The Introduction

By:Yogeshar C May 23/2019

Organisations are hesitant to migrate their data warehouse to the cloud because of various reasons, including time taken for migration, anticipated security hazards and absence of experienced human resources to empower these relocations. Most of the organisations determine relocation cost as one of the primary obstructions for Cloud Migration. Absence of Knowledge of the new Platform, absence of relocation governance, absence of insights into execution and performance on the new platformin line with the expectations are some of the contributing aspects for the hesitation.

While addressing the above-mentioned worries, a data migration solution must be swift and simple. The speed of delivery in the present day resembles driving on the Formula One Track. There is no speed limit, yet you must guarantee to drive safely and effectively. Speed and sharpness are very much instrumental with reduced time to market, high level of automation, negligible human intervention, high data accuracy, top-notch state of security and improved Total Cost of Ownership.

The key stepswhich are needed for Data migration are as follows:

• • Source Analyzer: The first important step to any migration is to understand the data source model. It needs to be reviewed for accuracy, enable users to define rules and generate reports. It should also help generate data model including table names, column names, indexes, datatypes etc., The extraction SQL should have flexibility to modification.
• • Performance Optimizer: The data movement must be optimized for performance. This can be achieved by splitting the data files into multi-parts and engaging all the available nodes in the target database. Data files need to be compressed for faster data movement onto the cloud landing zone.
• • Data Migration: The data loading should leverage all available resources for maximum similarity.
• • Post Load Validation:Once the data loading is finally completed, the data between the source and target at individual table level must be validated for accuracy. It can be achieved either by comparing row counts or checksums for each table in source and target.
• • Governance Set Up: The migration tool should have end to end governance.

An end to end self-service platform-agnostic and cloud-agonistic solution that can accelerate migration of data-warehouse from on premise to cloud is presently required.
The comprehensive solution with above mentioned steps will be able to provide the following for users:

• • Helps in Analysingthe existing data warehouse prior to migration
• • Creates knowledge repository for target optimization and scalability
• • Provides Data remediation and migration capabilities
• • Provides Data validation
• • Enables Data security through encryption
• • Provides Intuitive user-friendlyinterface that can be used by business users
• • Provide end-to-end data migration governance

About the Author

Yogeshar C –
Senior Consultant- Database, Data Warehouse & Analytics

Having more than 16 years of experience in Big Data and Data Warehousing, Yogesh is currently part of the Database and Data warehouse team focusing on helping clients modernize their data warehouses and move them to the cloud. He assists system and data analysis team in all Data Quality Management activities and helps in Designing the data quality processes and resolve all resource efficiency issues. Yogesh is responsible for coordinating with cross functional team and execute all project deployments. He has to Maintain knowledge on all data warehouse technologies and assist to make various decisions along with supporting the practice in managing cloud-based engagements and delivering end to end Big Data and Data warehousing solutions. His responsibilities also include supervising team working in various large scale and complex projects along with aiding end users and resolve all queries for data extraction and generate appropriate reports.