The life cycle of Data Migration

We have discussed the importance for data migration and its essentials. The data migration process demands a well-planned strategy and its execution approach. Therefore, we will be talking about the life cycle of data migration- on-premise to the cloud (migration) or cloud to another cloud.

Data migration is not just a process of transferring data from source A to destination B. It involves complex processes and demands for a step-by-step execution of the migration process. If the data migration process is carried out without authorization and understanding of the data migration needs, there is a high risk of data losses and other impacts on the business processes such as revenue loss, facing downtime, time and resources involved in data correction, impact on the public relations and more.

To overcome the inherent risk in the data migration process it is important to understand the data migration life cycle.

“Analysis of data migration projects over the years has shown that they meet with mixed results. While mission-critical to the success of the business initiatives they are meant to facilitate, lack of planning structure and attention to risks causes many data migration efforts to fail.” (Gartner, “Risks and Challenges in Data Migrations and Conversions,” February 2009, ID Number: G00165710)

The following information describes the data migration strategy from on-premise to cloud platforms.

Data Migration life cycle

Choose application -> Migration assessment -> Schema and code conversion -> Data conversion and synchronization -> Testing -> Training -> Production Go-live -> Health Check

Choose Data Migration Application

If it is the first data migration process, then start slow, with fewer challenges and risks of migration. Select the application or set of databases that you want to move to the destination database center. You can hire an experienced database administrator or a third-party data migration service provider like Datametica. Look for the application or a tool, which can help you to convert the source code for easy modification in a new database environment.

Datametica Automated Workload (SQL/ETL/Script) product Raven can help with this requirement as it supports multiple databases. The capability of establishing Open database connectivity (ODBC) and Java database connectivity (JDBC) connection assist with simple and compatible migration.

Data Warehouse Assessment

During the migration assessment, one must consider the factors such as technology process and personnel and tool requirements.

The first step of the migration process is to analyze the enterprise data warehouse or source systems and application. This will evaluate the challenges involved for the source to target database migration, various data complexities, etc. Following is the checklist for the Data warehouse assessment or data migration assessment:

1. Understanding both Data Source and Target
2. Detailed discovery of data
3. Defining mapping specifications
4. Workload pattern analysis
5. Identifying underlying dependencies & complexities
6. Budgeting and deadline
7. Defining Migration Strategy
8. Utilizing Human Resource
9. Selecting the right tools and technologies

You can utilize the automated product of Datametica called Eagle for the data warehouse assessment. Eagle — the planner is designed by Datametica for the assessment, planning and optimization of data warehouses.

Schema and Code Migration

The schema or code migration process is pretty straightforward, in which SQL syntax and data types are converted from source to destination syntax and data types.

Datametica’s Automated Workload/Code Conversion Product — Raven easily converts tables, SQL, ETL, triggers, procedures, permissions, sequences and database objects from source to destination.

For the successful schema conversion, after the automated product procedure, the DBA or developer must review the complete conversion for establishing a fully working destination environment.

Data Migration and Sync

Once you have successfully converted the schema, the next process is to migrate the data from source to destination database. It is always advisable to perform migration of subset or testing dataset first in development and then at staging environments. Post assurance migrate the data in full production or destination environment

The approaches for data migration and synchronization depends on the following factors:

1. Size of the database
2. Rate of data change
3. Permitted downtime

In the end, verify the data if it is correctly synchronized without losing data integrity at the destination.

Testing & Database Validation

Post data migration, the entire database has to be validated. It is mandatory to perform the following testing:

1. Business logic testing
2. Functional testing
3. Performance testing
4. Load testing

Along with these testing, execute backup and restore procedure testing, validate configuration, deployment processes and operational activities.

In order to automate the data validation process, you can use Datametica’s tool called Pelican — the validator. It is designed for automating the data validation, reconciliation and comparison of datasets across two heterogeneous data stores till cell level.

Training

The stakeholders, especially the developers, DBAs, and the operation team who are responsible for the data migration process must have the training to work during all phases of migration.

Production Go-live

Before the production goes live, perform activities on the migrated databases such as final data synchronization, application configuration, updating DNS entries and accesses, etc. It is always recommended to break up data migration activities into a logical and functional manner before moving on to the production go-live.

Support and Health Check

If you can’t take risk of downtime or data loss, schedule the database health check at regular intervals of time. It not only helps to address the underlying problems but also gives time to proactively prevent or deal with them.

Summary

Data migration to the cloud can help customers leverage their data for achieving new insights and business outcomes while saving cost and improving system performance. Thus it is necessary to execute the process properly and in a timely manner. We at Datametica ensure that our customers have a seamless and risk free cloud migration journey.

About Datametica

Datametica is a global leader in data warehouse migration and modernization to the cloud. We empower businesses by migrating their Data/Workload/ETL/Analytics to the Cloud by leveraging Automation. Through their automated products: Eagle — Data warehouse Assessment & migration Planning Product, Raven — Automated Workload Conversion product and Pelican — Automated Data Validation Tool, Datametica automates and accelerates data migration to the cloud enabling us to remove anxiety from the migration process, making it Faster, with Greater Accuracy, Lesser Risk, and at more Competitive Cost.

We expertise in transforming legacy Teradata, Oracle, Hadoop, Netezza, Vertica, Greenplum along with ETLs like Informatica, DataStage, Ab Initio & others, to cloud-based data warehousing with other capabilities in data engineering, advanced analytics solutions, data management, data lake implementation and cloud optimization.