Dextrus is a purpose-built cloud powered self-service solution, for all data practitioners. Dextrus is a no-code high performance solution that is complete and comprehensive and helps in building, deploying, and managing assets. For data ingestion, streaming, cleansing, transforming, analyzing, wrangling and machine learning modeling.

RightData tool is a no-code data testing, reconciliation and validation suite. We empower data testing, data governance and data steward teams with data quality assurance and quality control audit automation capabilities.

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Dextrus is a purpose-built cloud powered Self-service, Data integration and Data science solution, for all data practitioners.

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RightData product is a no-code data testing, reconciliation and validation suite.

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Solutions

Dextrus

Solutions

Data Migration

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Dextrus

Dextrus Solution

Data Migration

The first step in re-platforming the databases as part of data engineering initiative is Data Migration. This one-time migration of data from legacy platforms to modern cloud databases like AWS Redshift, Google Big Query, various Azure databases and Snowflake cloud database is achieved by using several embedded Dextrus connectors. Dextrus can migrate data at the individual table, schema, or database level by simple configuration steps. There is no need of executing DDL scripts on the target databases for creating target table schemas before migrating the data.

Be it a database version an upgrade effort, an on-premise to cloud database migration, or a business process migration due to optimization, reorganization, or M&A, the following are key points for a successful migration approach.

Ability to connect to all the data points
Ability to get insight on the data
Ability to profile the data
Ability to pre-prepare the data for the successful and efficient data migration
Ability to validate and reconcile the data between source and target databases after the migration is complete.

Big Bang Data Migration Approach

This approach is quicker, less complex, and economical. Depending on the data volumes to be migrated, this approach can be chosen. All the data is moved from legacy environments to modern data platforms in one single operation. The systems won’t be available and users are not expected to use the system during this approach until the migration is completed. This approach is more suitable for companies with smaller data volumes. The caveat with this approach is it can not be adopted for organizations where the downtime of the systems is not an option.

Phased Data Migration Approach

This approach splits the migration process into sub-processes based on the functional areas so that data is migrated in a phased manner. The advantage is old systems can remain functional and operational while the migration happens in parallel. No downtime is necessary. Though this process is more complex, it best suits organizations with huge data volumes where system downtime is not an option. While both legacy and modern data platforms are running in parallel, quality control processes can make sure the data is successfully migrated before certain functional area is turned off in the legacy platform.

Dextrus as an off-the-shelf data integration tool, while having all these capabilities, also can cleanse the data to improve the data quality before the migration of data happens. In fact, this is a good opportunity for data engineers to address the old problems with the data quality before the migration, so that the new platform will have quality data.

Another important aspect of data migration initiative is the “type of approach.” It can be Big Bang Data Migration Approach or Phased Data Migration Approach. Though the process remains the same, adopting a better approach depends upon some factors.

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