Subrat's Technical Blog

  In the rapidly evolving fields of artificial intelligence (AI), machine learning (ML), and data engineering, the need for efficient data storage and retrieval systems is paramount. Vector databases have emerged as a critical solution for managing the complex, high-dimensional data that these technologies often rely on. Here, we explore seven vector databases that every AI/ML/data engineer should be familiar with, highlighting their unique features and how they support the demands of modern data-driven applications. 1. Milvus Milvus  is an open-source vector database designed to handle large-scale similarity search and vector indexing. It supports multiple index types and offers highly efficient search capabilities, making it suitable for a wide range of AI and ML applications, including image and video recognition, natural language processing, and recommendation systems. Key Features: Highly scalable, supporting billions of vectors. Supports multiple metric types for similarity searc

  [PART 1] → [Beginner — Intermediate] SQL ## Question 1. Difference between Truncate, Drop, Delete. DROP  statement can be used to remove any database objects like tables, views, functions, procedures, triggers etc. Delete  is a DML statement hence we will need to commit the transaction in order to save the changes to database. Whereas  truncate and drop are  DDL statements hence no commit is required. For example: Below statement will delete only the records from employee table where the name is ‘Tanya’ DELETE FROM employee WHERE name = ‘Tanya’; COMMIT; Below statement will delete all records from the employee table. DELETE FROM employee; COMMIT; Below statement will also delete all the records from the employee table. No commit is required here. TRUNCATE TABLE employee; ## Question 2. Difference between RANK, DENSE_RANK and ROW_NUMBER window function. RANK()  function will assign a rank to each row within each partitioned result set. If multiple rows have the same value then each of