When and How to Leverage Lambda Architecture in Big Data

In the current technology environment, numerous companies are attracted by Big data. However, in the past Big data utilized data stored in the Hadoop technology and had to deal with the issue of latency. An entirely new system could be used for large amounts of data and with high speeds to solve this issue completely.

In this article, we'll attempt to make it easy to comprehend the structure, which makes it easy to use Big Data, which is nothing but Lambda Architecture. The design was developed in the hands of James Warren & Nathan Marz.

Let's look at a few facts about Lambda Architecture.

What is this Architecture all about?

For better business decisions and to gain insight, systems are designed to handle the variety, speed, and volume. This is a novel method of Big Data created to analyse, process, and insulate this data which is difficult and huge for the conventional system. The hybrid architecture aids in supporting the Big Data system in real-time and batch processing of data.

In this model, we can access both historical and new data. To get a better understanding of the movement of data in the past, information is transmitted to the data storage.

The basic idea behind this architecture is built on Lambda calculus and is known as Lambda Architecture. The architecture was specifically designed to work with unchangeable data sets, specifically for the purpose of its manipulation.

The technology can also solve the issue of arbitrary computing functions. In general, the problem can be divided into three levels:

1. Batch
2. Serving
3. Speed

Key Components of Lambda Architecture:

1. Batch Layer:

• Purpose: Stores the master dataset (immutable and append-only) and pre-computes the batch views.
• Process: Periodically processes the entire dataset and generates batch views that represent a holistic and accurate state of the data.
• Tools: Hadoop, Apache Spark.

2. Speed Layer:

• Purpose: Handles real-time data processing to produce low-latency results.
• Process: Processes data as it arrives, providing fast, approximate results or updates. It focuses on the most recent data.
• Tools: Apache Storm, Apache Flink, Apache Kafka Streams.

3. Serving Layer:

• Purpose: Provides query results by merging batch and real-time views.
• Process: Queries are executed against both the batch views and real-time views, combining the results to provide a comprehensive output.
• Tools: NoSQL databases like Apache Cassandra, HBase.

The image below will provide an understanding of the layers described above. Similar to Hadoop, this batch layer is referred to by the name of "Data Lake". It also functions as an archive for the past to store all data that has been fed into it. It also facilitates batch processing of data and aids in the generation of analysis results.

To speed up the steaming and queuing of data, the speed layer is put into the picture. The function of this layer is to conduct analytic calculations on real-time data. The speed layer shares many features with the batch layer in that it can also calculate analogous analytics. The only difference is that the analysis is performed on data that is recent. Based on the speed of the data, the data could be as little as an hour outdated.

The serving layer serves as the layer which combines the results from the two layers to produce the final.

When data is transferred to the system, it is separated into speed and batch layers. The queries are answered through the integration of real-time view and batch view.

While the batch layer performs two crucial roles.

1. Management of the master data set.
2. Batch View pre-computation.

The output of the batch layer is presented in the pattern of batch views, whereas the speed layer output is displayed in the form of live-time view patterns. The results are then passed to the layer serving. This indexing occurs in the serving layer so that requests can be made with low latency and in-demand.

The speed layer, also known as the stream layer, is responsible for data that was not presented through batch views because due to the delay that the batch layers suffer. The layer only handles the most recent data, so it can provide an entire view by creating real-time views.

In essence, we could say that in the Lambda structure, the data pipeline is separated into layers, and each is responsible for a specific task. In each layer, the provision is available to select the appropriate technology. For instance, in the Speed layer, one could select Apache Storm, Apache Spark streaming, or other technology.

In the lambda structure, errors can be quickly rectified, as that it is necessary to return to the original version of the data. This can be accomplished because, in this case, data is never changed, but it is added in the event that the programmer inputs inaccurate data. They can delete and then recompute the data.

Data Lake:

A Data Lake is a centralized repository that allows organizations to store vast amounts of structured, semi-structured, and unstructured data in its raw form. Unlike traditional databases, which require data to be processed and organized before storage, a data lake accepts data as-is, making it a highly flexible and scalable solution for modern data management.

Key features of a data lake include its ability to handle diverse data types, such as log files, multimedia, sensor data, and social media feeds, from multiple sources. This flexibility enables organizations to perform a variety of analytics, from simple queries to complex machine learning tasks, without the need to move data to different systems.

Data lakes are often built on distributed storage systems like Hadoop or cloud platforms, offering scalability and cost efficiency. They serve as the foundation for data-driven initiatives, supporting real-time analytics, batch processing, and advanced data science applications. However, without proper governance, data lakes can become disorganized, leading to a "data swamp" where valuable insights are hard to retrieve.

Applications of Lambda Architecture:

Lambda Architecture is widely applied in various industries and use cases where the need to process both real-time and batch data efficiently is crucial. Here are some key applications:

1. Real-Time Analytics and Reporting:

• Use Case: E-commerce platforms, social media, and financial markets often require up-to-the-minute analytics to monitor user behavior, sales trends, or stock prices.
• How Lambda Helps: The speed layer provides immediate insights by processing data as it arrives, while the batch layer ensures long-term accuracy and historical analysis.

2. Fraud Detection:

• Use Case: Banks and financial institutions need to detect and prevent fraudulent transactions in real-time to protect customers and mitigate losses.
• How Lambda Helps: The speed layer identifies suspicious activity quickly, enabling immediate alerts, while the batch layer provides a more detailed analysis to refine fraud detection algorithms.

