How retail data teams can build smarter, scalable ETL pipelines without drowning in code

By Sravanthi Kethireddy

In retail, time is money and data is leverage. But for most retail tech teams, turning terabytes of raw operational data into business insights still involves too much custom coding, fragile pipelines, and slow iteration cycles.

Now, imagine a process improvement in which you can build 80% of your data workflows - sales dashboards, customer segmentation, inventory forecasting - without writing a single line of code. How would this increase your productivity and outcomes?

Over the last several years working in multiple data rich verticals, I have experienced firsthand how traditional data ingestion and transformation workflows slow teams down. Seeking a solution to this common organisational problem, I built a no code Spark platform that enables data engineers to move faster, support broader use cases, and reduce tech debt, without sacrificing reliability or performance. This innovative solution can be adapted by other organisations applying the same principles to improve both speed and scale.

The data bottleneck

Data pipelines face unique challenges, starting with billions of transactions, events, and records generated daily. This is compounded by dozens of teams requesting insights from product, supply chain, and marketing data, all with ever changing needs, from new KPIs (Key Performance Indicators) to new suppliers and new types of sales promotions.

Most engineering teams struggle to keep up with this dynamic environment, often because they are bogged down writing workflows from scratch, maintaining redundant logic, and managing schema drift manually. The result is slower analytics delivery, rising cloud costs, and frustrated stakeholders.

A smarter approach: No Code ETL

I designed and built a framework that introduces a clear separation between pipeline intent and execution. Engineers specify the desired data outcomes, while the platform autonomously manages Spark orchestration, performance optimisation, and rigorous data quality enforcement at scale.

Employing this framework is like giving your team a high speed conveyor belt, as compared to having them carry boxes manually. The result is scalable, auditable, and easy-to-maintain workflows, even for complex retail datasets.

Key features tailored for retail environments

In designing the no code framework, I built in capabilities that make this technology a game-changer for real-world data teams. Engineers appreciate solution-driven and time-saving features such as:

·      Fast Time-to-Insight with Query Driven Logic: In retail, business questions change daily in retail. What is the impact of a new promotion? Which products are trending by region? By allowing teams to express logic directly in Query, the framework accelerates iteration, experimentation, and delivery.

·      Plug-and-Play Flexibility for Diverse Retail Data: From Supply chain logs to PoS receipts, inventory files to supplier catalogs, retail data is messy and varied. This framework supports multiple file types - Structured, Semi-structured, Unstructured, CSV, JSON, XML, Parquet, Avro, ORC, Delta Lake, Protocol Buffers (Protobuf), BSON, TSV, YAML and can sync to any Spark-compatible destination, allowing for structure without sacrificing flexibility.

·      Custom Modules for High Value Use Cases: Need to apply particular rules, definitions or product hierarchies? The system allows engineers to plug in custom extractors, transformers, and loaders, so even edge cases can be handled cleanly without hacking the core pipeline.

·      Built-In Data Quality and Schema Alignment: Bad data leads to bad decisions. The framework integrates validation, schema drift detection, and lineage tracking, so merchandising, finance, supply chain and operations teams can trust what they are seeing.

·      Performance Optimised for Retail Scale: Whether you are processing sales events from thousands of stores, or joining customer data across multiple touchpoints, the system supports built-in Spark optimisations like coalescing, persistence, and lineage breaking for efficient large-scale jobs.

·      Future Ready by Design: Retail data teams often face replat forming, version upgrades, or new compliance rules. This framework provides a single point of control for enhancements, providing faster response to change, without rewriting dozens of pipelines.

How it works

This no-code framework enables rapid adoption across retail enterprises by eliminating hardcoded transformation logic. Engineers specify desired data behaviour through a declarative abstraction, allowing the platform to autonomously construct, optimise, and execute the underlying processing pipelines.

·  Input

Standardised user activity data originating from an organisation’s operational systems, regardless of vendor, schema variations, or storage technology.

·  Processing intent

·       Apply organisation defined time windows and business rules

·       Derive aggregated, entity level metrics from raw activity data

·       Enforce consistent data quality, validation, and governance policies

·  Output

A curated, analytics ready dataset designed to support reporting, experimentation, and machine learning use cases.

Behind the scenes, the framework then dynamically:

• Reads runtime arguments and resolves them into the Structured Query Language

• Applies data quality rules and schema alignment

• Tunes Spark execution with best-practice configurations

• Writes the output in the desired format, with logging and observability baked in.

For the 10–20% of cases that require special treatment, such as integrating an API source or applying custom enrichment, the system allows developers to plug in their own modules without rewriting the entire pipeline.

