All the interactions with the C-suite across multiple companies point to one thing: leaders don’t see cloud migration as an IT project anymore. They see it as a business strategy, one that directly impacts speed, resilience, and innovation. And almost every CTO or CIO says the same: migration isn’t the finish line. It’s where the real work, and the real value, begins. And one of the earliest areas where this becomes clear in the modernization journey is in how organizations approach code migration.
Code migration is an integral part of the ongoing race to modernization. However, organizations continue to rely on traditional, manual means of code migration, which is both slow and labor-intensive. Legacy warehouses and ETL systems have become resource-intensive and challenging to manage, requiring the availability of professional teams with expertise in various technologies.
Automated code conversion has emerged as the strategic imperative, driven by the need for improved agility, scalability, and automation. At the same time, enterprises are clearly favoring cloud migration, which is expected to grow from a market size of $10.2 billion to $29.2 billion by 2029. Cloud deployments accounted for nearly 68% of the revenue share in legacy modernization in 2024.
These numbers indicate that organizations are making transformative changes in restructuring their existing systems and applications, instead of re-hosting (or re-platforming) older systems. But this push toward true modernization also exposes the gaps in traditional approaches, especially when it comes to code conversion.
As an automated code conversion tool, Onix’s Raven tool can overcome the limitations of traditional, manual methods. Here’s a deeper insight into the current challenges in modernization – and how Raven can help.
What’s driving the modernization initiative?
The use of legacy warehouses like Teradata, Oracle, and Informatica has increased technical debt, which is not just a “software issue,” but a “business risk.” The continued reliance on legacy applications and outdated systems has gradually added to the technical debt. It slows down modernization efforts, makes integration harder, increases operational risk, and raises the cost of change, ultimately impacting the speed and agility of the business.
According to McKinsey, 10-20% of technical budgets (assigned for new products) is spent each year on addressing issues arising due to technical debt. 60% of technical debt has increased in the past 3 years, driven by the organizational need to modernize for AI, analytics, and automation. By 2027, 75% of enterprises will face systemic failures due to unmanaged technical debt. Which brings us to the real issue every organization eventually feels on the ground.
Here are some of the challenges that arise due to rising technical debt:
- High maintenance costs – It’s expensive to maintain legacy warehouses and systems, consuming the majority of the allocated IT budgets. The high costs are often related to complex, customized code and process-related inefficiencies.
- Talent shortage – Legacy warehouses rely on legacy tools and proprietary technologies, which no longer enjoy a large pool of skilled professionals.
- Complex and expensive migration – Data migration from legacy warehouses (like Teradata) is complex because of their proprietary architecture and data auditing.
All of this ultimately leads to a tougher reality: the hardest part of modernization is not infrastructure migration but converting legacy SQL, ETL, and orchestration logic. Here’s a look at the core challenges linked to manual code and ETL conversion during modernization:
Challenges in Manual Code and ETL Conversion
Manual code and ETL conversion introduce their own set of challenges, from high effort and long timelines to dependence on niche legacy skills. Here are some of the most common issues teams face during this stage of modernization:
- SQL dialect fragmentation
Among the common hurdles, legacy databases like Oracle and SQL Server have their proprietary SQL dialects with unique functions, data types, and expressions. This can lead to barriers such as incompatible functions, data type mismatches, and vendor-specific methods and architecture.
Complex stored procedures in legacy databases can also be a bottleneck in database modernization. For instance, stored procedures in procedural languages – for example, PL/SQL for Oracle – can present compatibility issues in the target environment. - ETL workflow challenges in Informatica and DataStage
ETL pipelines and workflows in legacy tools like Informatica PowerCenter (INF) and IBM InfoSphere DataStage (or DataStage) are major bottlenecks in modernizing ETL workloads on the cloud. This involves migrating the core integration logic from a proprietary environment to a cloud-native ecosystem like Databricks.
Another related challenge is the lack of documentation for legacy ETL jobs, without which modernization teams need to manually reverse-engineer the code snippets and mappings. - Orchestration failures
Manual cloud migration from legacy systems often fails due to challenges with replicating and managing the orchestration layer in any cloud environment. During the migration process, orchestration failures can occur as most cloud-native tools have a different execution model from legacy tools.
On-premises legacy orchestration is typically managed by a single, monolithic system like a dedicated scheduler or legacy ETL tool. The cloud migration means adopting a decentralized, service-based architecture, which decouples processes into smaller services.
And this is the point in the modernization journey where automation becomes critical, where years of experience were applied to solving these challenges and ultimately led to the creation of Raven: The Transformer, an intelligent AI-powered tool for automated code and ETL conversion.
Automating cloud modernization with Raven
Onix’s Raven tool is an intelligent, AI-powered code conversion and modernization engine designed to modernize SQL, ETL, and orchestration for the cloud. Among its multiple capabilities, Raven can automate the conversion of legacy SQL code and ETL workloads from legacy systems (Oracle, Teradata, Datastorage, Informatica) to cloud-native environments.
Let’s look at the real challenges teams run into today and how Raven solves them with AI capabilities and enterprise-grade features built for large-scale modernization :
- Intelligent SQL conversion engine
Existing problem: Enterprises using traditional warehouses, including Teradata, Oracle, Snowflake, and Netezza, possess large volumes of proprietary SQL scripts (with incompatible syntax). Similarly, legacy SQL code often contains complex nested queries, macros, dynamic SQL, and procedures, which are challenging to convert using manual, rule-based translation tools.
While manual rewriting can be error-prone and time-consuming, an incorrect SQL translation can lead to problems like mismatched data, performance regressions, and even failed migrations.
