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Hive Practice Example - Explore hive usage efficiently for data transformation and processing in this big data project using Azure VM.
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Big Data is a collection of massive quantities of semi-structured and unstructured data created by a heterogeneous group of high-performance devices spanning from social networks to scientific computing applications. Companies have the ability to collect massive amounts of data, and they must ensure that the data is in highly usable condition by the time it reaches data scientists and analysts. The profession of data engineering involves designing and constructing systems for acquiring, storing, and analyzing vast volumes of data. It is a broad field with applications in nearly every industry.
Apache Hadoop is a Big Data solution that allows for the distributed processing of enormous data volumes across computer clusters by employing basic programming techniques. It is meant to scale from a single server to thousands of computers, each of which will provide local computation and storage.
Apache Hive is a fault-tolerant distributed data warehouse system that allows large-scale analytics. Hive allows users to access, write, and manage petabytes of data using SQL. It is built on Apache Hadoop, and as a result, it is tightly integrated with Hadoop and is designed to manage petabytes of data quickly. Hive is distinguished by its ability to query enormous datasets utilizing a SQL-like interface and an Apache Tez, MapReduce, or Spark engine.
A data pipeline is a technique for transferring data from one system to another. The data may or may not be updated, and it may be handled in real-time (or streaming) rather than in batches. The data pipeline encompasses everything from harvesting or acquiring data using various methods to storing raw data, cleaning, validating, and transforming data into a query-worthy format, displaying KPIs, and managing the above process.
In this project, we will use the Airlines dataset to demonstrate the issues related to massive amounts of data and how various Hive components can be used to tackle them. Following are the files used in this project, along with a few of their fields :
airlines.csv - IATA_code, airport_name, city, state, country
carrier.csv - code, description
plane-data.csv - tail_number, type, manufacturer, model, engine_type
Flights data (yearly) - flight_num, departure, arrival, origin, destination, distance
➔ Language: HQL
➔ Services: Azure VM, Hive, Hadoop
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