Are you choosing between Spark and Hadoop for your development project?

Question

Everything you need to know is here.

Answer 1

Both Hadoop and Spark are open source projects by Apache Software Foundation and both are the flagship products in big data analytics. Hadoop has been leading the big data market for more than 5 years. According to our recent market research, Hadoop’s installed base amounts to 50,000+ customers, while Spark boasts 10,000+ installations only.

Answer 2

Spark with MLlib proved to be nine times faster than Apache Mahout in a Hadoop disk-based environment. When you need more efficient results than what Hadoop offers, Spark is the better choice for Machine Learning.

Answer 3

Hadoop is good when you are dealing with big data sets. And the spark is meant for the data set which is already filtered and cleaned and needs processing. So medium to small data set is good with the spark. 

Hadoop is pretty much economical if deployed from the start. Whereas the spark deployment and usage takes some time to settle as well. 

Spark is faster with the data processing. For graph processing and also for the machine leaning processing. You should prefer it for the iterative processing. 

Answer 4

The choice between Spark and Hadoop depends on the specific needs of the project. Spark is faster for data processing while Hadoop is better for large-scale data storage and processing.

Answer 5

I am not choosing between Spark and Hadoop for my development project.

Spark is a more modern and advanced development tool while Hadoop is more comprehensive and efficient.