Comparing Apache Spark and Hadoop MapReduce | iCert Global

Hadoop is a robust software program that stores and processes huge amounts of data by splitting it into many computers. This breaks the Big Data into small chunks, making it easier to handle.

HDFS (Hadoop Distributed File System)

HDFS is where data is housed. Imagine it as one large storage building; in reality, however, the data is divided and distributed across numerous computers. The computers work together, with one computer controlling the others. The master is referred to as the NameNode and is the one in charge, with the rest of the computers being referred to as DataNodes.

NameNode

The NameNode is the boss computer. It keeps track of where all the data is saved, the size of the files, and who can access them. If any changes happen, like if a file is deleted, the NameNode notes it down right away. It also checks on the DataNodes regularly to make sure they are working properly.

DataNode

DataNodes are the helper computers. They actually store the real data. When someone wants to read or write data, the DataNodes do the job. They also follow orders from the NameNode to copy, delete, or create data blocks when needed.

YARN helps run and manage tasks in a big data system. It gives the right amount of computer power to make sure everything works well. YARN has two main parts: the ResourceManager and the NodeManager.

ResourceManager

This serves as the central boss for the entire group of computers (a cluster). This operates on the master computer. It is responsible for resource management and determining what has to run.

NodeManager

This component runs on every helper machine (a node). It controls tiny units called containers where jobs execute. It monitors how much computer power it consumes, identifies issues, and keeps records (logs). The NodeManager also communicates with the ResourceManager periodically to remain in touch.

Introduction to Apache Spark

Apache Spark is a powerful tool that can quickly look at and understand data, even while the data is still coming in. It runs on several computers at the same time and uses memory to increase the data processing speed. Since it uses memory instead of slow hard drives, it offers incredible speed. Nonetheless, in order to operate at its best, it needs robust computers.

A unique feature of Spark is called RDD, which is Resilient Distributed Dataset. RDD is a new data structure, a mechanism to store data in Spark. Once an RDD is created, it is immutable, broken into pieces so that different computers in a cluster can work on it independently. RDDs are general-purpose, can hold any kind of data, from numbers and words to even user-defined objects.

Spark Core

Spark Core is the foundational element of Apache Spark, allowing for processing big data on many computers at once. Spark Core also manages computer memory, recovery from failures, scheduling and execution of jobs, and interaction with storage systems.

Spark Streaming

Spark Streaming allows Spark to handle data as it comes in real time, such as a video feed or sensor alerts. It handles large amounts of live data in an efficient and reliable way.

Spark SQL

Spark SQL enables the use of SQL, a data manipulation language, in the Spark environment. If you are already familiar with using some tools such as MySQL or some other database, you are familiar with Spark SQL. It helps you to use tables and execute queries in a very simple manner.

GraphX

GraphX is a Spark module that enables you to deal with graphs. A graph consists of points, or nodes, and lines, or edges, that join them together. A map or social network would be an example of a graph. GraphX enables you to examine such networks with Spark.

MLlib (Machine Learning Library)

MLlib is Spark's machine learning tool that allows you to develop smart programs that can learn from data and predict future occurrences. It can be used to perform a wide variety of tasks, including finding patterns or predicting future trends.

Spark works well with many languages like Python, Java, Scala, R, and SQL. Additionally, it works perfectly with other tools, allowing you to create sturdy data projects that take advantage of its many features like MLlib, GraphX, SQL, and Streaming.

A Simple Comparison between Hadoop and Apache Spark

1. Performance and Speed

Spark beats Hadoop by holding most of its data in memory (RAM) while processing. Even when data is so much that it becomes overwhelming, it still has the ability to make use of disk space. Spark is most suitable for operations that require immediate results, like credit card checks, machine learning, security checks, and the operation of smart devices like sensors.

Hadoop collects enormous data from various sources and scatters them across many computers. Hadoop uses a mechanism known as MapReduce to process the data in batches, going through chunks of data over a period of time. Because of this, it is slower than Spark and is not as appropriate for real-time use.

2. Ease of Use

Spark is simple to use and works with many languages like Java, Python, Scala, and SQL. It also provides developers with the ability to test and see results immediately with an interactive shell, making it easier to develop with Spark.

Hadoop can easily get data from tools like Sqoop and Flume. It can also be readily integrated with other software such as Hive and Pig. For SQL-savvy individuals, Hive is a blessing because it lets them use big data with the familiar commands.

3. Cost

Both Hadoop and Spark are open-source. They are free software. The main cost is in terms of computers and servers required to run them.

• Hadoop holds data on disk, thereby requiring additional room and additional machines to read and write data in a timely fashion.

• Spark employs memory (RAM), which is more expensive, but it requires less machine since it is quicker. Hence, in the long run, it can prove to be cheaper, particularly when less system is required to accomplish the task.

4. Data Processing

There are two main types of data processing:

• Batch Processing – It is collecting massive data initially and processing it afterwards. It is suitable for analyzing something that already occurred in the past. Example: Finding the average income of a nation over the past 10 years.

• Stream Processing – This involves processing data when it comes in. It is helpful if updates are required quickly or decisions have to be made rapidly. Example: Fraud detection in a credit card transaction.

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Conclusion

Apache Spark is faster and better for real-time data, while Hadoop works well for large batch processing. Spark is easier to use and supports more features. Both are important tools for handling big data, depending on your needs.

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