1. Database Knowledge: Road Map

Follow this path to learn each points.

1. Types of Databases:

a. Relational Databases (SQL):

• Overview: These databases use tables to store data and are based on the relational model. They use SQL (Structured Query Language) for querying and maintaining the database.

• Examples: MySQL (which you're familiar with), PostgreSQL, Oracle, Microsoft SQL Server.

• Use Cases: When data integrity and structured storage are crucial. Commonly used for transactional systems.

b. NoSQL Databases:

• Overview: These databases are non-relational and can store and retrieve data in ways that don't involve tabular relations. There are different types of NoSQL databases based on their data model.

  • Document-Based: Store data in documents, typically JSON-like.

    • Examples: MongoDB, CouchDB.

    • Use Cases: Content management systems, catalogs.

  • Column-Based: Data is stored in columns rather than rows.

    • Examples: Cassandra, HBase.

    • Use Cases: Analytical processing, any use case that requires efficient read and write of columns.

  • Key-Value Stores: Simple hash table where each key is unique.

    • Examples: Redis, Riak.

    • Use Cases: Caching, session storage.

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

    • Examples: Neo4j, OrientDB.

    • Use Cases: Social networks, recommendation engines.

2. Deep Dive into SQL:

• Advanced SQL Queries: Understand window functions, CTEs (Common Table Expressions), and hierarchical queries.

• Database Design: Learn about normalization, denormalization, and when to use each. Understand the principles of designing robust and scalable database schemas.

• Indexes: Deep dive into indexing, how indexes speed up queries, the different types of indexes, and when to use them.

• Transactions: Understand ACID properties (Atomicity, Consistency, Isolation, Durability) and how to write transaction-safe SQL.

• Optimization: Learn how to optimize SQL queries, understand query plans, and use tools to profile and monitor database performance.

• Stored Procedures & Triggers: While not always used in modern data engineering pipelines, having knowledge about them can be beneficial.

3. Transitioning to Data Engineering:

• ETL Processes: Given your SQL knowledge, you'd often be writing SQL for ETL processes. Understand how data is Extracted, Transformed, and Loaded into data warehouses.

• Data Warehousing: Learn about data warehousing solutions like Amazon Redshift, Google BigQuery, and Snowflake. Understand how they differ from traditional databases.

• Data Lakes: Given the rise of unstructured data, understand how data lakes work and how they complement data warehouses.

Next Steps:

1. Hands-on Practice: Set up a NoSQL database (like MongoDB) and practice basic CRUD operations. This will give you a feel for how they differ from relational databases.

2. Advanced SQL: Dive into advanced SQL topics. Websites like LeetCode and HackerRank offer SQL challenges that can help you hone your skills.

3. Courses & Books: Consider enrolling in specialized courses or reading books that focus on database internals and advanced SQL.

Remember, the world of databases is vast, and there's always something new to learn. Given your background, you're well poised to dive into the deeper aspects of databases from a data engineering perspective. If you have any specific questions or need resources on any of the topics mentioned, please let me know!