Difference Between Hadoop And Traditional RDBMS
Like Hadoop, traditional RDBMS cannot be used when it comes to process and store a large amount of data or simply big data. Following are some differences between Hadoop and traditional RDBMS.
Data volume means the quantity of data that is being stored and processed. RDBMS works better when the volume of data is low(in Gigabytes). But when the data size is huge i.e, in Terabytes and Petabytes, RDBMS fails to give the desired results.
On the other hand, Hadoop works better when the data size is big. It can easily process and store large amount of data quite effectively as compared to the traditional RDBMS.
If we talk about the architecture, Hadoop has the following core components:
HDFS(Hadoop Distributed File System), Hadoop MapReduce(a programming model to process large data sets) and Hadoop YARN(used to manage computing resources in computer clusters).
Traditional RDBMS possess ACID properties which are Atomicity, Consistency, Isolation, and Durability.
These properties are responsible to maintain and ensure data integrity and accuracy when a transaction takes place in a database.
These transactions may be related to Banking Systems, Manufacturing Industry, Telecommunication industry, Online Shopping, education sector etc.
Throughput means the total volume of data processed in a particular period of time so that the output is maximum. RDBMS fails to achieve a higher throughput as compared to the Apache Hadoop Framework.
This is one of the reason behind the heavy usage of Hadoop than the traditional Relational Database Management System.
Data Variety generally means the type of data to be processed. It may be structured, semi-structured and unstructured.
Hadoop has the ability to process and store all variety of data whether it is structured, semi-structured or unstructured. Although, it is mostly used to process large amount of unstructured data.
Traditional RDBMS is used only to manage structured and semi-structured data. It cannot be used to manage unstructured data. So we can say Hadoop is way better than the traditional Relational Database Management System.
Latency/ Response Time
Hadoop has higher throughput, you can quickly access batches of large data sets than traditional RDBMS, but you cannot access a particular record from the data set very quickly. Thus Hadoop is said to have low latency.
But the RDBMS is comparatively faster in retrieving the information from the data sets. It takes a very little time to perform the same function provided that there is a small amount of data.
RDBMS provides vertical scalability which is also known as ‘Scaling’ Up a machine. It means you can add more resources or hardwares such as memory, CPU to a machine in the computer cluster.
Whereas, Hadoop provides horizontal scalability which is also known as ‘Scaling Out’ a machine. It means adding more machines to the existing computer clusters as a result of which Hadoop becomes a fault tolerant. There is no single point of failure. Due to the presence of more machines in the cluster, you can easily recover data irrespective of the failure of one of the machines.
Apache Hadoop supports OLAP(Online Analytical Processing), which is used in Data Mining techniques.
OLAP involves very complex queries and aggregations. The data processing speed depends on the amount of data which can take several hours. The database design is de-normalized having fewer tables. OLAP uses star schemas.
On the other hand, RDBMS supports OLTP(Online Transaction Processing), which involves comparatively fast query processing. The database design is highly normalized having a large number of tables. OLTP generally uses 3NF(an entity model) schema.
Hadoop is a free and open source software framework, you don’t have to pay in order to buy the license of the software.
Whereas RDBMS is a licensed software, you have to pay in order to buy the complete software license.
We have provided you all the probable differences between Big Data Hadoop and traditional RDBMS. Hope you enjoyed reading the blog