How do I learn Hadoop and Spark

Apache Spark vs. Hadoop MapReduce: Which Big Data Framework Should You Choose?

It is a real challenge to choose the right one among several big data frameworks available in the market. A classic approach, in which the advantages and disadvantages of each platform are compared, can be of little help because companies should look at each framework from the perspective of their special needs. By receiving multiple inquiries about Hadoop MapReduce and Apache Spark, our experts in big data consulting are able to compare these two leading frameworks to answer a burning question: which option to choose - Hadoop MapReduce or Apache Spark.

A quick look at the market situation

Both Hadoop and Spark are open source projects from the Apache Software Foundation, and the two are flagships in big data analysis. Hadoop has been a leader in the big data market for more than 5 years. According to our current market study, more than 50,000 customers have used Hadoop, while Spark only has 10,000+ installations. However, Spark's popularity skyrocketed in 2013, and interest in Spark exceeded interest in Hadoop in just a year. A new growth rate for installations (2016/2017) shows that the trend is still ongoing. Spark outperforms Hadoop at 47% versus 14% accordingly.

The key differences between Hadoop MapReduce and Spark

To make the comparison fair, we compare Spark Hadoop MapReduce, because both are responsible for data processing. Basically, the main difference between them is how they process them: Spark can do this directly in memory, while Hadoop MapReduce has to read from and write to disk. As a result, the processing speed differs significantly - Spark can be up to 100 times faster. But the amount of data processed is also different: Hadoop MapReduce can work with much larger amounts of data than Spark.

Now let's take a closer look at the tasks that each framework is good for.

Hadoop MapReduce works well for:

  • Linear processing of large amounts of data. Hadoop MapReduce enables the parallel processing of large amounts of data. It first divides a large portion of the data into many smaller parts, which are processed in parallel on different data nodes, automatically collects the results from several nodes in order to combine them into a single final result. If the resulting data set is larger than the available RAM, Hadoop MapReduce can outperform Spark.
  • Cost-saving solution if quick results are not expected. Our Hadoop team thinks MapReduce is a good solution when processing speed is not critical. For example, if data processing can be done during the night, it makes sense to use Hadoop MapReduce.

Spark works well for:

  • Fast data processing. In-memory computing makes Spark faster than Hadoop MapReduce - up to 100 times for data in RAM and up to 10 times for data in memory.
  • Iterative processing. If the job is to process data over and over again - Spark knocks Hadoop MapReduce down. Resilient Distributed Datasets (RDDs) enable multiple distributed in-memory calculations, while Hadoop MapReduce has to write intermediate results to a hard disk.
  • Near real-time processing. When a company needs instant insights, it should choose Spark and its in-memory processing.
  • Graph processing. Spark's mathematical model is well suited for iterative calculations, which are typical in graph processing. And Apache Spark has GraphX ​​- an API for calculating graphs.
  • Machine learning. Spark brings MLlib - an integrated machine learning library - while Hadoop needs a third party to provide it. MLlib has out-of-the-box algorithms that are also executed in memory. There is also the option to tune and adjust them.
  • Linking records. Because of its speed, Spark can create all combinations faster. Although Hadoop may be better if you need to join together very large records that require a lot of shuffling and sorting.

Concrete application examples

We analyzed several use cases and concluded that in all of the cases listed below, Spark is likely to outperform MapReduce with its fast or even near real-time processing. Let's look at the examples.

  • Customer segmentation Analyzing customer behavior and identifying customer segments that exhibit similar patterns of behavior helps companies understand customer preferences and create unique customer experiences.
  • Risk management. Forecasting various possible scenarios can help managers make correct decisions by choosing risk-free options.
  • Real-time fraud prevention. After learning historical data using machine learning algorithms, the system can use the findings to identify or predict in real time an anomaly that could indicate possible fraud.
  • Big data analysis in production. It's also about detecting and predicting anomalies, but in this case those anomalies are related to machine failures. A properly configured system collects the data from sensors to detect pending failures.

Which framework should be chosen?

Your specific business needs should be decisive when choosing a framework. The linear processing of large amounts of data is the benefit of Hadoop MapReduce, while Spark fast performance, iterative processing, real-time analysis, graph processing, machine learning, and more offers. In many cases, Spark can outperform Hadoop MapReduce. The good news is that Spark is fully compatible with the Hadoop ecosystem and works seamlessly with Hadoop Distributed File System, Apache Hive, etc.

Big data is the next step to your success. We help you find the right approach and develop big data to its full potential.