Service
Data and Analytics
AI and Data & Analytics: Powering Tomorrow’s Intelligent Enterprise
Highlights
- Modernising legacy ETL data systems reduces complexity and enables faster, data-driven insights.
- Generative artificial intelligence (GenAI) automates code generation, streamlining development and migration to cloud-native platforms.
- ETL modernisation improves speed to market, with organisations deploying data solutions 40-60% faster.
- Cost efficiency is achieved, with GenAI-based ETL resulting in 30-40% savings in development and maintenance costs.
- Automated processes minimise human errors by up to 25%, leading to more reliable data pipelines.
On this page
On this pageinpage
ETL is a critical process
In today’s fast-paced business landscape, organisations are under immense pressure to update legacy data systems, streamline operational processes, and deliver actionable insights quickly. This is because the systems and pipelines built for yesterday’s scale and reporting needs can no longer keep up with today’s data volumes, business velocity, and expectations for real-time insight.
At the core of these efforts is the extract, transform, load (ETL) process that is critical for moving, cleansing, and standardising data from multiple sources before loading it into analytics platforms. ETL is fundamental to a variety of operations, including compliance reporting, real-time dashboards, and training AI models. Therefore, inefficiencies in ETL processes can significantly impede timely business decision-making.
In AI era, ETL modernisation is an urgent imperative
Over time, ETL pipelines have become progressively complex, fragile, and expensive to maintain, especially as data volumes grow and business requirements evolve. Modernising ETL is, thus, essential not only for improving performance but also for establishing a resilient, artificial intelligence (AI)-ready data infrastructure. As organisations embrace real-time analytics, AI-driven decision systems, and cloud-native architectures, traditional ETL approaches often become bottlenecks.
Research indicates that data engineers spend approximately 40-50% of their time maintaining and operating data pipelines, time that could be better utilised for innovation, analytics, or creating business value. Modernising ETL pipelines enables faster onboarding of new data sources, reduces operational overhead and error rates, lays the foundation for real-time and AI-powered business processes, and enhances trust in data consumed by downstream models and applications.
A future-proof approach with GenAI
While many modernisation initiatives focus only on cloud migration or tool replacement, a more future-proof approach is one that is powered by generative AI.
Instead of simply ‘lifting and shifting’ workloads, this approach helps:
- Reimagine ETL logic through natural-language-driven automation
- Standardise transformation patterns across business units
- Embed data quality checks into pipelines
- Reduce human effort in code creation, test generation, and documentation
It enables enterprises to move from fragmented, batch-oriented data ecosystems to real-time, AI-ready infrastructures.
GenAI accelerates ETL modernisation
GenAI accelerates ETL transformation by helping overcome bottlenecks such as manual rewrites, limited documentation, and growing quality risks at scale. GenAI embeds intelligence and automation across the ETL lifecycle, speeding up modernisation while improving readability and consistency. This enables the following capabilities:
1. Automated code generation
Engineers spend up to 70% of project time writing or rewriting ETL logic. GeneAI can transform natural language design documents or legacy logic into optimised SQL or PySpark code.
Large language models (LLMs) can interpret existing ETL logic (from SQL, Python, legacy ETL tools) and translate it into scalable cloud-native code, apply reusable transformation patterns for consistency, and automatically rewrite logic to meet new architectural standards.
For example, for a global retail client, TCS automated the conversion of 80 ETL jobs, reducing manual effort by 40%.
2. Code conversion for Databricks and cloud platforms
For enterprises adopting Databricks, Azure, AWS, or GCP, GenAI simplifies the migration of existing pipelines into native runtime formats such as PySpark. This approach reduces human error in translation, decreases the time spent deciphering legacy code, and lowers dependency on highly specilised skill sets.
3. Unit test case generation
Quality assurance can lag when thousands of ETL jobs require migration. GenAI automates the creation of unit tests, boundary condition tests, and transformation logic validations. These tests are exportable into notebooks, pipelines, or Databricks jobs using representational state transfer application programming interfaces (REST APIs), greatly speeding up quality assurance cycles.
4. Synthetic test data generation
LLMs can generate contextually accurate test data that mirrors production patterns without exposing sensitive information. This facilitates faster validation of migrated pipelines, provides safe testing environments for AI models, and ensures higher confidence before final cutover.
5. Automated documentation
Clear documentation is one of the most persistent gaps in legacy ETL systems. GenAI can automatically produce documentation for input and output specifications, data lineage, transformation logic, parameter descriptions, and operational guidelines. This enables faster onboarding for engineering teams and significantly reduces future maintenance requirements.
Data quality gets a big boost
Modern ETL modernisation is incomplete without data quality measures.
Using AI-powered agents, organisations can:
- Detect anomalies, duplicates, missing values
- Recommend cleansing rules
- Flag schema drifts
- Generate data quality scorecards
- Integrate automated quality checks with Unity Catalog, Delta Live Tables, or enterprise metadata platforms.
All these help improve the quality of data, resulting in better insights for intelligent decisions.
The business benefits
By applying GenAI to ETL modernisation, enterprises move beyond technical efficiency to deliver clear, measurable business outcomes. Automation and standardisation across the data pipeline directly translate into faster delivery, lower costs, and more reliable data foundations. The key business benefits include:
- Speed to market: Organisations achieve 40-60% faster deployment of data solutions, enabling rapid response to market opportunities and competitive advantages.
- Cost efficiency: Implementation of GenAI-based ETL code generation leads to 30-40% reduction in development and maintenance costs through automated code generation and reduced manual effort.
- Development acceleration: Development cycles are shortened by up to 70% through automated code generation, allowing teams to deliver ETL solutions significantly faster than traditional manual coding approaches.
- Process standardisation: Standardised code generation ensures uniform implementation of ETL patterns across all data transformation processes, reducing maintenance overhead and improving code quality.
- Error reduction: Automated code generation minimises human errors by up to 25%, resulting in more reliable data pipelines and reduced debugging time.
As enterprises move toward AI-first operating models, the modernisation of ETL systems is a foundational step. This enables business to make the most of the opportunities that present themselves in a dynamic business landscape and seamlessly adopt cloud-native, real-time data ecosystems.