What is Lambda Architecture Basics — Data Engineering
Lambda architecture is a data processing framework that aims to provide a unified and fault-tolerant approach to big data processing. The architecture is designed to handle both batch and real-time data processing, providing a comprehensive solution for handling large-scale data analysis. In this article, we’ll explore the basics of lambda architecture, its components, and its benefits.
Overview of Lambda Architecture
Lambda architecture is a data processing framework that was introduced by Nathan Marz in 2014. It aims to provide a unified approach to big data processing by combining the strengths of batch processing and real-time processing. The architecture is designed to handle data processing in a fault-tolerant and scalable manner, making it ideal for handling large-scale data analysis.
Lambda architecture consists of three main components:
- Batch layer
- Speed layer
- Serving layer
These layers work together to provide a comprehensive solution for handling both batch and real-time data processing.
Batch Layer
The batch layer is responsible for handling batch processing of large amounts of data. It stores all the data in a master dataset, which is immutable and contains all the raw data. The batch layer processes the data in batches, usually overnight or on a regular schedule, and updates the master dataset with the results of the processing.
The batch layer is designed to be fault-tolerant and scalable, ensuring that the processing can be carried out even if there are failures or errors in the system. The batch layer is responsible for creating a batch view of the data, which is used for analysis and reporting.
Speed Layer
The speed layer is responsible for handling real-time data processing. It receives data streams and processes them in real-time, providing immediate results. The speed layer uses distributed systems, such as Apache Storm or Apache Spark, to process data in real-time.
The speed layer is designed to handle high-velocity data streams, ensuring that data processing can be carried out in real-time, even with high volumes of data. The speed layer is responsible for creating a real-time view of the data, which is used for real-time analysis and reporting.
Serving Layer
The serving layer is responsible for providing a unified view of the data. It combines the results from the batch and speed layers and provides a single view of the data. The serving layer is responsible for answering queries and providing results to users.
The serving layer is designed to be fault-tolerant and scalable, ensuring that it can handle high volumes of requests and provide quick responses to users. The serving layer creates a serving view of the data, which is used for querying and reporting.
Benefits of Lambda Architecture
Lambda architecture provides several benefits for big data processing:
- Fault-tolerant: Lambda architecture is designed to be fault-tolerant, ensuring that data processing can continue even if there are failures or errors in the system.
- Scalable: Lambda architecture is designed to be scalable, ensuring that it can handle large volumes of data processing.
- Real-time processing: Lambda architecture provides real-time processing of data, ensuring that users can receive immediate results.
- Batch processing: Lambda architecture provides batch processing of data, ensuring that all the data can be processed, even if it takes a long time.
Conclusion
Lambda architecture is a comprehensive solution for handling big data processing. It combines the strengths of batch processing and real-time processing, providing a fault-tolerant and scalable approach to data processing. Lambda architecture provides several benefits, including fault tolerance, scalability, real-time processing, and batch processing. With lambda architecture, organizations can handle large-scale data analysis and gain valuable insights into their data.
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Asim is an applied research data engineer with a passion for developing impactful products. He possesses expertise in building data platforms and has a proven track record of success as a dual Kaggle expert. Asim has held leadership positions such as Google Developer Student Club (GDSC) Lead and AWS Educate Cloud Ambassador, which have allowed him to hone his skills in driving business success.
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