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Hadoop vs. SQL - Coggle Diagram
Hadoop vs. SQL
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Architecture
Hadoop is meant for Big Data solution, and usually, Hadoop architecture consists of an unlimited number of servers.
Because every time data gets replicated in each data blocks, hence data processing continues without any interruption and maintains consistency. As a result, Hadoop architecture is highly reliable for data.
On the other hand, for SQL you need complete consistency across all the systems before it releases anything to the user. This is called a two-phase commit.
Supported Data Format
SQL only work on structured data, whereas Hadoop is compatible for both structured, semi-structured and unstructured data.
SQL is based on the Entity-Relationship model of its RDBMS, hence cannot work on unstructured data.
On the other hand, Hadoop does not depend on any consistent relationship and supports all data formats like XML, Text, and JSON, etc.So Hadoop can efficiently deal with big data.
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Performance
However, there is another aspect when we compare Hadoop vs SQL performance. This is Latency. Hadoop cannot access a particular record from the data set very quickly. Hence, it has very low latency. On the other hand, you can retrieve information from data sets faster using SQL.
One of the significant parameters of measuring performance is Throughput. It is the total volume of output data processed in a particular period and the maximum amount of it. SQL database fails to achieve a higher throughput as compared to the Apache Hadoop Framework.
Data Storing Technique
A crucial principle of relational databases is data stores in tables containing relational structure characterized by defined row and columns. Moreover, data is stored in interrelated tables.
In Hadoop, a basic data can begin in any shape. However, in the long run, it changes into a key-value pair. Because once the data enters into Hadoop, it is replicated across multiple nodes in the Hadoop Distributed File System (HDFS).
The Way of Data Mapping
In case of SQL operations like a write operation from one table to another for data mapping, we need to know the information beforehand. The information here indicates the schema of the mapping tables. Hence, it is a schema on write.
On the other hand in Hadoop when we perform write operation on data, i.e., on the Hadoop Distributed File System we do not need to follow any rules. Similarly, when we want to read the data, we need to code. It is schema on reading.
ACID Property
With SQL, you will get the support of RDBMS ACID properties – Atomicity, Consistency, Isolation, and Durability.
However, in Hadoop, this is not out of the box. So you have to code all the scenarios to implement commit or rollback during a transaction.