For those who might have missed out, here's a summary of my LinkedIn discussions this week: View Weekly Roundup https://www.linkedin.com/posts/brijpandeyji_as-another-week-comes-to-a-close-heres-activity-7106333509073428480-idVr

Which query runs faster ? And why ?

50 Linux commands for our day-to-day work:

1. ls - List directory contents.
2. pwd - Display current directory path.
3. cd - Change directory.
4. mkdir - Create a new directory.
5. mv - Move or rename files.
6. cp - Copy files.
7. rm - Delete files.
8. touch - Create an empty file.
9. rmdir - Remove directory.
10. cat - Display file content.
11. clear - Clear terminal screen.
12. echo - Output text or data to a file.
13. less - View text files page-by-page.
14. man - Display command manual.
15. sudo - Execute commands with root privileges.
16. top - Show system processes.
17. tar - Archive files into tarball.
18. grep - Search for text within files.
19. head - Display file's beginning lines.
20. tail - Show file's ending lines.
21. diff - Compare two files' content.
22. kill - Terminate processes.
23. jobs - List active jobs.
24. sort - Sort lines of a text file.
25. df - Display disk usage.
26. du - Show file or directory size.
27. zip - Compress files into zip format.
28. unzip - Extract zip archives.
29. ssh - Secure connection between hosts.
30. cal - Display calendar.
31. apt - Manage packages.
32. alias - Create command shortcuts.
33. w - Show current user details.
34. whereis - Locate binaries, sources, and manuals.
35. whatis - Provide command description.
36. useradd - Add a new user.
37. passwd - Change user password.
38. whoami - Display current user name.
39. uptime - Show system runtime.
40. free - Display memory status.
41. history - List command history.
42. uname - Provide system details.
43. ping - Check network connectivity.
44. chmod - Modify file/directory permissions.
45. chown - Change file/directory owner.
46. find - Search for files/directories.
47. locate - Find files quickly.
48. ifconfig - Display network interfaces.
49. ip a - List network interfaces succinctly.
50. finger - Retrieve user information.

Learn how to 𝗕𝘂𝗶𝗹𝗱 𝗮𝗻 𝗔𝗜 𝗦𝘁𝗼𝗰𝗸 𝗠𝗮𝗿𝗸𝗲𝘁 𝗖𝗵𝗮𝘁𝗯𝗼𝘁 𝘂𝘀𝗶𝗻𝗴 OpenAI for Free

🗓️ Join me on 𝗪𝗲𝗱𝗻𝗲𝘀𝗱𝗮𝘆, 𝗦𝗲𝗽𝘁𝗲𝗺𝗯𝗲𝗿 𝟭𝟯𝘁𝗵, 𝗮𝘁 𝟭𝟬:𝟬𝟬 𝗮𝗺 𝗣𝗗𝗧 for an insightful and FREE session that will teach you how to create a stock market chatbot with OpenAI.

👉 𝗥𝗲𝗴𝗶𝘀𝘁𝗲𝗿 𝗵𝗲𝗿𝗲: https://bit.ly/brij-ai

In this hands-on session, you'll discover:

💰 The fundamentals of OpenAI and its application in the FinTech sector

🤖 Step-by-step guide to building a voice-activated stock market advisor chatbot

⚙️ Best practices for ensuring your chatbot is both efficient and effective

📈 Real-world use cases and success stories of AI-driven financial tools

This session is perfect for software engineers, developers, data professionals, or anyone interested in leveraging AI for automating things!

Don't forget to understand these essential SQL topics if you're just starting out.

1. SQL Basics
- SELECT Statement:
It's like asking the database for specific information.
- FROM Clause:
Tells the database where to look for that information.
- WHERE Clause:
Filters out the stuff you don't need.
- ORDER BY Clause:
Arranges the results in a specific order.

2. Data Manipulation
- INSERT: Adds new data.
- UPDATE: Changes existing data.
- DELETE: Removes data.
- MERGE: Combines these actions.

3. Data Definition
- CREATE TABLE: Makes a new table.
- ALTER TABLE: Edits an existing table.
- DROP TABLE: Deletes a table.
- INDEXES: Helps with finding data quickly.

