Thorough Analysis of Big Data Frameworks: Spark vs. Hadoop MapReduce

Choosing the right big data framework is a challenge, especially since there are many available on the market. Examining each framework from the perspective of certain needs is probably the best bet for your business, rather than comparing the pros and cons of each platform.

Our big data consulting practitioners compare two leading frameworks to address a lingering question that many people have wondered. Between Hadoop MapReduce or Spark, which framework should you choose? Let’s dive in!

Checking Out Market Situations

Taking a quick glance at the market situation, we know that both Hadoop and Spark are the flagship products in big data analytics, with Hadoop leading the market for more than 5 years. Both frameworks are open-source projects by the Apache Software Foundation. 

The user base of Hadoop amounts to 50,000+ clients according to our market research, while Spark possesses 10,000+ installations. In 2013, Spark’s popularity surpassed Hadoop in only a year. A recent growth rate for installations indicates that the trend is still ongoing. Spark correspondingly outperforms Hadoop with 47% vs. 14% (2016/2017).

Main Distinction Between Frameworks

The main difference between Hadoop MapReduce and Spark, in fact, resides in the processing approach. While Hadoop MapReduce needs to read from and write to a disk, Spark can simply do it in-memory. There is a significant difference in the speed of processing as a result from this. 

Spark potentially could be up to 100 times faster. You also have to consider the volume of data processed since that differs between the two frameworks. Hadoop MapReduce is able to operate with much larger data sets as opposed to Spark.

What tasks are each framework good at? Let’s take a closer look.

The Good About Hadoop MapReduce

Huge Data Sets – Linear Processing: 

Hadoop MapReduce permits massive amounts of data to be processed in a way where two or more processors (CPUs) handle separate parts of an overall task. This is known as parallel processing. 

Large chunks of data are broken down into smaller pieces that are processed separately on different data nodes. The results from multiple nodes automatically get gathered to return a single result. Hadoop MapReduce may outperform Spark if the resulting dataset is larger than the RAM available.

If Speed Doesn’t Matter, This Is For You: 

If the pace of processing isn’t crucial for your business, then Hadoop MapReduce is considered to be a good solution. It makes sense to suggest using Hadoop MapReduce if data processing can be done during the night hours.

The Good About Spark

Speedy Data Processing: 

Spark is faster than Hadoop MapReduce as a result from in-memory processing. Up to 100x faster for data in RAM and up to 10x faster for data in storage.

Iterative processing: 

Spark defeats Hadoop MapReduce if the assignment is to process data repeatedly. Resilient Distributed Datasets (RDDs) by Spark authorize multiple memory mapping operations. As compared to Hadoop MapReduce, it must write interim results to a disk.

Processing on-the-fly: 

Businesses should opt for Spark and its in-memory processing if it needs immediate insights.

Processing Graphs: 

For iterative computations that are common in graph processing, Spark's computational model is great! Plus, Apache Spark has GraphX, an API for computing graphs.

Machine Learning: 

Spark has a built-in library for machine learning that has out-of-the-box algorithms which run in memory. This function is called MLib. Hadoop needs a third-party to provide a machine learning library.

Combining Datasets: 

Spark can generate all combinations faster because of its speed. However, Hadoop may be better if it’s necessary to join very large data sets that require a lot of shuffling and sorting.

Practical Application Cases

Thanks to near real-time processing, Spark is likely to outperform MapReduce after examining several examples of practical applications. Let’s look at the examples.

Customer Dissection: 

To create a distinctive customer experience, businesses need to have an understanding of customer preferences. To help with this, customer behavior should be analyzed while identifying segments of customers that demonstrate similar behavior patterns.

Risk management: 

By selecting non-risky options, predicting various future scenarios can help managers make right decisions.

Fraud detection in Real-Time: 

By using machine-learning algorithms, the system would be trained on historical data where then these findings can be used to detect or predict an anomaly in real time that may indicate a potential fraud.

Industrial Big Data Analysis: 

It's all about identifying anomalies and predicting them, but these anomalies are connected to machinery breakdowns in this case. To detect pre-failure conditions, a correctly designed system collects the data from sensors.

Hmmm, What To Choose?

To determine which framework you should choose, the needs of your business will help guide you to make a final decision. Hadoop MapReduce has an advantage when it comes to linear processing huge datasets. Spark is fast, efficient and provides real-time analytics, graph processing, machine learning, and much more! One last thing that might change your mind, Spark is fully compatible with the Hadoop ecosystem.

To gain more insight on making your decision, get in contact with us.