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Vector Search with Relational Databases using PostgreSQL

Skill-Up EdTech Team and Lavanya Thiruvali Sunderarajan

4.5

Based on 24 ratings

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Skill-Up EdTech Team and Lavanya Thiruvali Sunderarajan

With vector databases now powering business competitiveness through super-fast applications such as recommendation engines, it’s no surprise that the vector database market is set to grow 23% CAGR by 2032 (Markets and Markets).

This micro course gives aspiring data scientists, ML engineers, gen-AI engineers, software developers, and other data-oriented roles the in-demand skills for performing vector searches in relational databases.

Businesses use vector search with relational databases to improve information retrieval via advanced similarity matching. You’ll gain hands-on experience working with PostgreSQL as your relational database platform and Python and JavaScript to vectorize data, create embeddings and collections, and load data, including bulk insertion techniques. Plus, you’ll provide similarity search recommendations using techniques such as cosine similarity.

This micro course is part of the IBM Vector Database Fundamentals specialization, designed for professionals building on their NoSQL and relational database experience to work with vector databases.

So, enroll today and get set to power your career with highly sought-after relational vector database skills.

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What's inside

Syllabus

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Vector databases are becoming highly valued, with a projected CAGR of 23% by 2032, making this specialization crucial for professionals seeking to stand out in the industry

The specialization is especially valuable for professionals with backgrounds in NoSQL and relational databases, providing a seamless transition into the realm of vector databases

The hands-on experience with PostgreSQL, Python, and JavaScript provides learners with practical skills in vectorizing data, creating embeddings and collections, and implementing similarity search

The module on Vector Search Practices for SQL Databases provides a solid foundation in implementing vector searches using relational databases, a highly relevant topic in today's data-driven era

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Reviews summary

Practical vector search with postgresql

According to students, this course offers a largely positive and highly practical introduction to integrating vector search capabilities directly within existing PostgreSQL databases. Learners frequently highlight the hands-on labs as a significant strength, particularly for those using Python for embeddings. The course is seen as directly applicable for data professionals and ML engineers looking to extend their relational database skills. While it provides a solid foundation, some learners noted the Node.js sections felt less developed compared to the Python content. It is most beneficial for those with prior database experience.

Emphasizes practical labs and direct coding for learning.

"The hands-on labs with PostgreSQL, pgvector, and Python were incredibly practical."

"The practical demos, especially with bulk insertion, were very helpful."

"I appreciated the balance between theory and practical application. The Python examples were well-explained."

Offers directly applicable skills for real-world scenarios.

"This course was exactly what I needed to bridge the gap between relational databases and the new world of vector search. The hands-on labs with PostgreSQL, pgvector, and Python were incredibly practical."

"I immediately applied the concepts of similarity search to my current project."

"It truly delivers on its promise. I learned how to set up `pgvector` and perform cosine similarity queries which is directly applicable to my job."

Good basic overview, but limited on advanced topics.

"I wish there were more advanced use cases or optimization tips, but for a micro-course, it's comprehensive."

"It covers the basics but doesn't go very deep into optimization or more complex vector indexing strategies."

"If you're looking for advanced topics, you might need to look elsewhere."

Strong Python content, but Node.js sections are less thorough.

"Some parts felt a bit rushed, especially the Node.js section, but overall, it provided a solid foundation."

"The Python examples were well-explained, but the Node.js section felt a bit like an afterthought."

"I primarily used the Python parts and found them excellent. The Node.js integration was less clear for me."

Best for database pros, challenging for beginners.

"For a beginner in this field, it might be a bit challenging without prior database experience."

"I struggled with this course... felt the pace was too fast, especially for someone who is not already an expert in PostgreSQL."

"It assumes a certain level of familiarity with database concepts, which is fair for the target audience."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Vector Search with Relational Databases using PostgreSQL with these activities:

SQL querying fundamentals recap

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Refreshes basic querying skills, which are foundational to this course

Browse courses on SQL

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Review the syntax of basic SQL queries
Write sample queries to retrieve data
Practice using aggregate functions
Try using subqueries
Explore using joins

Create a comprehensive course notebook

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Organize and synthesize course materials to enhance retention.

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Gather lecture notes, slides, assignments, and other course materials
Organize materials into logical sections and subsections
Annotate materials with your own notes and summaries
Create a table of contents and index for easy navigation

Create a comprehensive study guide

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Consolidate and summarize key concepts from the course for easy reference.

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Gather all course materials (notes, slides, assignments).
Identify and organize main topics and subtopics.
Summarize key concepts and examples in a concise manner.

Three other activities

Expand to see all activities and additional details

Show all six activities

Work through vector similarity examples

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Improve your understanding of vector similarity measures by practicing with examples.

Browse courses on Cosine Similarity

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Review the different vector similarity measures
Calculate the similarity between two vectors using each measure
Interpret the results and identify the most appropriate measure for your application

Build a vector-powered recommendation system

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Apply vector search concepts to create a real-world recommendation engine.

Browse courses on Recommendation Systems

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Design the data model for storing vectorized product data.
Implement vector search functionality using a relational database.
Develop a user interface for browsing and searching products.
Evaluate the performance and accuracy of the recommendation system.

Write a blog post or article on a vector database topic

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Deepen your understanding and showcase your knowledge by creating content on vector databases.

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Choose a specific topic related to vector databases that you are passionate about
Conduct thorough research to gather insights and information
Organize your content into a logical structure with an introduction, body, and conclusion
Write clear and engaging prose that effectively conveys your knowledge

Career center

Learners who complete Vector Search with Relational Databases using PostgreSQL will develop knowledge and skills that may be useful to these careers:

Reading list

We've selected 50 books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Vector Search with Relational Databases using PostgreSQL.

JavaScript

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Comprehensive and well-written guide to JavaScript. It covers all the basics, as well as more advanced topics such as closures and functional programming. It's a great book for anyone who wants to learn JavaScript in depth.

Vector Search with Relational Databases using PostgreSQL

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Reviews summary

Practical vector search with postgresql

Activities

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