Quality data is key in decision-making. But, demanding big data to be processed in seconds creates a lot of pressure on data engineering systems and can impact the accuracy of the processed data. Large datasets are usually processed by batch processing pipelines which take more time because they thoroughly process data in intervals while data streaming pipelines process data in small packets and deliver processed data in real-time.
In 2015, Nathan Marz and James Warren developed a pattern called the Lambda architecture that takes a parallel approach by combining real-time processing and batch processing. This means that business analysts and stock traders can get instant processed data from the speed layer (data streaming layer) and get more complete data from the batch layer. The information retrieved from the batch layer will override the data retrieved from the speed layer because the batch layer produces more complete processed data.
In this article, you will learn how the Lambda architecture works and how you can leverage it to improve data streaming.
What is Lambda Architecture?
The Lambda architecture uses big data frameworks to prevent human errors by making data in the batch layer immutable and alleviates high latency by introducing real-time data processing in the speed layer. This architecture enables you to be event-driven when handling big datasets as it processes large data sets in the batch layer and gives you real-time data in the speed layer. Apache Kafka is used to streamline data to the batch and speed layer.
The implementation of efficient data streaming has been made possible by the Lambda architecture which enables data engineers to deploy processing models in the speed layer and rebuild the models as markets fluctuate in the batch layer. You will learn the lambda architecture layers and components in the next sections.
The 3 Layers of the Lambda Architecture
The Lambda architecture is derived from a combination of the batch processes and real time data processes. The lambda architecture has 3 different layers:
1. The Batch Layer
This layer receives data through the master dataset in an append-only format from different sources. The batch layer processes big data sets in intervals to create batch views that will be stored by the serving layer. The data in this layer is immutable. Immutability and receiving data in append-only format is what makes the Lambda architecture fault tolerant and prevents data loss.
The batch layer does not use incremental algorithms rather it uses re-computation algorithms. This layer produces complete data because the machine learning algorithms are able to train models since the batch layer takes more time to process large datasets.
2. The Speed or Streaming Layer
The speed layer processes data using data streaming processes and tools such as Apache Kafka; its goal is to deliver data in real-time. It favors low-latency over throughput. The speed layer focuses on filling the gaps left by the batch layer. This layer uses complex incremental algorithms and computation.
3. The Serving Layer
This layer queues batch views that have been prepared by the batch layer and then indexes them. The serving layer’s goal is to make the data queryable in a very short period of time. The server layer stores the output and merges the batch layer output with the speed layer output.
Here is a list of tools used in the Lambda architecture:
Apache Hadoop is used to store data and create distributed clusters.
Apache Kafka is used for data streaming in the speed layer.
Hadoop Distributed File System (HDFS) is used for managing immutable data in the batch layer.
Apache Spark is used for data streaming, graph processing, and data batch process.
Apache Cassandra is used to store real-time views.
Apache Storm is used for the speed layer tasks.
Apache HBASE is used for the serving layer tasks.
The Benefits and Disadvantages of Using Lambda Architecture
The Lambda architecture manages to reduce latency by indexing recent data in the speed layer. This makes it possible for critical data to be delivered in time whenever needed. It is highly scalable because it does not dictate the technologies and tools you use. In addition, you can add more nodes on different layers because the Lambda architecture has decoupled layers.
The good part about this architecture is that if the batch layer fails, the speed layer will process the recent data while you re-run the batch layer.
The main problem with the Lambda architecture is that it requires two codebases for the batch layer and the stream layer. This just adds more complexity to the application design and implementation. Also, complex codebases are hard to maintain and debug.
Unlock the Power of LAMBDA
Sending and receiving data in real-time is crucial for tasks such as stock market analysis, system log analysis, and business analytics to predict rapidly changing factors. But, batch processing is also vital to produce precise and complete data required for data analysis. That’s why LAMBDA architecture, which combines batch processing and data streaming, works so well.
The StreamSets Platform is built to leverage the optimizations of your LAMBDA architecture and support a low/no code environment that supports batch, streaming, CDC, and ETL/ELT. StreamSets helps unlock all the power of your LAMBDA architecture.
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