Hadoop vs. Conventional RDBMS – The Major Differences to Note Before Choosing Oneby Elena T. Manager
Hadoop is in fact not a type of database, but basically, a well-distributed file system that can store large data sets and process them across a computer cluster. There are many major differences between conventional RDBMS (relational database management systems) and Hadoop.
Hadoop is an advanced Big Data application, which has two core components as:
- Hadoop Distributed File System (HDFS), and
HDFS is primarily a storage layer which can store huge volume of data. MapReduce is basically a programming model to processes large sets of data and split them into various data blocks. Such data blocks are further distributed across different nodes of machines inside a computer cluster.
Unlike this, the traditional RDBMS functions as a structured database itself which stores data in tables with multiple rows and columns. Structured Query Language or SQL is used to access the data or update data in the tables.
Now as you understood the basic difference between these two, let’s further look into some important working differences between RDBMS and Hadoop.
Differences between RDBMS and Hadoop
RDBMS may fail or imply a huge cost with the need to store or process a large amount of data or big data.
- Hadoop Distributed File System – HDFS was developed based on the paper published by Google. The paper discussed a file system which is broken into different blocks to be stored in the nodes across a distributed architecture.
- Map Reduce – This framework helps the programs to perform parallel computation using key-value pairs. The map can take input data to convert into data sets for easy computation. Map output is again consumed by reducing task to give the desired result.
- Yarn - It is another unique Resource Negotiator which can be used to schedule jobs and cluster management. This was an add-on in Hadoop 2.
RDBMS or relational database management system is a data management concept, which is based on a relational data model established in the 1970s by Edgar F. Codd. Many existing database management applications like MySQL, Oracle server, Microsoft SQL, and IBM DB2, etc. are based on the RDBMS model. Data is stored in RDBMS in tables as rows and columns. All database tables are a collection of related data and its objects. Each table has a primary key and normalization acts as a critical element of RDBMS.
- Tables - A table is a record of data stored in a vertical and horizontal grid format. The table consists of fields like for example name, address, and product, etc.
- Rows – Rows represent horizontal values in the tables.
- Columns – Table columns are stored horizontally and each column contains a field of data.
- Keys – Keys are identification tags for the data in each row.
As we can see above, RDBMS and Hadoop have a different approach to store, process, and retrieve data. Hadoop is fresh in the market, but RDBMS exists for more than five decades. As time passed, data volume is growing, and the nature of data is also diversifying with increased demands for data management and analysis.
As RemoteDBA.com points out, storing and effectively processing these huge amounts of data quickly is vital in the current-day industries. Conventional RDBMS is ideal for relational data as it will work well in the form of tables. The major feature of relational databases is its ability to use tables for storing data alongside maintaining and enforcing various data relationships.
Hadoop vs. RDBMS in user perspective
#1. Data Volume
The database can store and process a certain amount of data. RDBMS is found to be an ideal solution if the data volume is comparatively low. It may fit in the best if the data size is in Gigabytes. However, when data size is huge as in case of big data nowadays (in Terabytes or Petabytes), the RDBMS may pathetically fail to get the desired results. In this case; however, Hadoop will work the best as it can store a large volume of data and process them quickly and easily compared to conventional RDBMS.
Hadoop Distributed File System, Hadoop MapReduce, and Hadoop YARN enhances the ability of these platforms to accommodate programming models to process huge data sets and to better manage the computing resources in bit computer clusters.
On the other hand, traditional RDBMS has ACID properties which are meant to ensure Atomicity, Consistency, Isolation, and Durability. These elements are important in order to ensure the accuracy and integrity of data while the database transactions take place. Many of these transactions now are related to finance and banking systems, manufacturing, telecommunication, e-commerce, education, etc.
Throughput refers to the total data volume processed in a given time period to optimize the output. Traditional RDBMS may fail to gain a higher throughput when compared to the Hadoop Framework which is meant for it. This is one major reason why Hadoop is largely preferred by the modern-day corporate over RDBMS.
#4. Data Variety
Data variety refers to various types of data to be stored and processed. Data can be in any format as structured, unstructured, and semi-structured, etc.
When it comes to processing a variety of data, Hadoop has an inbuilt ability to handle all types of data. Hadoop is now widely used to process a huge volume of unstructured data also. On the other hand, traditional RDBMS can only be managed structured and, up to an extent, semi-structured data. RDBMS fail if the need is to manage any unstructured data.
#5. Response time
As seen above, with higher throughput, Hadoop can instantly access loads of data sets compared to RDBMS; however, you may not be able to access to a specific record from the whole set of data instantly. In this case, Hadoop is found to have low latency. However, RDBMS can be faster and better in this aspect of retrieving the information from structured data sets. Provided the data volume is small, RDBMS takes only very little time and effort to perform the same function.
RDBMS offers vertical scalability, which is the concept of scaling up the host machine to increase its capacity. You simply alter the hardware such as adding memory and CPU performance to a machine in the cluster. This requires further investment. However, Hadoop offers horizontal scalability. This means simply adding more machines to the cluster and Hadoop becomes more fault tolerant. There is no single point of failure to end up in a complete black-out. As it is a cluster with multiple machines, data recovery is automatically made if there is any case of infringement.
More than anything, Hadoop is open source and free to use software framework. You don't have to bear any license fee. RDBMS is licensed, and you have to pay to get the software license and also subscription fees.
Created on Jan 7th 2019 03:19. Viewed 35 times.