1. Database Knowledge

Database Knowledge Road Map

Database Knowledge for a Data Engineer:

1. Types of Databases:

   a. Relational Databases (RDBMS): These databases store data in structured tables with rows and columns. They follow the principles of the relational model and are often based on SQL (Structured Query Language).

   • Examples: MySQL, PostgreSQL, Oracle, Microsoft SQL Server

   • Key Concepts: Tables, Rows, Columns, Primary Key, Foreign Key, Indexes, Normalization

   b. NoSQL Databases: They offer more flexibility in terms of storage and schema design. There are different types of NoSQL databases based on the data model they follow.

   • Document-based: Store data as documents, often in JSON format.

     • Examples: MongoDB, Couchbase

   • Column-based: Optimized for operations involving columns rather than rows.

     • Examples: Cassandra, HBase

   • Key-Value Stores: Simple hash table formats.

     • Examples: Redis, Riak

   • Graph-based: Store data as nodes and edges.

     • Examples: Neo4j, ArangoDB

   c. Distributed Databases: Databases designed to run on multiple servers or machines.

   • Examples: CockroachDB, Cassandra

   d. In-memory Databases: Store data in the system's main memory rather than on disk to offer faster response times.

   • Example: Redis, Memcached

2. SQL Proficiency:

   • Basic SQL: SELECT statements, WHERE clause, JOIN operations, GROUP BY, ORDER BY.

   • Advanced SQL: Subqueries, stored procedures, triggers, indexing, views, and functions.

   • SQL Optimization: Understanding execution plans, optimizing queries for better performance.

   • Database Design: Normalization, denormalization, understanding ER diagrams.

3. ACID Properties:

   • Atomicity: Ensures that all operations within a work unit are completed successfully; otherwise, the transaction is aborted at the point of failure, and previous operations are rolled back to their former state.

   • Consistency: Ensures that data remains consistent before and after the transaction.

   • Isolation: Provides the isolation of each transaction in a database.

   • Durability: Ensures that once the transaction has been committed, it remains committed even in cases of system failure.

4. Database Administration & Management:

   • Backup & Recovery: Techniques to backup data and restore when necessary.

   • Performance Tuning: Optimizing database performance, indexing, and using cache.

   • Concurrency & Locking: How databases handle multiple users and ensure data integrity.

   • Security: Access controls, encryption, and monitoring.

Resources for Deep Dive:

1. Books:

   • "Database System Concepts" by Abraham Silberschatz, Henry Korth, S. Sudarshan

   • "NoSQL Distilled: A Brief Guide to the Emerging World of Polyglot Persistence" by Pramod J. Sadalage, Martin Fowler

2. Online Courses:

   • Coursera:

     • "Database Management Essentials" by University of Colorado System

     • "NoSQL Systems" by University of San Diego

   • Udemy:

     • "SQL & Database Design A-Z™: Learn MS SQL Server + PostgreSQL"

     • "Mastering NoSQL: A complete guide to NoSQL databases"

3. Websites & Blogs:

   • Official documentation of databases you choose to explore (like MySQL, PostgreSQL, MongoDB, etc.)

   • SQLZoo - Great for practicing SQL queries.

   • DB-Engines - Provides rankings and overviews of various databases.

Remember, the depth and breadth of your database knowledge will have a direct impact on your efficiency and capability as a data engineer. Gaining practical experience, setting up different types of databases, and building projects around them will solidify your understanding. After you're comfortable with database concepts, we can then move on to the next point in the learning path.