Language engineering:
A framework for seamless cloud migration
Language Engineering is a one-stop solution for SQL translation needs. Learn how language engineering can automate an enterprise cloud migration.
Oct. 13, 2021 | By Pradipta Dhar
When an organization plans their infrastructure needs, it is impossible to future-proof beyond a few decades, at best. An organization that started in the mid 1980’s to early 1990’s certainly had no way of forecasting the way cloud platforms have transformed IT infrastructures today.
Are on-premise infrastructures a problem?
Yes—when an organization prefers to keep their infrastructure on-prem, they lose the opportunity to use the benefits a cloud environment provides. Think: cost effective resource utilization, automatic resource and infrastructure management. And while a move to the cloud seems like a no-brainer, it can be a massive undertaking: planning and transitioning workloads is cumbersome if needed to be done manually.
How language engineering can automate cloud migration
Language engineering (LE) is a framework to help automate part of the cloud migration process. It translates your data definition language (DDLs) and data manipulation language (DMLs) from one structured query language (SQL) dialect to another. This helps avoid the manual effort of trying to identify the syntactical and functional differences between the dialects for each statement and then converting. LE helps reduce work hours and minimize the errors that come with a human process.
Google Cloud Platform and language engineering
LE is highly focused on solving the problem of migrating workloads from various systems into the Google Cloud Platform (GCP). Along with other frameworks, LE is designed to seamlessly work with GCP services through connectors and application programming interfaces (APIs). The APIs are built to leverage the key features of various GCP services such as Google Cloud Storage and Google BigQuery to help automate as much of the migration process as possible.
The goal? Provide seamless connectivity and automation for much of the migration process from on-premises systems to Google Cloud Storage to Google BigQuery. This method solves the problems that come with human interventions such as man-made errors, longer man-hours and higher overall costs.
How does language engineering work?
Below are the steps to the LE process:
Identification
LE addresses the task in a systematic and efficient manner. It starts by reading the input as a whole and then parsing to identify the points of difference between the source and target dialects.
Translation
Once it identifies the list of changes required to be made, it then addresses the changes that are minor and mostly to do with naming conventions and minor syntax changes.
Conversions
The next step is to make the changes that are more functional and differ significantly for each dialect. The source and target dialects are fed to LE which in turn maps the changes required and handles the conversion accordingly.
What does language engineering do?
LE is designed to work with Oracle, Teradata and Hive SQL as source systems and Google BigQuery as a target system. It can successfully work with the following:
- DDLs (Tables, Views, simple functions, and simple procedures)
- DMLs (Simple, Medium, and Complex queries)
- Handling aggregate functions, string functions, date functions and with clauses
- Handling complex subqueries
Why should you implement language engineering?
Besides automating a manual process, LE is designed to provide a complete end-to-end user experience that is easy-to-use, efficient and insightful.
Intuitive User Interface (UI)
LE provides users with a smart and intuitive UI that helps you achieve the following:
- View and use historical conversions
- Compare the differences between source and target SQL dialects
- Type SQL statements on the go, or upload existing files for conversion
- View detailed stats and summary reports for each conversion cycle
Insightful statistics
LE provides detailed and insightful statistics for each conversion process to help users gain detailed understanding of the conversion points and errors.
Speed
Converting with LE allows you to scale—providing a significant reduction in time required and eventually translating to financial benefits for the organization.
Cloud agnostic
LE considers that not everybody would have the same infrastructure requirements or options. Therefore, we make LE a cloud-agnostic framework to be easily deployable across platforms with relative ease
Scalability
LE provides outdated systems the ability to scale, while including new features and dialects of the future.
- Language Engineering offers a high-speed solution to translate SQL dialects
- Easy to use GUI makes the migration process user friendly and flexible
- Detailed summary reports help analyze translation results and keep track of things that much easier
- Cloud agnostic design ensure high portability with no compromise in features or speed while maintaining the same use experience
About the Author
Pradipta is a highly driven and quality focused software engineering professional with 5+ years of experience in software development and data engineering. He specializes in data pipeline design and various programming languages with hands-on implementation expertise. He possesses the ability to manage and work with large teams to be able to build robust and streamline processes to increase productivity and deliver quality results while at the same time enabling everyone in his team to learn new things and step up to the next level. He is on point with his communication skills and has been known for establishing healthy and trustworthy relationships with clients and colleagues alike.