Real results from a config driven model

Adopting this framework delivered measurable improvements across both retail platform performance and organisational agility. In a sample use case, significant benefits included:

• Significantly reduced the effort required to develop new data pipelines by eliminating repetitive boilerplate and abstracting execution complexity

• Substantially improved pipeline reliability through automated schema handling, validation, and fault tolerant execution

• Meaningfully lowered infrastructure overhead by applying advanced Spark optimisations, including coalescing, dynamic partitioning, autoscaling, adaptive query execution, intelligent shuffle management, predicate and projection pushdown, broadcast join optimisation, and workload-aware resource allocation

• Accelerated turnaround time for data requests, enabling stakeholders to receive results in dramatically shorter cycles

• Simplified developer onboarding by enabling engineers to deploy production grade data workflows with minimal prior Spark expertise

The framework has been deployed to support a variety of critical retail use cases, including loyalty and membership programmes, inventory and supply chain analytics, pricing and promotion management, customer data platforms, demand forecasting, and omnichannel operations. By streamlining processes, it has accelerated delivery timelines and reduced debugging effort and support overhead.

Designed to handle testing, validation, schema alignment, and deployment out of the box, the system allows engineers to focus on writing logic rather than developing boilerplate Spark jobs, improving efficiency across the organisation.

Governance, compliance, and standardisation benefits

Before adopting the framework, engineering teams commonly faced several recurring challenges that are prevalent across technology organisations:

·       Redundant Code: Multiple near identical workflows existed, differing only by source paths or filter logic.

·       Painful Migrations: Platform upgrades or version changes required manual edits across hundreds of jobs.

·       Inconsistent Standards: Teams followed different conventions, resulting in brittle dependencies and non-uniform structures.

·       Limited Semi-Structured Data Support: Evolving or complex data formats were difficult to manage without built-in compatibility.

The framework mitigates these challenges by enforcing a standardised configuration format and decoupling business logic from infrastructure code. It provides native support for schema evolution, centralised upgrade control points, and extensible plug-ins for custom validation and data loaders - capabilities essential for regulated and large scale environments.

Implications for retail data leaders

As an industry, retail operates too quickly for slow moving pipelines. Whether you are running demand forecasting, inventory optimisation, fraud detection, or customer segmentation, your data loads needs to keep up.

Adopting this framework provides a cost- and time-effective alternative to rebuilding a data platform from scratch. Integration is straightforward and follows best practices such as:

·       Shifting from manually coded workflows to configuration-driven execution.

·       Using a high level query or scripting interface as the primary means of expressing business logic.

·       Building modular pipelines that are easy to audit, extend, and reuse.

·       Investing in automation across validation, tuning, lineage, and orchestration.

As a result of implementing this system, organisations have benefitted from data teams that ship faster, stakeholders who trust their dashboards, and a tech stack that scales with your business. 

What’s next for Framework-managed data workflows in retail data platforms

Looking ahead, the future application of framework managed data workflows in retail data platforms aims to enable conversational, user driven data ingestion and transformation. Users could describe their requirements in plain language, and the system would automatically generate production ready pipelines that are tested, optimised, and deployed. For example, a request such as “Show weekly sales by category for top-performing stores” could result in a fully functional workflow without manual engineering intervention.

This evolution bridges the gap between business questions and technical execution, supporting self-service analytics and reducing the need for engineering support for routine data requests. Future enhancements for such frameworks may include:

·       Automated rule validation to prevent misconfigured workflows before execution.

·       Standalone data quality checks for high-priority datasets, such as pricing and inventory.

·       Automatic detection of the latest data partitions for near real-time updates.

·       Snapshot-based synchronisation to enable consistent point-in-time reporting.

Each advancement reduces manual overhead and strengthens pipeline governance, which is particularly valuable in large-scale retail environments.

Simplify to scale

Retail data engineering is about more than volume; it must also factor in velocity and agility. The faster your teams can move from raw data to business ready insights, the more responsive your operations become. By embracing config driven design, modularity, and automation, retail organisations can build smarter pipelines that support growth without growing complexity.

The best pipelines aren’t the most complex. They’re the ones that get out of your way.

About the author

Sravanthi Kethireddy is a staff data engineer and platform architect for the world’s largest retailer. Specialising in scalable, real-time data systems, she builds automation first infrastructure that helps data teams reduce friction, lower cost, and quickly move insights to decision-makers, leveraging more than a decade of experience in designing data centric digital transformations for global organisations across various industries.

Sravanthi is particularly recognised for her significant expertise in pioneering scalable Framework-managed data workflows for data giants. She received her bachelor's degree from the Institute of Aeronautical Engineering in Hyderabad, India, and earned a master’s in Computer Science from Northeastern University in Boston, Massachusetts (US).

Her ongoing professional development includes advanced credentials in cloud-based machine learning, artificial intelligence, and solution architecture with a focus on security. She has deep expertise in data engineering, including the design and management of scalable data pipelines, data integration and transformation workflows, data quality and governance practices, and analytics platform architecture.

She is also skilled in automation, distributed data processing, and optimising large scale data systems to support real-time and batch workloads.