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With the advent of the internet, data and its distribution have been in the prime focus. With millions of interconnected devices capable of distributing data anywhere in the world at any time, data and its usage is likely to grow in geometric progression. Such large sets of data, big data, has to be analyzed to learn about patterns and trends associated with it.

Data analysis has taken the business world to the next level and now the focus is on creating tools that could process the data faster and better. Apache Spark and Hadoop are two technological frameworks introduced to the data world for better data analysis. Though Spark and Hadoop share some similarities, they have unique characteristics that make them suitable for a certain kind of analysis. When you learn data analytics, you will learn about these two technologies.

Hadoop

Apache Hadoop is a Java-based framework. It is an open-source framework that allows us to store and analyze big data with simple programming. It can be used for data analysis across many clusters of systems and the result is generated by a combined effort of several modules like Hadoop Common, Hadoop Distributed File System (HDFS), Hadoop YARN and Hadoop MapReduce.

Hadoop: Advantages and Disadvantages

Advantages Disadvantages
Stores data on distributed file and hence, data processing is faster and hassle-free It is more suitable for bigger files. It cannot support small files effectively.
It is flexible and allows data collection from different sources such as e-mails and social media. It features a chain form of data processing. So it is not a choice for machine learning or other solutions based on Iterative learning.
It is highly scalable The security model is low/disabled. Data can be easily accessed/stolen
It does not need any specialized system to work, so it is inexpensive It is based on the highly exploited language – Java; so easier for hackers to access sensitive data.
It replicates every block and stores it and hence, data can be recovered easily. It supports only batch processing.

Spark

This framework is based on distributed data. Its major features include in-memory computation and cluster computing. Thus, the collection of data is better and faster. Spark is capable of hybrid processing, which is a combination of various methods of data processing.

Spark: Advantages and Disadvantages

Advantages Disadvantages
Dynamic data processing capable of managing parallel apps It does not have a file management system.
It has many built-in libraries for graph analytics and machine learning algorithms. Very high memory consumption, so it is expensive

 

It is capable of performing advanced analytics that supports ‘MAP’ and ‘Reduces’, graph algorithms, SQL queries, etc. It has less number of algorithms
Can be used to run ad-hoc queries and reused for batch-processing It requires manual optimization
Enables real-time data processing It supports only time-based window criteria, not record based window criteria
Supports many languages like Python, Java, and Scala Not capable of handling data backpressure.

Spark vs Hadoop

Feature Spark Hadoop
Speed fast slow
Memory needs more memory needs less memory
Ease of use Has user-friendly APIs for languages like Python, Scala, Java, and Spark SQL Have to write a MapReduce program in Java
Graph Processing good Better than Spark
Data processing supports iterative, interactive, graph, stream and batch processing Batch processing only

Conclusion

Both Spark and Hadoop have their strength and weaknesses. Though appears to be similar, they are suitable for different functions. Choosing Spark or Hadoop Training depends on your requirement – if you are looking for a big data framework that has better compatibility, ease-of-use, and performance, go for Spark. In terms of security, architecture, and cost-effectiveness, Hadoop is better than Spark.

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