4. Constraints
- PRIMARY KEY: Ensures each row is unique.
- FOREIGN KEY: Keeps data relationships intact.
- UNIQUE: Ensures values are unique.
- DEFAULT: Sets a value if none is given.

5. Joins (Really Important)
- INNER JOIN: Combines data from different tables.
- LEFT JOIN: Gets all data from one table and matching data from another.
- RIGHT JOIN: Opposite of LEFT JOIN.
- FULL JOIN: Gets data if it's in either table.
- SELF JOIN: Links data within the same table.

6. Subqueries:
- Correlated Subqueries: Subqueries connected to the main query.
- Scalar Subqueries: Subqueries that return a single value.
- Subquery in FROM Clause: Using a subquery as a table.

7. Aggregation Functions:
- SUM, COUNT, AVG, MAX, MIN: Math on groups of data.
- GROUP BY: Groups data before doing math.
- HAVING: Filters groups based on math results.

8. Views:
- CREATE VIEW: Makes a pretend table.
- ALTER VIEW: Changes the pretend table.
- DROP VIEW: Deletes the pretend table.

9. Transactions:
- BEGIN TRANSACTION, COMMIT, ROLLBACK: Ensures data stays safe and consistent.
- ACID Properties (Important): Rules for safe transactions.

10. Database Security:
- GRANT and REVOKE: Decides who can do what.
- Roles: Groups of permissions for users.

11. Normalization (Important):
- 1NF, 2NF, 3NF, BCNF, 4NF:
Ways to organize data for efficiency and accuracy.

12. Indexes:
- Clustered vs. Non-Clustered Indexes: Different ways to find data quickly.

13. Database Management Systems (DBMS):
- Different software tools for working with databases, like MySQL etc.

🔝💻 Top 10 Websites for Coding Practice:

🚀 Hackerrank.com
💡 Leetcode.com
⚔ Codewars.com
🏋️ Exercism.org
🌀 Codeforces.com
🌍 Hackerearth.com
🏆 Topcoder.com
⏲️ Coderbyte.com
🧮 Projecteuler.net
🍽️ Codechef.com

Complete Linux File System [Explained]:

📁 /
∟ 📄boot
∟ 📁bin
∟ 📄ls
∟ 📄mkdir
∟ 📁dev
∟ 📄sda
∟ 📁etc
∟ 📄hostname
∟ 📄passwd
∟ 📄nginx .conf
∟ 📁home
∟ 📁user1
∟ 📄.bashrc
∟ 📁user2
∟ 📄notes.txt
∟ 📄.bashrc
∟ 📁lib
∟ 📄libcrypto .so
∟ 📄libssl .so
∟ 📁mnt
∟ 📁opt
∟ 📁app1
∟ 📄app1_executable
∟ 📁app2
∟ 📄app2_executable
∟ 📁proc
∟ 📁root
∟ 📁sbin
∟ 📄init
∟ 📄shutdown
∟ 📁srv
∟ 📁sys
∟ 📁tmp
∟ 📁usr
∟ 📁bin
∟ 📄gcc
∟ 📄python
∟ 📁include
∟ 📁lib
∟ 📄libncurses .so
∟ 📁local
∟ 📁bin
∟ 📄custom_app
∟ 📁lib
∟ 📄libcustom_lib .so
∟ 📁share
∟ 📁var
∟ 📁log
∟ 📄syslog
∟ 📄nginx .log
∟ 📁www
∟ 📁html
∟ 📄index .html

---------------------------

1. /boot: This directory contains essential files for booting the system.

2. /bin: Basic system binaries reside here, such as common command-line utilities like ls, mkdir, and cp.

3. /dev: This directory contains device files that represent various devices connected to the system, such as hard drives (`sda`, sdb`) and pseudo devices like `null.

4. /etc: Configuration files for the system and installed applications are stored here. Examples include fstab (filesystem table), hostname (system's hostname), passwd (user account information), sudoers (sudo configuration), and nginx .conf (configuration for the Nginx web server).

5. /home: User home directories are typically found here. Examples include user1, user2, and user3, each with their files and settings.

6. /lib: Shared libraries (similar to Windows DLLs) that programs can use are stored here. Examples are libcrypto .so and libssl .so.

