What is the difference between Spark vs Hadoop?

In the context of data science, you can assess the suitability of Apache versus Hadoop to process the financial transaction data by using the several points which are given below:-

Data processing needs

You can determine the nature of the data processing task required for analyzing the financial transaction data. If the task includes real-time processing then you can choose Spark as it can provide an advantage over Hadoop in the context of batch processing.

Scalability

You can evaluate the scalability requirements of the project. Both Hadoop and Spark can distribute processing, however, the ability of Spark to catch the data in memory and perform computations in parallel can lead you to Faster processing.

Ease of use and development

You can try to assess the ease of use and development of your team. Spark is famous for its user-friendly interface as compared to Hadoop. However, if your team is familiar with Hadoop then you should choose Hadoop.

Ecosystem and tooling

You can also consider the availability of ecosystem tools and libraries for your specific use case. Both Spark and Hadoop have rich ecosystems however the ecosystem of Sparky has grown rapidly in recent years.

Resource utilisation

You can try to consider the hardware resources available for the project. Spark demands more memory as compared to Hadoop. If the memory is limited and if there is a concern about cost then Hadoop’s disk-based processing can be more economical.

Based on these factors, you can make an informed decision about whether to use Apache or Hadoop for processing financial transaction data. Here is a high level comparison of coding given:-

Apache Spark

Apache hadoop

// Apache Hadoop (using Hadoop MapReduce with Java)

// Example code for calculating total transaction volume

// Define Mapper class

Public class TransactionMapper extends Mapper {

    Public void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {

        String[] parts = value.toString().split(“,”);

        Context.write(new Text(“total_volume”), new LongWritable(Long.parseLong(parts[2])));

    }

}

// Define Reducer class

Public class TransactionReducer extends Reducer {

    Public void reduce(Text key, Iterable values, Context context) throws IOException, InterruptedException {

        Long sum = 0;

        For (LongWritable value : values) {

            Sum += value.get();

        }

        Context.write(key, new LongWritable(sum));

    }

}