We are now producing huge amounts of data at an expedited rate. To satisfy business requirements, approach to improve business dynamics as well as better decision-making, complex interpretation of this data from diverse sources is needed. The hurdle is how to obtain, store and model these large pools of data efficiently in relational databases.
Let’s take an example of an e-commerce website. There is a necessity to manage product information, performance data as well as product reports. That data can only be saved in the RDBMS (relational database management system). But as the volume of comments raises, we must modify the table to support the increase. These differences are Nearly-Real-Time (NRT) and data modeling grows very challenging due to the time and resources needed to achieve these changes. Any modifications in the RDBMS schema may also influence the performance of the production database. There can be many situations related to this where changes in the RDBMS schema are needed due to the nature and volume of information collected in the database. These difficulties can be discussed using toolsets from the Hadoop ecosystem.
Hadoop is a java-based framework intended to untangle the complexities of big data analytics, supporting users’ process and collect huge quantities of data for handling real-time analysis. Hadoop performs by utilizing a set of algorithms to evaluate outcomes from big data.
Examining data in the production database happens by querying it, which may result in a degeneration of overall database performance. In such circumstances where a lot of data analysis requires to be done in production databases, users prefer that the data collected in RDBMS be transferred to the Hadoop ecosystem for better performance. Note that Hadoop cannot substitute RDBMS simply as Hadoop is only fit for online analytical processing (OLAP) and never to online transaction processing (OLTP).
What is RDBMS?
RDBMS is the abbreviation of “Relational Database Management System”. An RDBMS is a DBMS (database management system) created particularly for relational databases. Hence, RDBMSes are a subset of DBMSes.
A relational database refers to a database that stores data in a structured format, using rows and columns. This makes it easy to find and locate specific values within the database. It is rather relational because the values within each table are related to each other. Tables may also be related to other tables. The relational structure makes it feasible to run queries over many tables at once.
While a relational database represents the type of database an RDBMS accomplishes, the RDBMS indicates to the database program itself. It is the software that performs queries on the data, including adding, renewing, and searching for values. An RDBMS may also provide a visual representation of the data. For instance, it may display data in a table like a spreadsheet, permitting you to view and even edit unique values in the table. Some RDMBS plans allow you to build forms that can streamline entering, editing, and deleting data.
Most well-known DBMS applications come in the RDBMS section. The use cases include MySQL, Microsoft SQL Server, Oracle Database, and IBM DB2. Some of these programs encourage non-relational databases, but they are essentially utilized for relational database management.
Instances of non-relational databases are Apache HBase, IBM Domino, and Oracle NoSQL Database. These types of databases are maintained by other DMBS programs that help NoSQL, which does not fall into the RDBMS category.
Major Difference between HADOOP vs RDBMS
An RDBMS operates well with structured data. Hadoop will be a great option in environments when there are requirements for big data processing on which the data being treated does not have steady relationships. When the size of data is too big for aggregate processing and saving or not simple to determine the relations between the data, then it grows hard to save the obtained information in an RDBMS with a consistent relationship.
Hadoop software framework work is a very properly structured, semi-structured, and unstructured data. RDBMS database technology is determined, compatible, developed and extremely supported by the world’s most reputable companies. It runs well with data classifications such as data types, relationships among the data, restrictions, etc. Therefore, this is more suitable for online transaction processing (OLTP).
How to Migrate RDBMS to Hadoop HDFS: Tools Required
While considering data migration, one of the best tools obtainable in the Hadoop Ecosystem is Apache Sqoop. Sqoop serves as the transitional layer between the RDBMS and Hadoop to assign data. It is utilized to import data from the relational database like MySQL/Oracle to Hadoop Distributed File System (HDFS) and export data from the Hadoop file system to relational databases.
In situations where computing resources are insufficient, Sqoop is not a viable choice as it may have high resource usage. A better choice in such cases is to handle Apache Spark SQL.
Apache Spark SQL is a module of Apache Spark for working on structured data. It assists to process the data in a fast and classified manner and is meant to efficiently perform interactive queries and stream processing. It grants advantages of speed, user-friendliness and a centralized processing engine.
Migrate RDBMS to Hadoop Equivalent Utilizing Spark
Let’s take a likely situation where the project stack does not incorporate Hadoop Framework, but the user needs to migrate the data from an RDBMS to HDFS equivalent system, for instance, Amazon s3. In this situation, Apache Spark SQL can be utilized.
Apache Spark SQL has two kinds of RDBMS segments for such a migration, identified as JDBCRDD and JDBCDATAFRAME.
Sqoop is recognized to use a lot of processing power for data migration. Where this is not available, Spark SQL can be granted. There are many factors when assessing the tools and methods to migrate, maintain and analyze large amounts of data.
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