158.09 -> the database migration assessment report
160.21 -> for Chinook and LargeDB schemas.
166.41 -> Now, we convert these two database schemas
169.07 -> to their respective
target database platforms.
173.65 -> The target metadata tree
now includes schemas
176.03 -> with converted code.
184.84 -> We can edit the database code,
186.7 -> which is stored in the project.
188.52 -> For example, you can see an action item
190.96 -> because AWS SCT can't convert
the selected procedure.
195.32 -> It uses the SYSDATE function,
197.21 -> which returns the date
from the database time zone
199.66 -> instead of your time zone.
201.53 -> We change the SYSDATE
function to CURRENT_DATE
203.88 -> and convert this procedure again.
205.97 -> These changes apply to the code,
207.9 -> which is stored in the AWS SCT project.
211.46 -> Your source database
code remains unchanged.
214.56 -> Now AWS SCT converts the procedure
217.34 -> and doesn't show an action item.
223.167 -> AWS SCT converted the
Chinook database schema
226.27 -> to a format compatible with
Amazon Aurora PostgreSQL.
230.09 -> We save the converted code to a text file.
235.05 -> We can review, edit,
and then apply this code
237.78 -> to the Amazon Aurora PostgreSQL database.
241.76 -> This completes the overview
of the AWS SCT offline mode.
246.3 -> Thanks for watching this video.
248.584 -> (upbeat music)
Source: https://www.youtube.com/watch?v=Skw78o88XWs