Both Apache Spark and Hadoop are crucial computing frameworks of Big Data. Every year we see a new distributed system in the market to manage the data, but of these systems, Spark and Hadoop are the two majorly used frameworks. But how would you decide which one of them is right for you?
In this article, we will discuss the difference between Spark and Hadoop and why is it important to know if you are taking up Spark certification training. But, before we jump into the difference and analyze the strengths and weaknesses of the two computing technologies, we will start the blog with a small introduction of both the frameworks to set the right context.
What is Spark?
Apache Spark is an open-source framework for real-time data analytics in a distributed computing environment. It is based on Hadoop MapReduce, designed for fast computation. It extends the MapReduce model to efficiently use more types of computations that also includes stream processing and interactive queries. The main feature of Apache Spark is the in-memory cluster computing that increases the speed of an application.
Spark has five major components:
- Spark Core
- Spark Streaming
- Spark SQL
- GraphX
- MLib
What is Hadoop?
Hadoop is a framework that allows you first to store Big Data in a distributed environment so that it can be processed quickly and parallel. Hadoop has three major components:
- Hadoop YARN
- HDFS
- Hadoop MapReduce
Now, as you know what Spark and Hadoop are, let’s move ahead and compare Spark and Hadoop to understand the two frameworks better.
Spark vs. Hadoop
| Spark | Hadoop | |
| Category | Data Analytics engine | Big Data processing engine |
| Latency | Low latency computing | High latency computing |
| Data | Process interactively | Process in batch mode |
| Ease of Use | Easy to use, process data using high-level operators | MapReduce model is complex and needs to handle low-level APIs |
| Usage | Process real-time data | Batch processing with a large volume of data |
| Security | Less secure as compared to Hadoop | Highly secured |
| Scheduler | No external scheduler is required as it has in-memory computation | External job scheduler required |
| Performance | Fast because of in-memory processing | Slow as it processes a massive volume of data |
Key Differences between Apache Spark and Hadoop
- Hadoop is an open-source framework that completely uses a MapReduce algorithm wherein Spark is a lighting fast computing technology and extends the MapReduce model to handle more types of computations efficiently.
- Hadoop model reads and then writes from a disk that apparently slows down the entire processing speed wherein Spark reduces the number of reads and write cycles that increase the processing speed.
- Hadoop requires developers to code in each, and every operation wherein Spark is easy to program with the help of RDD (Resilient Distributed Dataset).
- Hadoop is designed to handle batch processing, and a developer can only process data in batch mode wherein Spark is efficiently designed to handle and process real-time data through Spark streaming.
- In Hadoop, storage and processing are disk-based, it uses a standard amount of memory but also requires multiple systems to distribute the disk I/O wherein Spark has in-memory processing that requires a lot of memory. Thus it requires a large amount of RAM for executing data, and due to the RAM requirements, Spark incurs more cost.
- Hadoop and Spark both provide fault tolerance but with different approaches. Hadoop uses commodity hardware, a parallel way in which HDFS ensures fault tolerance is by replicating the data wherein Spark uses RDD, these RDDs are building blocks of Spark and provide tolerance to Spark by recovering the data present in external memory systems like HDFS, HBase, and shared file system.
- Hadoop does not have an interactive mode as it is a high latency computing framework wherein Spark is a low latency computing framework, and it can process data interactively.
- Hadoop needs an external job scheduler like – Ozone to schedule the complex flow of data wherein Spark has its in-memory computation, so it has its flow scheduler and does not require an external one.
- Hadoop supports Kerberos for authentication, which is difficult to handle. Hadoop also supports LDAP (Lightweight Directory Access Protocol) for authentication, which is a third party vendor. It also supports encryption. Wherein, Spark supports authentication via a shared secret. It can integrate with HDFS, and it can also use HDFS ACLs and file-level permissions.
Conclusion
Both Spark and Hadoop are the two prominent distributed systems for processing data but also have their own limitations. Spark is an advanced cluster computing engine compared to Hadoop MapReduce as it can handle any requirement while Hadoop can only handle batch processing. On the other hand, Spark is costlier than Hadoop due to its in-memory processing, which requires a lot of RAM.
So, it all depends upon the business, their requirements, and the budget to choose the appropriate computing framework. These frameworks are wide and extensive and to learn more about Spark and its frameworks, check out online courses related to Spark training. They can help you master the essential skills of the Spark open-source framework and scala programming language along with the role of Spark in overcoming the limitations of MapReduce.