Since the evolution of a wide variety of devices, the volume of data being captured has grown. The IoT is emerging as a key enabler of our digital future, and global spending on IoT and Smart devices will increase tremendously in the next few years. There has been a widespread adoption of different types of technologies in industries like banking and investment services, education, healthcare, insurance, and many others. What this means is that data is growing and the need to store, manage and process it has become crucial.
As data grows, companies will face two major problems: storage and processing. Traditional RDBMS is not an ideal and scalable approach to handle such large volumes (terabytes or petabytes) of data. Such data needs to be processed so that it can be turned into valuable information that can help organizations make crucial futuristic decisions. This is where Big Data technologies like Hadoop and Spark come in and help organizations solve these problems.
Hadoop is a highly scalable open-source framework, which allows processing and storage of structured and unstructured complex data (big data) across clusters of computers. Its unique storage mechanism over distributed file system maps data wherever it is located on a cluster. The speciality of Hadoop is that it can scale from one server to hundreds of servers and can still perform well. Check out our article on Hadoop and HDFS for more information.
Hadoop internally uses MapReduce mechanism for processing large datasets; however, this approach handles data processing in batches where the data is divided into small sets and shared across different computers in the cluster for processing. Batch processing is time consuming as it happens on the disk and it is not ideal when you want real-time information. This is where Spark can help process data in-memory 100x faster than Hadoop. The adoption of Spark in the industry has increased due to its ecosystem, high speed data-stream processing, fault-tolerance, and the various advantages it offers.
Both Hadoop and Spark are the most preferred technologies currently in the big data industry. Hadoop is famous for its MapReduce mechanism, reliability and batch processing. Whereas Spark is famous for its fault-tolerance, fast and near real-time processing, but there can be scenarios where both batch and real-time data processing are required. Imagine building a data pipeline, which ingests data from different sources. The pipeline is divided into two different processes. One that processes data in real-time and the other one in batches. This is where Lambda architecture helps solve problems encountered in such scenarios. Let’s continue and understand what Lambda architecture is, its implementation and various advantages it offers.
The term Lambda in the word Lambda Architecture comes from the mathematical lambda symbol. The picture of the Lambda architecture shown below represents the tilted lambda symbol. The application of this architecture is not specific to Spark or Hadoop. It is a generic architecture that can be applied with any set of technologies. In this article, we will see how Lambda architecture can be used with Spark and other big data technologies.
Lambda architecture can be divided into different layers discussed in detail below:
In this layer, data that comes from a data warehouse or a central data source and is divided into small batches and sent for processing. One can easily rely on Hadoop for this layer and can use HDFS for storing the data and MapReduce for batch processing. Batch processing can be slow and can take up to several hours to complete; however, this layer is extremely reliable for processing large datasets and get detailed insights. Once a batch is processed, batch views are created and pushed to Service layer for storage and querying.
This layer accepts continuous streams of data and can use technologies like Apache Spark or Storm for real-time and near real-time processing. The data that is pushed to this layer is quickly processed within milliseconds to seconds and the corresponding real-time views are pushed to the Service layer. This layer not only helps in processing data at a faster speed, but also perform real-time analytics.
Data from both batch and speed layer are pushed into this layer for indexing. Database technologies like Cassandra, HIVE, MongoDB can be used to store and manage the indexed data. The data stored in this layer can be further queried by any inhouse application for business analytics.
This section describes the entire process shown in the diagram above.
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