In today’s digital-first society, Cloud migration is no longer an option. For increased flexibility, scalability, and cost-effectiveness, a growing number of businesses are migrating to the Cloud. However, migrating to the Cloud can be difficult and complex if you have a large codebase in a legacy language or technology.
Enterprise-scale cloud migration typically involves petabytes of data. Depending on database complexity, table relationships, and active tables, it could take years to manually rewrite and translate each line of code. In addition, manual conversions frequently lack standardization and depend largely on personnel with expertise in both the legacy and target system technologies.
How Datametica’s Raven simplifies code conversion
With Datametica’s Raven, the automated transpiler tool, organizations can significantly reduce the amount of time required to convert code. Raven ensures that a system-wide standard for the converted tool exists. When established standards exist, debugging a problem becomes much simpler. Automation of code conversion reduces the amount of time spent on this step, freeing up more resources for post-migration data validation.
Any changes made to the code to solve an issue are more easily reflected in all translated jobs using Raven than if they had to be made manually. Raven supports ETL-formatted custom expressions and is capable of converting non-SQL ETL workflow components.
Raven converts ETL to ELT-based scripts that are compatible with the target cloud platform quickly and accurately. By decoupling the extraction, transformation, and loading stages, Raven ensures keeping extraction and loading on-premises while only the transformation part is kept at the staging area, where the data is re-coded and migrated to the Cloud. With this conversion of ETL codes to ELT codes, network bandwidth requirements, and potential performance issues are also eliminated. Raven’s automated processes minimize the dependency on technical experts of both the source and target systems, and largely reduce the costs associated with the code conversion process of the migration.
For converting legacy code to target system code, Raven uses a canonical model to perform the one-to-one mapping. This canonical model is created after reading and analyzing the metadata of the legacy data and is completely independent of the source system. This model is then translated into the system code to be implemented. With the help of such canonical models, code optimization is streamlined, and users can reap the full benefits of the target system. Raven also helps eliminate branch duplication and reduces budget allocation based on both process resources and duration.
There’s more to Raven
Code Optimization is an important feature of Raven that ensures consistent and syntactical optimization of the generated scripts and code. Raven exercises complete data lineage awareness when converting legacy code to target platform-compatible code. With automated code conversion, Raven ensures the elimination of redundancy. The workflow and target-aware optimizations result in significant cost and time savings for the organization, so they don’t have to spend any resources later to optimize the code, expressions, or workflows.
Additionally, with Raven, organizations can leverage the benefits of simplified conversion of ETL jobs and easier conversion of embedded custom SQLs, avoid duplicate branch processing, optimize code via a canonical model, support the existing orchestration tool, and fully leverage the features of the target system.
Conclusion
While code conversion can be a challenging stage of the migration process, utilizing cutting-edge technologies and tools can help businesses accelerate the process and migrate to the Cloud faster. Datametica’s Raven automates the code conversion process, which reduces errors, saves time and costs, enables faster error fixes, improves the visibility of efforts and results achieved in regards to time, and ultimately shortens the time required to migrate to the Cloud.
Datametica is an experienced cloud migration and modernization partner that has helped multiple businesses seamlessly migrate to the Cloud using its state-of-the-art migration automation suite. Reach out to us to learn how we can help you in your migration journey.
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About Datametica
A Global Leader in Data Warehouse Modernization & Migration. We empower businesses by migrating their Data/Workload/ETL/Analytics to the Cloud by leveraging Automation.
Accelerate Your Cloud Migration
FAQs
What is data conversion in ETL?
| ETL, which stands for extract, transform and load, is a data integration process that combines data from multiple data sources into a single, consistent data store that is loaded into a data warehouse or other target system. | Data conversion in ETL is the transformation of data from one format or structure to another in order to make it compatible with the target system or data repository. The objective of data conversion is to ensure that the data is usable and consistent in the target system so that it can be analyzed, reported, and utilized for decision-making. |
What is the meaning of ETL migration?
| ETL represents Extract, Transform and Load, which is a cycle used to gather data from different sources, change the data relying upon business rules/needs and burden the information into an objective data set. | ETL migration is the transfer of data, metadata, and processes from an existing ETL (Extract, Transform, and Load) system to a new system. ETL migration seeks to improve data integration, data quality, and processing efficiency while minimizing disruption to existing systems and processes. |
How do you convert data in ETL?
ETL Transformation Steps
| In ETL, converting data involves multiple steps:
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