How do you use lambda architecture to handle batch and stream data?

1 What is lambda architecture?

Lambda architecture is a data pipeline design pattern that consists of three layers: batch, stream, and serving. The batch layer is responsible for processing large volumes of historical data using batch jobs that run periodically. The stream layer is responsible for processing real-time data using stream processing frameworks that handle incoming data streams. The serving layer is responsible for querying and presenting the results of both batch and stream layers using a unified interface. The idea is to leverage the strengths of both batch and stream processing while minimizing the latency and complexity of the data pipeline.

2 How does lambda architecture work?

The core concept of lambda architecture is to maintain two parallel views of the data: a batch view and a stream view. The batch view is created by running batch jobs on the raw data stored in a distributed file system, such as Hadoop or S3. The batch jobs perform complex transformations and aggregations on the data and store the results in a batch database, such as HBase or Cassandra. The stream view is created by running stream processing frameworks, such as Spark Streaming or Flink, on the incoming data streams. The stream processing frameworks perform simple transformations and aggregations on the data and store the results in a stream database, such as Redis or Kafka. The serving layer then queries both the batch and stream databases and combines the results using a lambda function, which is a logic that reconciles the differences between the two views. The serving layer can also expose an API or a dashboard for end users to access the data.

3 What are the benefits of lambda architecture?

Lambda architecture offers several benefits for data engineers who need to handle batch and stream data, such as providing a comprehensive and consistent view of the data, reducing latency, improving fault tolerance and scalability, and simplifying the development and maintenance of the data pipeline. By combining both historical and real-time data, it enables fast queries on the stream view while maintaining the accuracy of the batch view. Furthermore, it decouples the batch and stream layers to allow them to run independently and in parallel, as well as using standard and modular components for each layer while avoiding complex logic in the serving layer.

4 What are the challenges of lambda architecture?

Lambda architecture poses certain challenges for data engineers who need to implement and operate it, such as needing more resources and infrastructure to run two parallel data pipelines and store two copies of the data. This introduces complexity and overhead in the data pipeline, as synchronization and coordination between the batch and stream layers and the serving layer is required. Additionally, data inconsistency and duplication between the batch and stream views may occur, especially when dealing with late or out-of-order data. Furthermore, complex or interactive queries on the data may not be supported, as the serving layer has to rely on pre-computed results from the batch and stream layers.

5 How can you use lambda architecture in practice?

If you want to use lambda architecture in your data engineering projects, you need to consider several factors, such as the data sources, the processing requirements, the query patterns, and the technology stack. To get started, you must define the scope and objectives of your data pipeline, including the data sources, formats, quality, and volume; the processing needs; business logic; performance metrics; query patterns; user expectations; and output formats. You must also choose appropriate tools and frameworks for each layer of the data pipeline that can integrate with each other and with your existing infrastructure. Additionally, you must design and implement the batch and stream layers of the data pipeline by handling data errors, failures, retries, and ensuring data quality, security, and compliance. Furthermore, you must design and implement the serving layer of the data pipeline by querying and combining data from both layers and implementing a lambda function that reconciles them. Lastly, you must test and monitor the data pipeline by validating correctness and completeness of the data; measuring performance and efficiency; as well as troubleshooting and resolving issues.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?