Lambda architecture is a system designed to efficiently process large-scale data by addressing both batch and real-time data processing requirements concurrently. Adopting the Lambda architecture is essential for organizations that deal with big data to keep up with the challenges of today’s data-driven world.
Lambda architecture is composed of three layers, namely the batch layer, the serving layer and the speed layer. The batch layer is responsible for collecting historical data and storing it in a distributed file system like Hadoop Distributed File System (HDFS) or Amazon S3. The servicing layer indexes the data and provides ad-hoc querying capabilities using solutions like Apache HBase or Cassandra. Lastly, the speed layer processes real-time data using technologies like Apache Spark Streaming or Storm.
The batch layer is responsible for giving the heavy-lifting of data processing. In this layer, analytical queries run on static and immutable data. The batch layer guarantees data completeness and consistency. It is high latency since it takes a long time to process data. The scalability of this layer is horizontal; meaning more machines can be added to increase its processing power.
The serving layer is responsible for taking the query results obtained in the batch layer. It ensures query latency is low and provides fast retrieval of big-data queries. Query results are stored in a NoSQL database, which is then optimized for quick reads. This layer scales horizontally as well; adding more machines will increase the serving layer’s query-handling power.
The speed layer processes real-time data, making it available for querying as fast as the data comes in. This layer minimizes the delay between event occurrence and processing. It guarantees the most recent data is available for queries. Processing is carried out only for data within a given time frame, unlike the batch layer. The scalability of this layer is vertical, where processing power is increased by adding more CPUs or RAM.
The Lambda architecture has numerous advantages. Firstly, it is fault-tolerant since data is stored in different layers and different storage locations. Secondly, it’s flexible as it allows querying in real-time and analyses of historical batch data. Another advantage is that it accommodates for larger and more complex data sets. Lastly, it ensures the delivery of most recent data for real-time applications, reducing delays.
In conclusion, as big data becomes more widespread, the Lambda architecture is an indispensable solution for processing large volumes of unstructured data. It improves processing speed and accuracy and provides the most recent data essential for real-time applications. Organizations looking to adopt the Lambda architecture can start with open-source technologies like Hadoop, Spark, and Storm, which can integrate with numerous data storage and management solutions.
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