Complete Course of Apache Hive

Apache Hive is a data warehouse framework built on top of Hadoop that enables users to query and manage large datasets stored in distributed storage using SQL-like language (HiveQL). It abstracts the complexity of writing MapReduce programs, making big data processing more accessible to data analysts and engineers. Learning Apache Hive is crucial for anyone aiming to enter the big data industry, as it is widely used for data analysis and ETL processes in organizations handling massive amounts of data. By mastering Hive, you gain a competitive edge in roles like Data Engineer or Big Data Analyst.
A tutor can accelerate this learning process by providing structured lessons, practical projects, and real-world use cases, ensuring you grasp the concepts quickly and apply them effectively in job interviews and workplace scenarios.

Chapter 1: Introduction to Big Data and Data Warehousing

Lesson 1: What is Big Data? Understanding the 3 Vs (Volume, Velocity, Variety)
Lesson 2: Key Challenges in Big Data Analytics
Lesson 3: Overview of Data Warehousing Concepts
Lesson 4: Tools for Distributed Data Storage and Processing (HDFS, Ceph, GlusterFS, etc.)
Lesson 5: Data Processing Tools: Apache Spark, Apache Flink, Apache Storm, and more
Lesson 6: Comparison of Big Data Tools: Apache Hive vs. Other Data Warehousing Tools (e.g., Apache Impala, Presto, Amazon Redshift)

Chapter 2: Introduction to Apache Hive

Lesson 1: What is Apache Hive? An Introduction to Data Warehousing on Hadoop
Lesson 2: History and Evolution of Apache Hive
Lesson 3: Key Features and Benefits of Apache Hive
Lesson 4: The Role of Hive in the Hadoop Ecosystem
Lesson 5: Comparing Apache Hive with Other Data Warehousing Solutions

Chapter 3: Setting Up Apache Hive

Lesson 1: System Requirements and Prerequisites for Hive
Lesson 2: Installing Hive on Local and Cluster Environments
Lesson 3: Configuring Hive (Single Node vs. Multi-Node Setup)
Lesson 4: Setting Up Hive on Cloud Platforms (AWS, Azure, GCP)
Lesson 5: IDE Integration for Hive Development (IntelliJ IDEA, Eclipse)

Chapter 4: Apache Hive Architecture

Lesson 1: Hive Architecture Overview
Lesson 2: Components of Hive: Metastore, Driver, Execution Engine
Lesson 3: Hive Query Execution Flow
Lesson 4: Understanding HiveQL (Hive Query Language)
Lesson 5: Internal and External Tables in Hive
Lesson 6: Partitioning and Bucketing in Hive

Chapter 5: Working with Apache Hive Tables

Lesson 1: Creating and Managing Tables in Hive
Lesson 2: Working with Internal Tables
Lesson 3: Creating and Managing External Tables
Lesson 4: Partitioning Data in Hive
Lesson 5: Bucketing in Hive: Understanding Buckets and Their Use Cases
Lesson 6: Table Formats: Text, ORC, Parquet, and Avro

Chapter 6: Data Management and Operations in Hive

Lesson 1: Loading Data into Hive: Using LOAD and INSERT
Lesson 2: Querying Data with SELECT Statements
Lesson 3: Filtering and Aggregating Data
Lesson 4: Updating and Deleting Data in Hive
Lesson 5: Data Transformation with Hive (JOIN, GROUP BY, ORDER BY)

Chapter 7: Advanced Querying and Optimizations in Hive

Lesson 1: Advanced HiveQL: Subqueries, Window Functions, and UDFs
Lesson 2: Performance Optimization Techniques for Hive Queries
Lesson 3: Query Execution Plans in Hive
Lesson 4: Indexing in Hive
Lesson 5: Caching and Result Set Optimization in Hive
Lesson 6: Query Optimization Strategies: Tez and LLAP (Live Long and Process)

Chapter 8: Advanced Data Management in Hive

Lesson 1: Working with Complex Data Types in Hive (Structs, Maps, Arrays)
Lesson 2: Writing and Using User-Defined Functions (UDFs) in Hive
Lesson 3: Introduction to Hive Scripts and Macros
Lesson 4: Using Views and Materialized Views in Hive
Lesson 5: Data Serialization and Deserialization in Hive

Chapter 9: Hive Metastore

Lesson 1: Overview of Hive Metastore
Lesson 2: Metastore Configuration and Architecture
Lesson 3: Managing Metadata in Hive Metastore
Lesson 4: Integrating Hive with External Databases (e.g., MySQL, PostgreSQL)
Lesson 5: Understanding the Importance of Metastore in Large-Scale Environments

