Learn everything about Apache Hive a modern, data warehouse.
What you will learn
Why Hive is necessary for Data Engineer
The goal of this course is to help you become familiar with Apache Hive bits and bytes
Learn A to Z of Apache HIVE (From Basic to Advance level).
Hands on Experience on Apache Hive and Real-time Use Case
Description
The Apache Hive data warehouse software facilitates reading, writing, and managing large datasets residing in distributed storage using SQL. Structure can be projected onto data already in storage. A command-line tool and JDBC driver are provided to connect users to Hive.
One of the most valuable technology skills is the ability to analyze huge data sets, and this course is specifically designed to bring you up to speed on one of the best technologies for this task, Apache Hive! The top technology companies like Google, Facebook, Netflix, Airbnb, Amazon, NASA, and more are all using Apache Hive!
Built on top of Apache Hadoop, Hive provides the following features:
- Tools to enable easy access to data via SQL, thus enabling data warehousing tasks such as extract/transform/load (ETL), reporting, and data analysis.
- A mechanism to impose structure on a variety of data formats
- Access to files stored either directly in Apache HDFS™ or in other data storage systems such as Apache HBase™
- Query execution via Apache Tez™, Apache Spark™, or MapReduce
- Procedural language with HPL-SQL
- Sub-second query retrieval via Hive LLAP, Apache YARN and Apache Slider.
Hive provides standard SQL functionality, including many of the later SQL:2003, SQL:2011, and SQL:2016 features for analytics.
Hive’s SQL can also be extended with user code via user defined functions (UDFs), user defined aggregates (UDAFs), and user defined table functions (UDTFs).
There is not a single “Hive format” in which data must be stored. Hive comes with built in connectors for comma and tab-separated values (CSV/TSV) text files, Apache Parquet™, Apache ORC™, and other formats. Users can extend Hive with connectors for other formats. Please see File Formats and Hive SerDe in the Developer Guide for details.
Hive is not designed for online transaction processing (OLTP) workloads. It is best used for traditional data warehousing tasks.
Hive is designed to maximize scalability (scale out with more machines added dynamically to the Hadoop cluster), performance, extensibility, fault-tolerance, and loose-coupling with its input formats.
We will learn
1) Apache Hive Overview
2) Apache Hive Architecture
3) Installation and Configuration
4) How a Hive query flows through the system.
5) Hive Features, Limitation and Data Model
6) Data Type, Data Definition Language, and Data Manipulation Language
7) Hive View, Partition, and Bucketing
8) Built-in Functions and Operators
9) Join in Apache Hive
10) Frequently Asked Interview Question and Answers
11) 2 Realtime Projects
My goal is to provide you with practical tools that will be beneficial for you in the future. While doing that, with a real use opportunity.
I am really excited you are here, I hope you are going to follow all the way to the end of the course. It is fairly straight forward fairly easy to follow through the course I will show you step by step each line of code & I will explain what it does and why we are doing it. So please I invite you to follow up on it to go through all the lectures. All right I will see you soon in the course.
Content
Introduction
Installing Apache Hive on Ubuntu (Linux) Machine
Hive Data Model
Hive Data Types
HIVE Data Definition Language.
HIVE Data Manipulation Language
Hive View, Metastore, Partitions, and Bucketing
Hive Built-In Functions
Built-in Operators
Hive Join
Frequently Asked Interview Question and Answers
Hands On Projects (2 Projects)
The No-Nonsense Verdict on Apache Hive for Data Engineers
If you have spent any time in the big data ecosystem, you know that Apache Hive is often called the “grandfather” of SQL-on-Hadoop. Some people will tell you it is being phased out by Spark or Presto, but those people aren’t working in the enterprise trenches. The reality is that Hive remains a cornerstone of the modern data lake, and if you want job-ready skills that actually translate to high-paying career growth, you need to master it. I recently went through the “Apache Hive for Data Engineers (Hands On) with 2 Projects” course, and here is my unfiltered take.
Most instructors treat Hive like a simple SQL wrapper. This course does the opposite. It treats Hive like the complex, distributed data warehouse it actually is. What I appreciated most was the focus on “under the hood” mechanics. We aren’t just writing SELECT statements; we are looking at how those queries are translated into MapReduce or Tez jobs and how they navigate a distributed environment. This isn’t just a tutorial; it is certification prep for anyone looking to nail a technical interview at a Fortune 500 company.
The standout feature here is the hands-on labs. We have all been there—trying to install Hadoop and Hive on a local machine and ending up with a broken environment and a headache. This course solves that by providing step-by-step guidance for both Ubuntu and Windows via Docker Desktop. Using Docker is a game-changer because it mimics a production-grade containerized environment, which is the industry-standard tool set right now.
What You Actually Need Before Starting
Don’t jump into this if you don’t know the difference between a JOIN and a UNION. While the course is billed as beginner to advanced, you should have these basics locked down:
- Foundational SQL: You don’t need to be a guru, but you should understand basic DDL and DML operations.
- Basic Linux Command Line: You will be spending time in the terminal. If `cd` and `ls` scare you, take a 20-minute refresher first.
- Hardware Specs: Since you will be running Docker or a VM, make sure your machine has at least 8GB of RAM (16GB is better) to keep things smooth.
The Toolkit: Skills & Industry-Standard Tools
This course packs a serious punch when it comes to the tech stack. You aren’t just learning a single tool; you are learning how an entire ecosystem breathes. Key takeaways include:
- Containerization: Mastering Docker Desktop for local data engineering environments.
- Data Modeling: A deep dive into Partitions and Bucketing. This is where you learn to actually save a company money by optimizing storage and compute.
- The Hive Metastore: Understanding how metadata is managed separately from the actual data in HDFS.
- Advanced Querying: Handling Complex Data Types like Arrays, Maps, and Structs—skills that separate the junior developers from the seniors.
Career Benefits & Real-World Job Roles
Let’s talk money. Why learn Hive in 2024? Because legacy systems aren’t going anywhere, and modern “Lakehouse” architectures still rely on the Hive Metastore. Completing this course prepares you for several high-demand roles:
- Data Engineer: Designing and maintaining scalable ETL pipelines.
- Big Data Architect: Deciding when to use partitioning vs. bucketing for petabyte-scale datasets.
- Data Warehouse Engineer: Transitioning traditional RDBMS knowledge into the distributed world.
The real-world projects included in the curriculum are vital for your portfolio. When a recruiter asks if you have handled schema-on-read scenarios, you can actually point to a project where you built a full-scale Hive warehouse from scratch.
Why This Course Hits the Mark (The Pros)
- The Docker Approach: I cannot stress this enough. Learning to deploy Hive via Docker makes you more versatile in a modern DevOps-heavy data world.
- Deep Optimization: The course doesn’t just show you how to make it work; it shows you how to make it fast. The sections on partitioning and bucketing are career-defining for those working with massive datasets.
- Practical Projects: The two projects aren’t just “copy-paste” exercises. They force you to apply DDL and DML logic to solve actual data problems, reinforcing the hands-on labs experience.
The Reality Check (The Cons)
- Version Lag: As with any tech course, some of the specific library versions in the installation phase can be finicky. While the instructor provides great guidance, be prepared to do a little “troubleshooting” on your own if a Docker image update changes things. However, I’d argue that troubleshooting is a core skill for any real Data Engineer, so consider it a “hidden feature.”
Overall, if you want to move past basic SQL and start playing in the big leagues of Big Data, this course is an essential investment in your career growth. It turns a daunting tool into a manageable, powerful asset in your professional utility belt.