7. /mnt: This directory is often used as a mount point for temporary filesystems.

8. /opt: Additional software packages and applications that are not part of the core system can be installed here. Each package may have its own subdirectory, like app1 and app2.

9. /proc: A virtual filesystem that provides information about running processes and system status.

10. /root: The home directory for the root user.

11. /sbin: System binaries essential for system administration, like init (the first process) and shutdown (to shut down the system).

12. /srv: This directory is typically used for data served by the system.

13. /sys: Another virtual filesystem that provides information about kernel and devices.

14. /tmp: Temporary files are stored here. They are usually cleared on system startup.

15. /usr: This directory contains user programs and data.

- /usr/bin: User-level command binaries.

- /usr/include: Header files for C/C++ libraries.

- /usr/lib: Libraries for programming and software.

- /usr/local: Software manually installed by the system administrator.

16. /var: Variable data that changes frequently.

Overall, this file system structure reflects a standard layout found in many Linux distributions, with key directories serving specific purposes, from system binaries to user files, libraries, configuration, and temporary data. Keep in mind that while this is a general representation, individual distributions might have variations or additional directories based on their design and purpose.

If you want to learn Kafka and Spark in shortest possible time , follow these steps -

### Kafka

1. Start with Confluent:
- I'd suggest checking out Confluent. Here’s the link: [https://www.confluent.io/](https://www.confluent.io/). They've built their platform around Kafka, and it's a great place to begin.
- You can easily spin up a cluster there and use their datagen source to experiment with mock data. What's cool is they’re offering $400 in free credits for newbies, and they have a free tier called the "Basic" tier.

2. Certification:
- Once you're comfortable, you might want to think about getting certified. The Certified Kafka Developer certification from Confluent can be a real feather in your cap. Here's where you can find more about it: [https://www.confluent.io/certification/](https://www.confluent.io/certification/).

### Spark

1. Databricks Community Edition:
- For Spark, I'd advise you to look into the Databricks Community Edition. It’s free for non-commercial projects. Here’s the link to sign up: [https://community.cloud.databricks.com/](https://community.cloud.databricks.com/). When you're signing up, if they ask for your preferred platform service, there’s a kinda hidden option saying "I don't have any of those." Click that to ensure you’re on the free usage path.

2. Local Spark Setup:
- Alternatively, if you prefer hands-on, local setups, you can actually get Spark running on your computer. It’s a bit technical, but it’s a solid choice if you want everything on your machine. And hey, you can even use tools like Jupyter to interact with it.

3. Spark on Google Colab:
- Another neat trick I found is setting up Spark on Google Colab. Google Colab allows you to use notebooks for data tasks, and you can set up Spark with a few script commands. A quick online search will give you step-by-step instructions for this.

### A Quick Tip:

Once you have your environments ready, maybe grab some datasets from places like Kaggle or UCI Machine Learning Repository. It's always fun and educational to have real data to play around with.

I genuinely hope this helps you dive into Kafka and Spark. If you have any questions or get stuck somewhere, don’t hesitate to ask. All the best with your learning journey!

Great news for those who have been asking - the recording of the tutorial on building an AI stock market chatbot with OpenAI is now available on-demand for a limited time!

Many of you have reached out via DM asking how to access this tutorial after missing the live session. For the next few days, you can dive into the full webinar recordings here:

https://bit.ly/brij-data

In this hands-on tutorial, you'll discover:

💡 How OpenAI is transforming finance

🤖 Step-by-step guidance to create a voice-activated chatbot

⚙️ Best practices for an efficient and effective AI

📈 Real-world examples of AI improving finance

I'd like to offer some insights from my path to becoming a Data Engineer. These tips are applicable for anyone aiming for this role. Let's keep things straightforward.