How Raven addresses this problem: Raven features an intelligent SQL engine, which comprehends the SQL semantics, and not just syntax. This intelligent SQL conversion engine also leverages AI models for unique translation patterns and rare edge cases. Besides, Raven can adapt to multiple dialects and coding styles, thus accelerating SQL modernization with both accuracy and validation. - Composer DAG deployment & auto-fixing agent
Existing problem: During cloud migration, composer DAGs can fail because of dependency gaps, mismatched paths, parameter issues, and environmental differences. Further, it’s time-consuming to manually debug DAG failures and it requires orchestration skills. A failed DAG can stall data pipelines, slow down testing, and thus delay the migration process.
How Raven addresses this problem: Raven’s inbuilt agents can automatically detect any DAG-related issues and apply the corrective action, like fixing the configuration, parameters, or any dependencies. It can also automatically redeploy the fixed DAG and revalidate the execution. This helps in reducing downtime and ensuring functioning orchestration pipelines. - End-to-end ETL workflow conversion
Existing problem: As mentioned before, legacy ETL tools like INF contain nested transformations and undocumented job dependencies. Hence, it’s time-consuming to manually rewrite ETL logic for cloud-based pipelines. Further, any missed dependency or incorrect logic mapping can break down jobs and corrupt the data. Enterprises need a reliable ETL conversion tool that preserves both logic and sequencing.
How Raven addresses this problem: Raven can automatically convert ETL workflows and jobs into cloud-native pipelines. It can also automatically convert ETL logic into Composer DAGs and BigQuery SQL. During migration, it preserves all the job dependencies, sequencing, and workflow logic. By reducing manual efforts, it can eliminate migration errors and quickly modernize large ETL estates. - Automated SQL translation
Existing problem: SQL migration typically involves migrating thousands of SQL scripts, which is too massive for manual means. Every SQL source – Teradata, SQL Server, or Snowflake – has unique SQL constructs, which cannot be manually translated.
Hence, migration teams end up spending time rewriting any syntax-related differences and debugging any mismatches. This can lead to incorrect outputs and delayed migration.
How Raven addresses this problem: Raven can automate SQL script conversion, along with macros, views, stored procedures, and queries. It supports major dialects, including Teradata, Oracle, SQL Server, and Snowflake. This tool can also convert complex logic and embedded procedures. Effectively, it can accelerate the data modernization process across workloads. - Data assurance and validation
Existing problem: Post the SQL migration, enterprises also need to ensure data consistency between the source and target environments. Manual data validation can be slow and prone to errors. Any mismatches can lead to production failures and unreliable data.
How Raven addresses this problem: Raven can automatically execute queries on both source and target systems to check for consistency. It compares every database row, output, and execution. Besides flagging any mismatches, Raven provides a detailed resolution report and ensures data validation at the enterprise level. - End-to-end migration support
Existing problem: Larger enterprises have massive volumes of SQL and ETL estates. It’s complex to coordinate the conversion of such workloads, along with DDL handling, macro conversions, and workflow mapping. What’s needed is an end-to-end oversight into the data migration process, without which it can be fragmented and unpredictable. Enterprises need a unified tool or platform to monitor the migration process.
How Raven addresses this problem: As a complete ETL data migration tool, Raven can handle bulk script conversion, along with SQL/ DDL objects. Along with managing macros, views, and functions, Raven can provide exception tracking, coverage metrics, and summaries. This helps in making complex migrations more predictable and efficient. - Real-time insights and reporting
Existing problem: Cloud migration teams need visibility and transparency to monitor their work progress and quality. Without real-time visibility, they cannot identify any bottlenecks, errors, or areas that require human intervention. Business stakeholders also need to track metrics to plan their timeline.
How Raven addresses this problem: Raven provides complete visibility into the data conversion, quality, and validation results. It can deliver insights in the form of detailed summaries, error diagnostics, and any mismatches. It also displays metrics like successful conversions, manual rewrites (if any), and validation mismatches. - Data security and assurance
Existing problem: As cloud migration involves sensitive data, enterprises need to maintain strict access control and compliance. Credential mismanagement and insecure routing can pose serious security risks.
How Raven addresses this problem: Raven ensures enterprise-grade security with single sign-on (SSO), LDAP functionality, and integration with Vault. It supports features like secure impersonation of service accounts and efficient proxy routing. Other capabilities include multi-tenancy isolation (for multiple customers) and auditable migration practices. - AI enhancements
Existing problem: Legacy systems often have issues like missing metadata, undocumented logic, and inconsistent coding. Manually identifying these issues can lead to errors and delays. Traditional tools cannot validate complex transformations and intent.
How Raven addresses this problem: Raven can leverage its AI functionality to infer any missing metadata or inconsistencies during the migration process. It can also provide an intelligent validation to ensure correct outputs, and compare procedural equivalents with its semantic understanding.
Conclusion
Modernizing legacy data ecosystems is essential for any organization aiming to stay competitive, scalable, and innovation-ready. But the most significant challenges lie not in infrastructure migration, but in converting decades of SQL logic, ETL workflows, and orchestration layers burdened by technical debt. Raven helps eliminate these obstacles with intelligent automation and enterprise-grade capabilities, enabling faster, more accurate, and more confident modernization, so organizations can not just migrate, but truly modernize.
The Raven tool is part of Onix’s Birds suite of modernization tools, also comprising the following:
- Eagle handles the initial assessment process, including data discovery, planning, and migration strategy.
- Pelican executes the data pipeline and movement.
- Kingfisher generates synthetic data for secure testing and validation.
Contact us to know more about the Raven tool.
