
Explore the foundations of Apache Hive within the big data course, covering architecture, data types, user-defined functions, and partitioning and bucketing, with hands-on practice.
Download and install Oracle VirtualBox, meet prerequisites of 16 GB RAM and 64-bit processor, then launch the VirtualBox interface from the Oracle site.
Install Ubuntu on Oracle VirtualBox by creating a 64-bit VM with 8–16 gb RAM and a 25 gb dynamically allocated disk, then boot the Ubuntu ISO to install.
Sign up for Google Cloud Platform, create a Dataproc cluster, configure storage bucket, upload a CSV, and connect to HDFS and Hive to query flight data.
Differentiate transactional processing and analytical processing; transactional focuses on updating and real-time access of individual records, while analytical analyzes large historical data across multiple sources for insights.
Data warehouses integrate data from multiple heterogeneous sources to support analytical reporting and decision making, using ETL to extract, transform, and load historical data optimized for read operations.
Hive sits on top of the Hadoop ecosystem and uses a SQL-like HiveQL to query data stored in ZFS. It translates queries into MapReduce tasks, exposing data as tables.
Set up Hive on Google Cloud Platform, connect via a VM terminal, create a Hive database and a Customers table, then insert and query records to verify data.
Learn how Hive translates queries into MapReduce jobs to analyze data in High DB, practice inserting records, and write conditions, aggregates, and group by queries on the customers table.
Compare hive and rdbms by contrasting hive's parallel processing of large data on a distributed file system with rdbms's indexed, schema-on-write approach and ACID compliance.
Explore the architecture of Hive, an open source data warehouse that uses TFS as the storage system, and how the driver, compiler, execution engine, and metastable coordinate query processing.
Understand how Hive stores data in a file system and exposes it as tables through the metastore, which holds table definitions and metadata and can use Derby by default.
Compare the Hive command line interface with Beeline, focusing on authentication, authorization, and Hive server two, then create an orders table and query customers for better visualization.
Explore Hive primitive datatypes, including boolean and numeric types (tinyint to bigint, decimal, float, double), plus string, char, and varchar, and note timestamp handling for storage and calculations.
Continue learning big data and Apache Hive by subscribing to the Bliss Mars Academy YouTube channel for remaining videos or signing up for courses at blissmathacademy.com.
This is an introductory course on one of the most used tools in Big Data - Apache Hive, an ETL(Extraction, Transformation, and Loading) tool, and data warehouse infrastructure software that can create interaction between users and Hadoop Distributed File System (HDFS).Hive is a querying tool for HDFS and the syntax of it's queries is almost similar to our old SQL. Hive is an open source-software that lets programmers analyze large data sets on Hadoop.
We cover Hive, the SQL of Hadoop.(HQL) We will learn why and How Hive is installed and configured on Hadoop. We will cover the components and architecture of Hive to see how it stores data in table like structures over HDFS data
You will also learn internal and external table structures, reading data from different formats into Hive structure. With the help of an easy and intuitive explanation, you will get a good grasp of how to load data into Hive, querying techniques, and generate views in Hive tables. There are multiple examples included to demonstrate the concepts or a particular use case.
The course is a must for anyone in the IT industry who needs to upgrade Big Data knowledge.
This course includes:
Live session
Right blend of concepts and hands-on
Certificate of completion