3. Personalization and Recommendation Engines:

• Use Case: Online retailers, streaming services, and content platforms personalize user experiences based on their behavior and preferences.
• How Lambda Helps: The speed layer delivers personalized recommendations based on real-time interactions, and the batch layer processes historical data to improve recommendation accuracy.

4. Monitoring and Anomaly Detection:

• Use Case: IT infrastructure, manufacturing processes, and network security systems require continuous monitoring to detect anomalies or failures.
• How Lambda Helps: The speed layer allows for real-time monitoring and immediate response to anomalies, while the batch layer provides a comprehensive analysis for root cause determination and system optimization.

5. IoT Data Processing:

• Use Case: Connected devices in smart homes, industrial automation, and healthcare generate continuous streams of data that need to be processed in real-time.
• How Lambda Helps: The speed layer processes sensor data as it is generated for instant decision-making, while the batch layer aggregates data over time for trend analysis and predictive maintenance.

Advantages of Lambda Architecture

Lambda architecture offers a variety of advantages, but the most significant ones are impermanence, fault tolerance, and it is used to perform re-computation or precomputation.

The most significant advantages of this architecture are described in the following paragraphs:

• In this model, the data is stored in its raw format. This facilitates the development of new user cases analytics, as well as a brand new data processing algorithm by generating a basic batch and speed view. This is an enormous advantage over traditional data warehouses. In the past, data schemas had to be updated to accommodate new applications and was a lengthy process.
• Re-computation is an additional feature of this design. This fault tolerance is able to be fixed without any difficulty. If a data lake has massive amounts of data, the likelihood is that corruption and loss of data can occur, but this can't be prevented. This design allows for rollback, flushing of data, and re-computation of data to rectify these errors.
• The architecture places the greatest importance on keeping the input data in the same form. Data transformation for models is an additional crucial aspect of the structure. This allows for tracking MapReduce workflows. MapReduce performs batch processing on the entire data. The data is debugged separately in each step. The main issue that data processing in-stream has confronted is reprocessing issues. The input data drives output using this method.

In short, the benefits of this design are:

• Human error tolerance
• Tolerance in the case of hardware defects.

Disadvantages of Lambda Architecture

Selecting a lambda framework for an enterprise that is preparing a data lake can have some negatives, too, especially in the event that certain aspects aren't taken into consideration. Certain of these points are listed in the following paragraphs:

• The various layers of this structure can make it more complicated. The synchronization between layers could be costly. Therefore, it must handle in a prudent way.
• Maintenance and support become difficult due to the different dispersed layers, such as speed and batch.
• Many technologies are emerging that can aid in the building of Lambda architecture. However, finding those who are proficient in these new technologies isn't easy.
• It's not easy to implement this model for open-source technology, and the problem gets worse when it is implemented on the cloud.
• Maintaining the code that is part of the architecture can be a challenge since it is required to generate similar results across a distributed environment.
• It's also difficult to code in Big Data frameworks such as Hadoop and Storm.

A Unified Approach to Lambda Architecture

As mentioned above, one of the major drawbacks of the Lambda structure is its complexity. It's ongoing troublesome maintenance and installation because one must sync two systems that are distributed. To overcome these issues, there are three different methods that we will discuss below:

• Flexible frameworks and the adoption of a pure streaming model. In this case, Apache, Samza can be an ideal choice. It is an extensible distributed streaming layer, which can be used to perform batch processing.
• A different approach to the discussion above could also be considered. The choice of flexible batches is an option. Make sure to select batches of a small size so that it is near real-time batches. In this case, Apache Stark or Storm's Trident could be used.
• Combining real-time and batch processing with stack technology could be an alternative method. In this case, Lambda Loop and SummingBird could be viable options. The result is merged in SummingBird, both instantaneous and batch data since it's a hybrid system. In Lambda Loop, the same method is used.

The image below will help you gain a better understanding of the issues mentioned above.

The unification approach tackles the volume and velocity issues of Big Data as it utilizes a hybrid computing model. The model blends immediate and batch data with ease.

An Outline of the Architecture

Big data systems typically operate on raw and partially structured data. Organizations today require systems that have the capacity to process batch as well as real-time data. Lambda architecture is equipped to handle both processes. In the meantime, it is able to build impermanence in the process.

The structure follows a solid set of guidelines, and it is an agonist of technology. Any technology can be integrated into it to accomplish the task since it's made up of different layers. Ready cloud components are accessible and can be used using the lambda structure.

The architecture could be described as a pluggable system that can be used when a process is needed. Many data sources are plugged in or out based on the requirements.

Real-Time Working Examples:

Lambda architecture has proven itself many applications. A few of the examples that are working are listed in the following:

• Twitter and Groupon have numerous applications. Lambda architecture can be used to comprehend the meaning of tweets and is used to perform sentiment analysis.
• Crashlytics: Here, it is concerned specifically with mobile analysis that is used to generate significant analytical outcomes.
• Stack overflow is an extremely well-known and popular forum that has huge users. It is a place where people can ask questions and solutions. In this forum, batch views are utilized to analyse the results of the voting.

Conclusion

Since the beginning of time, Big data technology has gained popularity. But when it comes to the requirements of a company such as Google or Facebook, the technology in place was not suited to the demands of the business. To satisfy their requirements, a standardized and flexible architecture was needed, leading to the creation of Lambda architecture.

Following the introduction of this model, the proper planning must be taken to transfer the data into Data Lake. Since the architecture focuses on analytics, one could utilize the traditional transactional database to transfer the data into the cluster.

Each year, more companies are moving to Big Data.