Lambda architecture

A data architecture defines how data is collected, transformed, stored, and exposed to meet various business needs, both short- and long-term, while adhering to governance requirements. Choosing the right architecture is essential to ensure high availability, persistence, optimized storage, and efficient processing of diverse data volumes.

The Lambda architecture is a hybrid model, divided into two flows to handle both real-time and batch data processing.

• The Batch Layer processes large volumes of data at regular intervals. It ensures maximum consistency and provides reliable and durable access to data.
• The Speed Layer processes real-time data, ensuring high availability at the cost of some precision. The Batch Layer consolidates and corrects data if necessary.

The Serving Layer, or exposure layer, plays a key role in making data accessible to systems and end users. It aggregates results from both processing layers, delivering data that is both fresh and reliable while enabling fast and optimized queries. While this architecture is powerful, it can be complex to implement due to the simultaneous management of streaming and batch processing.

Among our clients, the Lambda architecture has provided real-time access to information while ensuring daily data updates. It meets governance needs, deduplication, and data cleansing requirements.

Theodo’s point of view

We recommend the Lambda data architecture when high availability and daily processing requirements need to be combined. However, its implementation requires significant effort and incurs maintenance costs that must be carefully considered—ensuring that the investment aligns with the business need is crucial!