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Difference Between Hadoop and SQL Performance

The data generation is continuous, and the amount of data is overwhelming. The enormous amount of data generated mainly from Facebook, Instagram, and Twitter resulted in creating Big data ecosystems. So, while handling big data, you must know SQL and Hadoop. But, if you want to see the difference between Hadoop and SQL performance, you have landed on the right page. So, let us look at those differences. 

Data Processing 

The amount of data SQL process is low. It works excellently with Gigabytes. But if handling a large amount of data, for example, processing Terabytes, Petabytes SQL fails to meet the expectations. 

Wherein Hadoop is designed to handle significant data types that we can see in current-day enterprises. So, for the big-sized companies, Hadoop is the optimal choice. 

Processing Speed

Is Hadoop processes the data quicker than SQL? Let’s find out the same by analyzing the processing techniques. 

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Hadoop consists of two core components. The HDFS or Hadoop Distributed File Systems processes data with MapReduce and Flat File System. It doesn’t support real-time processing of data or OLTP. It supports the large-scale processing of data, which is used heavily in data mining. With the help of OLAP (large scale processing), it aids in executing complicated things with aggregations. When it comes to the processing speed, it varies as per the data set you input. It may take from minutes to several hours.

The SQL supports Real-time data processing or OLTP. Since it handles normalized data, SQL’s processing speed is quicker, but it does not support batch processing. So, that is the reason Hadoop Certification Training is getting popular with each passing day. 

ACID property

In SQL, you can get the support of RDBMS ACID property, such as Consistency, Isolation, Atomicity, and Durability. On the flip side, in Hadoop, you must code all scenarios to implement this. Of course, in this, SQL is gaining the upper hand over Hadoop. 

Fault Tolerance

In conventional RDBMS, in case of any data lost because of network issues or corruption, it tasks a considerable amount of cost, time, and resources to recover. So, this is the case of SQL. But on the other hand, Hadoop has a system where data replicates into three different levels. It seems like the wastage of time, but if the data saved in one data saved lost, you could easily pull them from the other data nodes. 


The most significant difference between these two is you need to pay more to buy a SQL server. The server costs much, but even if your storage runs out, you need to pay additional charges to buy the storage required. But Hadoop is an open-source platform, where you need to pay nothing, and for larger-scale usage, Hadoop is the best. That is the reason many enterprises are opting for Hadoop. 

Thankfully, even the cost of Hadoop online training is not sky level so that you can get the courses at an affordable price. 


Hadoop has a dynamic schema, and it can store a massive amount of data and process the same. It can store videos, images, sensor data, and other types of data types in real-time. 

On the other hand, SQL has a static schema, and it can store only in tabular format or structured data. 

Functional Programming

Hadoop allows you to program with languages like Scala, Java, and python. If you need any additional function, you can get it by registering User-defined Functions or UDF in the HDFS. However, in RDBMS, you don’t get the freedom to write UDF; thus, the complexity of SQL increases. Additionally, the data stored in Hadoop can be accessed easily with Pig, Hive, Sqoop, and other ecosystems. 

Other Differences

·         Hadoop stores data in its HDFS and process them with MapReduce technology for better optimization. But SQL doesn’t have any such optimization techniques. 

·         When it comes to data updates in SQL, you have to read and write the data multiple times, and in Hadoop, you need to write the data once and read multiple times. 

·         The SQL uses property hardware; on the other hand, Hadoop uses commodity hardware. After seeing the many differences between the popular data handling platform, it is evident that Hadoop is the best choice. You can master it by enrolling any of the best available Hadoop online training classes.

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