What is MapReduce?
The world generated an incredible 79 zettabytes of data in 2021, and this number could double by 2025. As a budding data engineer or data scientist, you’ll use large volumes of data to create data infrastructures or perform analysis. But how can you process all this data?
MapReduce can help. This technology splits data into smaller parts and sends them to different computers, speeding up processing times. Get the answer to the question, “What is MapReduce?” below and learn how The Data Incubator can help you become a more successful data professional.
MapReduce is a programming model or algorithm for writing applications that access data in the Apache Hadoop File System (HDFS). It is part of the Hadoop framework. Jeffrey Dean and Sanjay Ghemawat created MapReduce for Google in 2004. What makes MapReduce so effective is its ability to split petabytes of data into smaller parts and then process them on multiple computer servers simultaneously — known as in-parallel data processing. It then collects data from those servers and integrates it into a single data set.
MapReduce can be more beneficial for data processing than traditional technologies like relational database management systems (RDMSs), which process and store data in a centralized server. By using several servers, users can process large volumes of data quickly and then use that data for business intelligence.
Jeffrey Dean and Sanjay Ghemawat created MapReduce for Google in 2004.
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How MapReduce Works
MapReduce works like this:
- It takes data inputted into HDFS and other distributed file systems, breaks it down into smaller parts, and assigns each of these parts to a different node in a Hadoop cluster.
- MapReduce’s Map function takes that inputted data and turns it into key-value pairs (two related data elements) before processing the data. Then, the Map function creates another set of key-value pairs to use as output data.
- The Reduce function turns the output data into input data and turns its key-value pairs into a smaller set of key-value pairs.
While this might sound complicated, MapReduce does all the above without human intervention, making it easy to process data. However, you will still need to configure MapReduce to function correctly.
Pros of MapReduce
Here are some of the advantages of MapReduce:
MapReduce processes large volumes of data more quickly than traditional models. Many businesses use it to process unstructured or semi-structured data rather than relying on RDBMS and other slower technologies.
MapReduce utilizes HDFS and HBase to improve security when moving data to different computer systems. Only approved users can access and manipulate data stored in MapReduce.
It improves data availability
MapReduce sends data to individual nodes and other nodes in a network. In the event an individual node fails, information is still available in the network’s other nodes, which improves data availability.
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Cons of MapReduce
Here are a couple of disadvantages of MapReduce:
Steep learning curve
Learning MapReduce can be challenging because it’s very difficult to program. Using tools like Apache Pig can make this process simpler, and several Hadoop tools can automate MapReduce without the need for programming.
No real-time processing
MapReduce doesn’t allow for real-time data processing because it processes information in batches. This method helps the model deal with large volumes of data, but it means you will experience a slight time lag when accessing your data.
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