Ismail profers insight to building resilient ETL pipelines

Expert in financial data systems, Akeeb Ismail, has shared valuable lessons on the importance of building resilient ETL (Extract, Transform, Load) pipelines in the banking sector. Having designed and optimised data pipelines for companies such as Moni and Okra, Ismail stresses that creating robust data systems isn’t merely about writing code but about ensuring the system can withstand failures, adapt to changes, and consistently deliver value.

Speaking on the matter, Ismail remarked, “Resilience in ETL pipelines isn’t something you bolt on at the end; it’s something you design for from the very beginning.”

ETL pipelines are fundamental in data-driven organisations, responsible for taking raw data from multiple sources, cleaning, transforming, and loading it into destinations where it can be analysed. However, when working in high-volume environments like financial systems, the challenge intensifies. Financial systems process millions of transactions daily, each with its own attributes and dependencies. Ismail emphasises that a resilient pipeline must handle these failures without losing data or introducing errors.

One of the core lessons Ismail has learned is the necessity of idempotency, meaning operations that can be repeated without changing the result. He points out that when a task fails and needs to be retried, it’s essential to prevent duplicate records or data corruption. Ensuring idempotency through techniques like upserts (update or insert) and using checksums to detect duplicate data helps maintain pipeline reliability. “Idempotency isn’t just a nice-to-have; it’s a requirement if you want your pipelines to be reliable,” Ismail says.

Modularity is another principle Ismail advocates for. He explains that a monolithic ETL pipeline, where all logic is crammed into a single script or job, becomes difficult to maintain. Instead, he recommends breaking the pipeline into smaller, independent tasks. By doing so, when one task fails, it doesn’t disrupt the entire pipeline, allowing for easier troubleshooting and resilience. Tools such as Apache Airflow, which supports Directed Acyclic Graphs (DAGs), enable the design of more flexible and modular workflows.

Monitoring and observability also play a vital role in creating resilient pipelines. Ismail stresses that without adequate monitoring, it’s impossible to understand what’s happening inside the pipeline. He advises implementing logging, metrics, and alerts to ensure the system is functioning as expected. “Monitoring isn’t just about collecting data; it’s about making that data useful,” Ismail explains. By tracking metrics such as throughput, latency, and error rates, you can identify trends and pinpoint issues before they become critical.

Another major challenge in financial data engineering is managing schema changes. In a fast-moving industry like finance, data requirements change frequently. Regulations, product updates, or new transaction types may necessitate changes to the pipeline. Ismail advocates for flexible solutions like schema-on-read or versioning, which allow pipelines to adapt to changing data without requiring a complete overhaul.

Error handling in financial pipelines requires robust strategies, particularly in the extraction, transformation, and loading stages. Ismail stresses the importance of implementing retry logic, dead-letter queues, and manual intervention workflows to minimize disruption and maintain data integrity.

Scalability is also paramount in financial data systems. With massive data volumes and low-latency requirements, distributed architectures using tools like Apache Spark and Apache Kafka are often necessary. These tools enable parallel processing and real-time data streaming, but they add complexity. Partitioning, replication, and fault tolerance must be carefully considered to prevent system failure under load.

For Ismail, rigorous testing is key to resilient pipeline design. “You can’t just build a pipeline and hope it works,” he says. He emphasises the need for load testing, chaos engineering, and real-world failure simulations to ensure the pipeline functions properly under stress and can handle unexpected disruptions.