Hadoop to HiveQL
Apache Hadoop is the storage system which is written in Java, which is an open-source, fault-tolerant, and scalable framework. It gives a platform to process a large amount of data. It is also useful in storing the collective data such as transactional, sensor, social media, machine, scientific, click streams, etc.
Hadoop makes use of Data Lake, which supports the storage of data in its original or exact format. Hadoop is designed in such a way through which there can be a scale up from single servers to thousands of machines, each of which offering local computation and storage.
Before getting started with Hadoop, you have to be familiar with the programming languages like Core Java and must have the conceptual understanding of Database, and Linux operating system.
Uses of Hadoop: –
There is no need to preprocess data before storing it (you may store as much data as you want and decide later how to use it)
- You may easily grow your system to handle more data easily by adding nodes (only a little administration is required)
It is convenient to use for millions or billions of transactions
Many cities, states, and countries make use of Hadoop to analyze data. For example, figuring out the traffic jams which can be controlled by the use of Hadoop (Concept of Smart City)
Big data is also used by many businesses to optimize their data performance in an effective manner
Apache Hive is a data warehouse software project which was built on the top of Apache Hadoop for supplying data query and analysis. It makes use of declarative language, which is similar to SQL called HQL. Hive allows programmers who are well-known with the language to write custom MapReduce framework to perform more knowledgeable analysis. The functional features of Hive are-
- Data Summarization
Remember that Hive is not-
- a relational database
- a design for OLTP (but it is designed for OLAP)
- a language for real-time queries and row-level updates
Uses of Hive: –
- It is easy for Hive to perform operations like Data encapsulation, ad-hoc queries, and analysis of large datasets
Creation of tables and databases are the prior tasks of Hive; data is loaded into these tables after that only
Hive uses “Read Many Write once” pattern which means after insertion of the table we can update them accordingly in the latest versions of Hive
Hive provides tools to enable easy data extract/transform/load (ETL)
With the help of Hive, one can access files stored in HDFS (Hadoop Distributed File System)
The Hive Query Language is a SQL like an interface which is used to query data stored in the database and file systems that are integrated with Hadoop. It supports simple SQL like functions- CONCAT, SUBSTR, ROUND, etc. and aggregate functions like- SUM, COUNT, MAX, etc.
It also supports clauses- GROUP BY and SORT BY. Also, it is possible to write user-defined functions using Hive Query Language (HQL). Basically, it makes use of the well-known concepts from the relational database world, like- tables, rows, columns, and schema.
DDL Commands in HQLCREATEDatabase, TableDROPDatabase, TableTRUNCATETableALTERDatabase, TableSHOWDatabase, Table, Table Properties, Partitions, Functions, IndexDESCRIBEDatabase, Table, ViewDML Commands in HQLLOADDatabase, Table, Rows, ColumnsINSERTDatabase, Table, Rows, Columns
Uses of HiveQL: –
- HQL is the twin of SQL
- HQL allows programmers to plug-in custom mappers and reducers
- HQL is scalable, familiar, extensible, and fast to use
- It provides indexes to correct queries
- HQL contains a large number of user function APIs which can be used to create custom behavior into the query engine
- It perfectly fits in the requirement of a low-level interface of Hadoop
Well, as per the above explanation of all three components of Big Data (Hadoop, Hive, and HiveQL), you would be understood how all three are relatable to each other in the area of Data Science. Let me brief you how all three components work collaboratively. Have a glance at the below figure: –
In the novice dialect, Hadoop is the framework or act as a platform which does all the native tasks of Big Data technology. On the other hand, Hadoop Hive is the component of Hadoop which provides the front-end part to Big Data.
Hive Query Language is also the component of Hadoop, but it provides back-end part to Big Data. As per the developer point of you, it is understood that no system is complete without front-end or back-end. Both are necessary to create and run the system smoothly and efficiently.
Let us try to understand this in a layman’s language. Hadoop is like the base on which the building has to be constructed. Hadoop Hive is the architecture which is to be designed on the building, and the HQL is responsible for creating the internal architecture.
Major Reasons to use Hadoop for Data Science
There are several reasons to use Hadoop for Data Science: –
- When you have to deal with a large amount of data, Hadoop is the best option to choose When you are planning to implement Hadoop on your data, the first step is to understand the complexity level of data and the data-rate based on which data is going to grow. In this case, cluster planning is required. Depending upon the size of data of the company (GBs or TBs), Hadoop is helpful here.
- When you want to protect your data for long-time or want your data to run forever, Hadoop can be the solution. Because using Hadoop, you may increase the size anytime depending upon your requirement by adding data nodes to it at a minimal cost.
Hadoop has become de-facto of Data Science and is the gateway of Big Data related technologies. It is the foundation of other Big Data technologies like Spark, Hive, etc. As per Forbes– “Hadoop market is expected to reach $99.318 by 2022 at a CAGR of 42.1 percent.” So, this is the right time to give a push to your skills in the field of Big Data. Happy Reading!