Chapter 10: Integrating Hive with Hadoop Ecosystem

Lesson 1: Using Hive with HDFS for Distributed Storage
Lesson 2: Integrating Hive with Apache Spark for Advanced Analytics
Lesson 3: Using Hive with Apache HBase for NoSQL Storage
Lesson 4: Using Apache Sqoop for Data Import/Export Between Hive and Relational Databases
Lesson 5: Streamlining Data Ingestion with Apache Flume and Hive
Lesson 6: Integrating Apache Oozie for Workflow Scheduling in Hive

Chapter 11: Security and Authentication in Hive

Lesson 1: Hive Security Overview
Lesson 2: Authentication and Authorization in Hive (Kerberos, LDAP)
Lesson 3: Role-Based Access Control (RBAC) in Hive
Lesson 4: Data Encryption and Secure Data Access in Hive
Lesson 5: Auditing Hive Queries for Security and Compliance

Chapter 12: Performance Tuning in Hive

Lesson 1: Optimizing Query Performance in Hive
Lesson 2: Using Columnar Storage Formats (ORC, Parquet) for Better Performance
Lesson 3: Tuning Hive Execution Engine (Tez and LLAP)
Lesson 4: Managing Resources in Hive (Memory Allocation, Disk Usage)
Lesson 5: Monitoring and Debugging Performance Issues in Hive

Chapter 13: Recent Updates in Apache Hive

Lesson 1: New Features in the Latest Apache Hive Releases
Lesson 2: Hive on Tez vs. Hive on MapReduce: Performance Comparison
Lesson 3: Hive LLAP (Low Latency Analytical Processing) and Its Benefits
Lesson 4: Hive 4.0 and Beyond: What's New and What’s Coming
Lesson 5: Apache Hive in Cloud Platforms (AWS, GCP, Azure)

Chapter 14: Troubleshooting and Debugging in Hive

Lesson 1: Common Issues in Hive Query Execution
Lesson 2: Debugging and Error Handling in Hive
Lesson 3: Optimizing Hive Logs for Troubleshooting
Lesson 4: Common Performance Bottlenecks and How to Resolve Them
Lesson 5: Best Practices for Hive Troubleshooting

Chapter 15: Real-World Applications of Hive

Lesson 1: Hive for Data Warehousing and Reporting
Lesson 2: Leveraging Hive for ETL (Extract, Transform, Load) Workflows
Lesson 3: Hive for Log Analytics and Data Mining
Lesson 4: Using Hive in Financial and Healthcare Data Analysis
Lesson 5: Best Practices for Scaling Hive for Large-Scale Data Operations

Chapter 16: Hive Integration with Machine Learning and Data Science

Lesson 1: Using Hive for Feature Engineering and Data Preparation
Lesson 2: Integrating Hive with Apache Spark for Machine Learning
Lesson 3: Leveraging Hive for Scalable Data Science Workflows
Lesson 4: Advanced Analytics Using Hive and Spark SQL
Lesson 5: Real-Time Analytics with Hive and Streaming Data

Chapter 17: Hive in Modern Data Architectures

Lesson 1: Hive in Data Lakes and Big Data Ecosystems
Lesson 2: Leveraging Hive in Lambda and Kappa Architectures
Lesson 3: The Role of Hive in Modern Data Pipelines
Lesson 4: Integration of Hive with Streaming Platforms like Apache Kafka
Lesson 5: Combining Hive with Data Lakes for Enhanced Analytics

Chapter 18: Best Practices and Future Directions

Lesson 1: Best Practices for Managing Hive in Large-Scale Environments
Lesson 2: Optimizing Hive for Cost-Effective Storage and Compute
Lesson 3: Hive and Data Governance: Ensuring Quality and Compliance
Lesson 4: The Future of Apache Hive in Cloud-Native Environments
Lesson 5: Emerging Trends in Apache Hive and Big Data Analytics

The online class is held via Skype (or Zoom or Microsoft Teams) and the cost per hour of tutoring is only $15. At the end of this long course, you will master all the required basic and advanced concepts of Apache Hive and we will develop a real world project together for about 10 hours, that fully prepares you to find a job as a professional Database Administrator or entry-level Big Data Engineer.
To book this class, message or call my telegram or WhatsApp:
+98 (912) 490-8372 or +98 (935) 490-8372
You can also send email to me:
abolfazl.mohammadijoo@gmail.com

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