Data migration is the way of moving data from legacy systems to a new system, or from one vendor’s software to another’s, or from on-premises to the cloud.
In this data-driven business world, the companies need to upgrade to technology advancements such as cloud, big data, and analytics, new compliance requirements, add-ons like HR package update, new features implementations like CRM, etc., to keep up to date as frontrunner over the competition. All these upgrades and updates lead to data migration.
Migrating data can be very distressing and risky due to:-
- Data Loss
- Poor data quality
- Data Mismatch
- Brand Impact
- Time consuming & expensive cost
What is Data Migration Testing?
The way of comparing source data with target data to find out any data issues or discrepancies is called Data Migration Testing. It does so with limited downtime and no loss of data or issues with data integrity.
Challenges of Data Migration Testing
Data Migration is considered as one of the most challenging processes within an organisation. Although these projects yield high business benefits and return on investments, they tend to involve a high level of risk due to the sheer volume and criticality of the data.
- Complex data mapping between source system and target system
- Multiple data platform / Data source/ Data sets extraction
- Huge Data size by volume
- Multiple mock testing before final migration with in limited time frame
So many questions pop up in stakeholder’s mind to assure successful data migration i.e .
- What is a database migration strategy ?
- How to implement an effective data migration testing strategy ?
- Is it necessary to perform pre-migration testing ?
- Will testing cover target system data platform and source data platform data extraction ?
- How to migrate database data without or minimum downtime ?
- How to switch from manual testing to automation ?
- How will we manage data mapping between legacy and new system ?
To mitigate risk and ensure that the data has been migrated and transformed, implementation of validation and testing strategy becomes imperative.
LDU helps you test your data quickly and easily through automated test solution:-
- T2T Test: Table-to-table test compares the source and the target data in all of the tables, row by row and column by column even if thesource and target table names and data typesare different. It fully assures the success of data migration.
- Row Counts Test: verifying row counts, validating hundreds of tables in minutes.
- C2C Test: Performing column-to-column compares with all or selected columns.
- Testing custom transformation: It can be combination of all tests, client’s business logic and data transformation, which automated as per the client’s requirement.
We offer upgraded technologies to resolve data migration issuesand worries. To get better data insights, we provide testing solution in a very systematic way:-
We can extract data from multiple-data platforms in less time. We work on image base data extraction so that data cannot be modified in any way. There is no limit on data size extraction
Plug & Play solution:
We offer plug & play solution for data migration testing except for custom transformation test. Which is time saving and makesit reusable for multiple mock tests and final migration.
Scheduling & Auto-email:
we can schedule data migration testing at any time of the day and on completion,the system automatically sends an email to stakeholders.
We provide instant detailed output report at the end of the activity via auto-email or manually to address the migration issuesif anyso, that the stakeholders can respond effectively.
Supported data technologies
Our solutions support below data platform for either legacy/Source or new/target system
- Amazon Redshift, DynamoDB, Simple Storage Service (S3), Athena
- Confluent KSQL
- Databricks in Azure
- Google BigQuery
- HP Tandem
- Pivotal GreenPlum
- Teradata, Aster
- Apache Hadoop/Hive/Spark/Kafka
- Flat Files (delimited and fixed-width)
- Oracle (Oracle db, MySQL, Exadata)
- IBM (DashDB, BigInsights, DB2, Netezza, Informix, Cloudant, Cognos Analytics)
- Microsoft (Azure Synapse Analytics, SQL Server, PDW, SSAS, Access, Excel)
- SAP (HANA, IQ, ASE, SQL Anywhere, Business Objects)
- Azure Analysis Services, Data Lake Storage, Blob Storage, SQL Data Warehouse, SQL Database