Henri Aghaei’s Post

SQL Vector Databases Are Shaping the New LLM and Big Data Paradigm #generativeai #llm #vectordatabases #sql #bigdata https://lnkd.in/gg_KbqKU

SQL Vector Databases Are Shaping the New LLM and Big Data Paradigm https://lnkd.in/gw6tUGDE #GenAi #llm #vectordatabase

https://lnkd.in/eEcfnkdZ #SQL #Vector Databases Are Shaping the New #LLM and #BigData Paradigm

#SQL Vector Databases Are Shaping the New #LLM and #BigData Paradigm, https://lnkd.in/dJXmkF34 #data #database #vectordatabase #DataScience #DataManagement

Combining vector databases with SQL can provide the accuracy and performance required to build modern production-level GenAI applications. #Database #SQL #LargeLanguageModels by Linpeng Tang thanks to MyScale

SQL Vector Databases Are Shaping the New LLM and Big Data Paradigm... Combining vector databases with SQL can provide the accuracy and performance required to build modern production-level GenAI applications.

Master SQL for Data Science: A Comprehensive Bootcamp Guide In the evolving landscape of data science, SQL (Structured Query Language) emerges as an indispensable tool, bridging the gap between data storage and insightful analysis. This section delves into the pivotal role of SQL in data-driven decision-making processes and its ubiquitous application across various industries. Read more

As someone who frequently works with data, I often have to understand new data sources or refresh my memory about the data's structure. For tabular data, we have SQL tools to enforce the data structure and UML to help us understand the structure. We are not always so lucky with nested data like JSON, API or NoSQL data. Neither data discipline nor documentation is guaranteed. I opted for the "data as its documentation" approach as the data consumer. I created functions to help us navigate through the structure of nested data and find relevant data as we may. By focusing on the structure, not only can I quickly scan through the dataset by leaving the details aside, I can also use statistics to analyse the schema discipline and find the most promenent data. I have cleaned up and refactored these function for anyone to use. I am sure this can speed up your data exploration phase and help you deliver the data with high confidence. https://lnkd.in/eYM9EsSf

Last week, we shared an explanation of what data stores are and why it is crucial for aspiring data engineers to learn about them. Today, we will dive into one of the most well-known types of data stores, databases. But first, let’s do a quick recap: A data store is pretty much what it is: a storage place for data. We know that sounds very vague, and indeed, anything can be a data store, from a database of spreadsheets in your computer to a paper file. Databases are data stores with data that is organized, formatted, and stored according to specific rules. Even though they’ve been around for decades, they are continually evolving, thus providing an exciting subject to study. We usually classify them based on the way they store data and the operations they support. Let’s have a look at the most common ones! ➡️Relational databases are databases in which each piece of information has a relationship with every other piece of information. They organize data into two-dimensional tables with a variable number of rows and a fixed set of columns and often use Structured Query Language (SQL) to create, update, retrieve, or delete data. ➡️Non-relational (or NoSQL) databases use different data structures than relational databases, which makes some of their operations faster. Unlike relational databases, they organize data into documents, graphs, and collections, which makes them more flexible and able to store more complex structures. Examples of NoSQL databases include the key-value and object-oriented databases, among many others. Check out our images below to find out more about these! Of course, there are many other types of databases out there. If they are something you are fascinated by, then our data engineering learning program might just be the place for you. Find out more about the program and apply on our official website: https://tutorrio.com/ #datastores #databases #tutorrio #dataengineering #computerscience #nosql #sql #relationaldatabases #objectoriented #distributeddatabase

Database Mastery: SQL vs. NoSQL, Which One and When? Hey data enthusiasts! 🤓 When it comes to databases, the eternal question arises: SQL or NoSQL? 🤔 SQL (Structured Query Language) is the OG database, known for its structured data and rigid schema. It's like a tidy filing cabinet, where everything has its place. NoSQL (Not Only SQL), on the other hand, is the new kid on the block. It's more flexible, allowing for unstructured data and dynamic schemas. Think of it as a messy but spacious attic, where you can store anything you want. So, which one should you choose? It depends on your data and application needs. Here's a handy guide: SQL: Structured data (e.g., customer records, financial transactions) Relational data (e.g., customers linked to orders) High data integrity and consistency NoSQL: Unstructured data (e.g., social media posts, sensor readings) Non-relational data (e.g., documents, graphs) High scalability and flexibility Example: If you're building a banking system, SQL is a better choice for storing customer accounts and transactions. But if you're developing a social media platform, NoSQL is more suitable for handling unstructured user posts and connections. Remember: Both SQL and NoSQL have their strengths and weaknesses. Choose the right database for your specific requirements. Don't be afraid to mix and match different databases for optimal performance. Bonus Tip: Check out this awesome blog for more in-depth info: [Insert Blog Link] #DatabaseMastery #SQLvsNoSQL #DataScience #BigData