Apache Spark VS Apache Hadoop
(Which is the best fit for you?)
Hey guys,
In this article I am going to discuss Apache Spark vs Apache Hadoop and when to choose what? Big data analysis has become the secret to success in many businesses. Businesses are constantly attempting to get more insights from every piece of data they collect, which has resulted in a desire to use big data frameworks that can provide the most significant information for the business. Several organizations are switching to Apache Spark and Hadoop to tackle these problems but choosing the correct one can sometimes a bit difficult. I have authored this article to help you in making this decision super simple. Presently, spark and Hadoop are the two most popular big data processing frameworks in the market. This article will explore Apache Hadoop versus Apache Spark and will help you to decide which one is the right choice for your business. So, let us dive deep into the discussion. The agenda for the discussion is as follows:
· What is Apache Hadoop?
· What is Apache Spark?
· Spark VS Hadoop
· Which framework is the best fit for you?
What is Apache Hadoop?
Hadoop is a sophisticated platform that employs the MapReduce architecture to divide data into blocks, which are then distributed over a cluster to be processed across machines and combined. Hadoop is a notable framework for its fault tolerance since it replicates data across clusters and then gets data from healthy sets to recreate data that has been lost or corrupted due to hardware failure. It is based on on-disc processing and is the most suitable for batch processing.
What is Apache Spark?
Spark is a flexible framework that processes massive volumes of data by distributing tasks over several nodes, but it does so much quicker than Hadoop since it leverages in memory (RAM) data processing to do so. The Spark engine is famous as the Swiss army knife of frameworks for one simple reason. Use of Spark for batch processing and real-time stream processing, is increasing fast.
Spark VS Hadoop
I will explore spark vs Hadoop on the following parameters:
· Performance of the framework
· Implementation cost
· Easy to maintain and use
Performance of the framework
Since it is not concerned with input-output issues every time it executes a component of a MapReduce operation, Spark runs 100 times faster in memory and ten times faster on disc. However, you need to have more RAM for in memory processing capabilities which may incur more cost. Hoop utilizes low-cost machines or commodity hardware which lacks the cyclical relationship between MapReduce phases, but Spark’s DAGs optimize between degrees better. If speed is not a primary need, choose MapReduce since it can handle massive data without consuming too much memory. Hadoop stores data before processing it. Its primary intention was to process data obtained from a variety of sources.
Implementation cost
Both platforms are free to use and open source. However, Spark requires a considerable amount of RAM to run, whereas Hadoop requires more disc capacity to function. In the near run, this makes Hadoop appear less expensive. However, when optimized for computation time, Spark completes the identical jobs far quicker than Hadoop. This is crucial since you pay for computers to browse the cloud.
Easy to maintain and use
Hadoop MapReduce is recognized to be more difficult to programme and lacks an interactive mode. With its pre-built APIs (Application Programming Interface) for Java, Scala, and Python, Spark features an interactive interface and significantly simpler building blocks, making it easier to develop user-defined functions. So, you can choose one of the platforms as per your expertise and business requirements which one is the best fit for you.
Which framework is the best fit for you?
These frameworks are widely used in combination and operate quite well when used in tandem. However, there are definite characteristics in which one is superior to the other. Spark is capable of efficiently handling real-time and bath data processing. Hadoop MapReduce is great for batch processing, but it falls short of the mark to real-time processing. Instead of separating your jobs over many platforms like in Hadoop, Spark deployment provides a one-size-fits-all platform.
If you are contemplating whether Apache Spark is right choice for you, consider the following scenarios:
· Analysis in real-time
· Instantaneous outcomes from the analysis
· Repetitive operations
· Algorithms for Machine Learning
Hadoop is better suited for you, consider the following scenarios:
· Archival data analysis
· Operation on commodity hardware
· Data analysis is not hurried.
· Linear data processing
Conclusion
Although, I have discussed the differences between Hadoop and Spark, as well as why Spark is quicker than Hadoop, a lot will rely on your project’s needs and the expertise and knowledge you have. At first sight, it may appear like Spark is a newer, better version of Hadoop, but that is not the case, and it really is a promising idea to do in-depth analysis based on your requirement and expertise into Spark versus Hadoop to see which is a good fit for you. In terms of skill, if your team is unfamiliar with Hadoop or has no prior experience with it, it may be tough to learn due to its significant reliance on Java, which is difficult to programme with. If that is the case, Spark is a superior option since it is easy to code and has an interactive capability that allows you to get immediate response after running commands.
Although I compared Hadoop and Spark, a great circumstance is when you do not really have to pick between the two and can utilize both. You may have observed that these frameworks have a healthy connection. Spark is Hadoop-compatible and works well with Hadoop’s distributed file system. As a result, if you choose one, think about combining the two for a more thorough study. You will be able to receive faster insights, save expenses, and avoid duplication by combining the two frameworks. Many large businesses, such as Amazon, Yahoo, and eBay, are already employing the two combinations, and it worked well for them based on their degree of development.
On wrapping up notes, feel free to share your comments. Your claps and comments will help me to present contents in better way. See you next week.
