Strengthened data platform resilience for scalable growth
Enabled real-time insights for faster decision-making
Reduced data exposure and infrastructure overheads
Owning Change in Financial Services
Our customer is a leading digital-native bank delivering tailored financial tools and insights to a fast-growing customer base across the region. As competition intensifies and expectations shift towards real-time, personalised experiences, they invest in modern, cloud-native data platforms that accelerate decisioning, unlock richer insight and scale seamlessly with demand.
From Batch Latency to Real–Time Decisioning
Removing Data Freshness Barriers Across Live Operations
Our customer was scaling their data engineering function to support the delivery of tailored financial tools and insights across their data lake, pipelines and real-time processing systems. However, their underlying ETL scheduling process still operated on an hourly batch cadence, limiting data freshness and constraining the speed at which teams could act on emerging insights.
With only 25% of the streaming infrastructure stood up internally, our customer required specialised data engineering support to complete the remaining transition while modernising the broader data platform in parallel, without disrupting live operations. Furthermore, they needed to balance platform transformation with business continuity, enabling real-time data capabilities, strengthening scalability and ensuring the modernised environment could support faster analytics, more responsive decision-making and future product growth.
Real–Time Streaming and Data Platform Transformation
Building Scalable Pipelines and Data Lake on AWS
To complete a complex, live migration without disrupting production, our customer engaged TEKsystems as a proven data engineering partner. With a proven record of modernising enterprise data platforms at pace, we deployed a domain-specialised squad to finish their transition from batch ETL to real-time streaming and lift the wider platform to a modern, cloud-native standard.
- Streaming Transition: Executed the remaining scope of the batch-to-streaming migration, building real-time data pipelines on Spark Streaming and Kafka to replace the hourly ETL scheduling process.
- Data Lake Modernisation: In parallel, led migration of the enterprise-scale data lake from Avro-based storage to Apache Iceberg, enabling ACID compliance, schema evolution and optimised table management on AWS.
- Orchestration Upgrade: To support these pipelines, orchestrated the Apache Airflow upgrade using Helm charts for Kubernetes deployment, introducing task groups and dynamic DAGs to lift workflow reliability and maintainability.
- Platform Integration: Completed the picture by partnering with product teams to structure schemas, design data models and integrate data sources into the modernised architecture.
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Real-World Results
TEKsystems’ engagement equipped our customer with a scalable, sustainable and responsive data platform capable of enabling faster, evidence-led decision-making at pace. We also strengthened the foundations for future product innovation by improving platform performance, workflow reliability and real-time processing capability across critical operational environments.
- Latency Reduction: Compressed data processing latency from hourly batch cycles to sub-second real-time streaming across operational workloads.
- Query Performance: Improved data lake query performance through the Apache Iceberg migration, unlocking faster analytics for downstream product and business teams.
- Storage Efficiency: Reduced data lake storage costs through Iceberg's optimised table management and schema evolution capabilities.
- Workflow Reliability: Strengthened orchestration reliability through the Airflow upgrade on Kubernetes, reducing DAG failures and lifting pipeline observability.
- Decision Velocity: Enabled dynamic, data-driven decisioning across product and operations by making fresh data available in near real time.
- Platform Scalability: Delivered a sustainable, cloud-native streaming architecture on AWS engineered to scale with our customer's growing user base.
