Architecture & DesignAn Introduction to The Lambda Architecture

An Introduction to The Lambda Architecture

We dwell in an era of technology where Big data, the Internet of Things, Machine Learning have all become an inevitable reality. In today’s world, decision-making processes thrive on data that can span various data sources such as social media, log files, sensor data, etc. While the heterogeneity of data has increased manifold, the need for reacting fast follows. Modern software architectures are built to cater to such requirements for sustenance in this ever-changing business world.

With the proliferation of the technologies mentioned above, several other technologies and architectures have sprung up as well. One such architecture is called the Lambda Architecture. Although Big data offers unparalleled insights and possibilities galore, there are specific issues that you should be aware of and address as well. Big Data refers to enormous amounts of data, both organized and unstructured.

The Lambda Architecture, coined by James Warren and Nathan Marzis, is a blend of batch processing and real-time stream processing techniques to ingest, analyze, and query new and historical (batch) data. It is capable of effectively processing large amounts of data efficiently.

This article takes a deep dive into the concepts related to Lambda architecture, its benefits, and drawbacks.

What is Lambda Architecture?

The Lambda Architecture is a data processing architecture based on lambda calculus that enables processing vast amounts of data at scale and building big data systems as a series of layers. It allows for massive data processing by scaling out rather than scaling up.

The Lambda Architecture is a new paradigm for handling vast amounts of data. It is a scalable and easy-to-understand approach to processing large volumes of rapidly arriving data using batch and stream-processing methods. The Lambda architecture is used to create high-performance, scalable, flexible, and extendable systems.

Lambda Architecture Design Goals

Here are the design goals of the lambda architecture:

  • Low latency
  • Data consistency
  • High fault tolerance
  • Improved scalability

Applications of Lambda Architecture in Big Data

In recent times, there has been a surge in the volume of data that organizations have had to process. This increase in the demand for handling massive quantities of data and the need for getting insights from that data have necessitated new technologies and architectures.

With the rise in popularity of Big Data, Machine Learning (ML), and the Internet of Things (IOT) in recent years, many technologies and architectures such as the Lambda Architecture, Kafka, Hadoop, etc., have evolved to cope with them.

The Lambda architecture is a new paradigm in Big Data computing and has gained a lot of popularity in recent times. It is an excellent choice for processing large amounts of data quickly and efficiently. Because of its ability to balance latency, throughput, and fault tolerance, it is a perfect choice for developing large data systems.

Components of Lambda Architecture

The Lambda architecture is composed of the following three layers.

Batch Layer

The batch layer is usually a “data lake” system that saves all incoming data as batch views. The batch layer guarantees data consistency by leveraging immutability. Hence, only copies of the original data are generated and stored. The batch layer may also pre-compute results using distributed processing systems like Hadoop. You can take advantage of Apache Hadoop to ingest the data and store it cost-effectively.

Serving Layer

The Serving Layer responds to user queries and offers low-latency access to the master dataset’s computations. The serving layer receives batch and real-time views from the batch and serving layers, respectively, and exposes pre-computed views so that the data can be queried as needed. The serving layer aggregates the results of the Batch and Real-time Views into a single dataset. The serving layer supports read-only data access and real-time queries.

Speed (Streaming) Layer

The speed layer complements the serving layer and indexes the most recent data. It offers near-real-time results in low latency and uses stream processing to index incoming data in real-time to minimize the latency of getting the data for querying.

Use Cases for Lambda Architecture

Here are a few use cases of Lambda Architecture:

  • Processing and analytics of massive data sets
  • Log analytics solutions
  • Stream processing
  • Machine Learning
  • Internet of Things

How Does the Lambda Architecture Work?

Here’s how the Lambda Architecture works:

  • As new data comes in, it is sent to the batch and streaming layers for further processing.
  • The batch layer performs two main functions: storing information and processing information for creating batch views.
  • In addition to indexing and creating real-time views, the speed layer supports batch and serving layers by indexing and creating real-time views based on new, non-indexed real-time data.
  • The service layer stores output from the batch and speed layers and is responsible for indexing the batch views to provide quick access.

Benefits of Lambda Architecture

The benefits of the Lambda architecture include the following.

Reduced Latency

The serving layer indexes raw data, enabling end-users to query and analyze all historical data. Considering that batch indexing requires quite some time, there is often a long-time frame during which data is temporarily unavailable to end-users for analysis. The speed layer takes advantage of stream processing technology to immediately index recent data that is not currently queryable in the batch or the serving layers, hence reducing the time window for unanalyzable data. This adds to reducing batch/serving layer latency, i.e., the wait time needed to make data accessible for analysis.

Better Scalability

The Lambda Architecture does not define the technologies to be used but is built on distributed, scale-out technologies that may be extended simply by adding more nodes. It is horizontally scalable across all layers of the system stack. You can do this at the data source, the batch layer, the serving layer, or the speed layer.

Data Consistency

The Lambda Architecture eliminates the perils of data inconsistency which is often encountered in distributed applications. Data in distributed applications can become inconsistent due to network failures. If this happens, one copy of the data might be the most recent version, while another copy can contain old data. Since data is processed sequentially (unlike in distributed systems where data is typically handled in parallel), the data is consistent – the indexing process ensures that the batch and the speed layers have the most recent data.

Fault Tolerance

In the Lambda Architecture, all data is stored in the batch layer, and it is built on distributed systems that provide support for fault tolerance. Any failure during indexing, either in the service layer or in the speed layer, can be handled by restarting the indexing process at the batch or serving layer. That way, the speed layer can continue to index the latest data.

There are a few downsides of this architecture as well, such as the following:

  • Complexity – Extremely complex to implement because of dependence on multiple different technologies.
  • Multiple codebases – You need to maintain two different codebases for the batch and streaming layers which would make debugging and maintenance difficult.

Lambda Architecture vs Kappa Architecture

The Kappa Architecture, an elucidation of the Lambda Architecture, is used for stream data processing. The fundamental assumption of the Kappa architecture is that it allows the simultaneous execution of real-time and batch processing, particularly for analysis, with the help of a single technology stack. It is built on a streaming architecture, which stores a sequence of input data in a messaging engine such as Apache Kafka. This data is then read by a stream processing engine and converted into an analyzable format. Lastly, the data is saved in an analytics database, enabling the end-users to query it when needed.

The Kappa Architecture has similarities with the Lambda Architecture, but it doesn’t have any batch pipeline. It is comprised of only two layers – the Streaming Layer and the Serving Layer. You cannot leverage the Kappa Architecture to replace the Lambda Architecture. Rather, it is an alternative to Lambda Architecture, where you don’t need a batch layer.

When deciding between the Lambda and Kappa architectures, there are trade-offs to be considered. Lambda architecture is an excellent choice if you need an architecture capable of consistently updating the data lake and capable of developing machine learning models from your data. If you’re looking for an architecture that is more reliable when updating the data lake and efficient when it comes to developing machine learning models to predict upcoming events robustly. In that case, you should consider the Lambda architecture, which leverages the batch layer and speed layer to ensure fewer errors and increased speed.

On the other hand, if you want to deploy a big data architecture using less costly hardware and need it to operate efficiently in response to unique run-time events, you should consider the Kappa architecture.

Conclusion

The Lambda Architecture is a data processing architecture based on lambda calculus adept at handling massive volumes of data efficiently. You can implement the Lambda Architecture in the real world using Hadoop data lakes. Some of the real-world use cases where Lambda Architecture is in use are Yahoo and Netflix.

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