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NoSQL databases Advantages and Disadvantages

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 It is not a relational database that means while creating a database we don’t have to pre-defined a schema. It is also called a non-relational database. It is used to store unstructured and semi-structured data.

NoSQL database can be divided into the following four types:

Key-value storage: In this database data is stored in key-value pair where each key is associated with one and only one value in a collection. It is easy to use, scalable and fast. Some key-values databases are Redis, Riak, etc in HADOOP assignment help from top-rated experts. Document-oriented Database: It stores data in JSON format. We don’t need to predefine schema for database and all information for an object or document is stored in a single instance. Some of the popular databases are MongoDB, PostgreSQL, Elasticsearch, etc.

Advantages of NoSQL Database

Performance – With NoSQL Data is nestled inside of one, so queries perform better that’s why NoSQL databases are faster than SQL databases.

Scalability – NoSQL databases are vertically scalable.

Flexibility – We can combine any type of data, both structured and unstructured with our evolving requirements.

Stores Massive Amounts Of Data – It stores a huge amount of Data.

Disadvantages of NoSQL Database

Not Mature – It’s not as mature as SQL that means It isn’t easy to find the kind of information and support on NoSQL.

Huge databases – NoSQL databases are not built to remove duplicate data so maintaining data quality becomes a little difficult.

 Hive

Apache hive is one of the most popular data warehouse components on the Big Data landscape. It is mainly used to complement the Hadoop file system with its interface. hive was originally developed by Facebook and is now maintained as Apache Hadoop assignment help hive by Apache Software Foundation. It is used and developed by biggies such as Netflix and Amazon as well. The Hadoop ecosystem is not just scalable but also cost-effective when it comes to processing large volumes of data.

It is also a fairly new framework that packs a lot of punch. However, organizations with traditional data warehouses are based on SQL with users and developers that rely on SQL database assignment help queries for extracting data. It makes getting used to the Hadoop ecosystem an uphill task and that is exactly why hive was developed. hive provides SQL interaction so that users can write SQL-like queries called HQL or hive query language to extract the data from Hadoop.

These SQL-like queries will be converted into MapReduce jobs by the hive component and that is how it talks to the Hadoop ecosystem and HDFS file system. hive can be used for OLAP online analytical processing. It is scalable, fast, and flexible. It is a great platform for SQL users to write SQL-like queries to interact with the large datasets that reside on the HDFS file system.

what hive cannot be used for: 

  • It cannot be used for a relational database.
  • It cannot be used for OLTP online transaction processing. 
  • It cannot be used for real-time updates or queries.
  • It cannot be used for scenarios where low latency data retrieval is expected because there is a latency in converting the hive scripts into MapReduce scripts by the hive.

 

Some of the finest features of the hive:

It supports different file formats like SQL file, text file, Avro file format, Oh RC file, and RC file. Metadata gets stored in our DBMS like the Derby database assignment help from top experts. Users can write SQL-like queries that convert into MapReduce or Tez or sparks jobs to query against Hadoop datasets. Users can plug in MapReduce scripts into the hive queries using UDF user-defined functions. Specialized joins are available that help to improve query performance.

Hive versus traditional RDBMS

Hive enforces schema on reading. Schema on reading allows the component to insert data without checking for the type or schema definition of the table. it verifies the data only when data is read from the table.

Traditional RDBMS enforce a schema on right . Schema on write includes verifying if the data is inserted as per the table definition and schema definition during the writing phase itself this is how our DBMS databases like my SQL and  Data Analytics using Jupyter Notebook or Oracle servers work. hive allows you to store hundreds of petabytes of data because hive stores data in HDFS which has a scalable storage space. Querying terabytes of data in RDBMS is not an easy task. Hive doesn’t support OLTP. RDBMS supports OLTP.

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