1. Data Engineering Basics: At its core, it's about efficiently moving and reshaping data from one place/format to another.
2. Be Curious: The field is vast. Dive deep, ask questions, and always be in the mode of learning and experimenting.
3. Master Data: Understand the intricacies of data types, where they originate, and how they're structured.
4. Programming: Grasping a language is crucial. If you're unsure, start with Python – it's versatile and widely used in the industry.
5. SQL: A timeless tool for querying databases. Mastering SQL will empower you to work with data across various platforms.
6. Command Line: Familiarizing yourself with command line operations can save a lot of time, especially for quick and repetitive tasks.
7. Know Computers: A basic understanding of how computers communicate and process information can guide better data engineering decisions.
8. Personal Projects: Practical experience is invaluable. Start projects, learn from them, and showcase your work on platforms like GitHub.
9. APIs and JSON: Many modern data sources are API-based. Understanding how to extract and manipulate JSON data will be a daily task.
10. Tools Mastery: Get proficient with your primary tools, but stay updated with emerging technologies and platforms.
11. Data Storage Basics: Know the difference and use-cases for Databases, Data Lakes, and Data Warehouses. Understand the distinction between OLTP (online transaction processing) and OLAP (online analytical processing).
12. Cloud Platforms: The cloud is the future. AWS, Azure, and GCP offer free tiers to start experimenting.
13. Business Acumen: A data engineer who understands business metrics and their implications can offer more value.
14. Data Grain: Dive deep into datasets to understand their finest level of detail. It aids in more precise querying and analytics.
15. Data Formats: Recognizing main data formats (like JSON, XML, CSV, SQLite, Database) will help you navigate different datasets with ease.

🗓️ Join me on Monday, 𝗦𝗲𝗽𝘁𝗲𝗺𝗯𝗲𝗿 𝟭8𝘁𝗵, 𝗮𝘁 𝟭𝟬:𝟬𝟬 𝗮𝗺 𝗣𝗗𝗧 for an insightful and FREE session that will teach you how to build a realtime analytics application using Kafka + AI

👉 𝗥𝗲𝗴𝗶𝘀𝘁𝗲𝗿 𝗵𝗲𝗿𝗲: https://bit.ly/brij-ai

Don't just learn theory - get hands-on practice with code and live examples.

If you're a developer, data professional or anyone eager to harness the power of OpenAI

with Kafka for real-time analytics, this is an event you won't want to miss.

What You’ll Learn:

Latest tools and technology for real-time streaming analytics and Generative AI LLMs

Step-by-step guidance on building robust IoT analytics applications with OpenAI and Kafka.

Get access to valuable code snippets and best practices to kickstart your own IoT analytics projects.

Friends - Don't Miss This Hidden Gem 💎

I came across an impressive article that has flown under the radar on using Python tools Dask, Xarray, and Coiled to process 250TB in only 20 minutes for $25!

Check out the details here:

Blog: https://blog.coiled.io/blog/coiled-xarray.html

Code: https://github.com/coiled/examples/blob/main/national-water-model/xarray-water-model.py

This project demonstrates how you can leverage Python for large-scale data processing. You can do this hands on and reference this on your profile or in interviews . Discussing real-world examples like this shows you are familiar with state-of-the-art solutions and can have informed conversations about data engineering challenges and approaches at scale.

5 Coding Courses From Michigan University 👇👇

1. Intro to HTML5
https://coursera.org/learn/html

2. Intro to CSS3
https://coursera.org/learn/introcss

3. Responsive Design
https://coursera.org/learn/responsivedesign

4. JavaScript and JSON
https://coursera.org/learn/javascript-jquery-json

5. The Power of OOP
https://futurelearn.com/courses/the-power-of-object-oriented-programming

Digital Asset Research (DAR) is one of the leading innovative Fintechs that provide ‘clean’, objective pricing and verified volume data for over 3100 digital assets.


However, with 140 million trades supported every day, providing a compelling user experience and separating the signal from the noise in digital asset pricing was not easy.

Join me for an interactive session with Digital Asset Research (DAR) to learn more about how they are able to scale seamlessly from 20 million to 140 million daily orders while still driving a better end-user experience and lower costs.

👉 𝗥𝗲𝗴𝗶𝘀𝘁𝗲𝗿 𝗵𝗲𝗿𝗲: https://bit.ly/brij-ai

Learn more about how DAR was able to drive 1000x better performance, and why they moved from AWS Aurora (MySQL) and Snowflake to a unified data platform.

This event is perfect for IT leaders, application developers, architects, data analysts, and anyone interested in building and scaling SaaS applications, especially within Fintech.

Here are 15 FREE Stanford courses you don't want to miss: 👇

📌1. Data Pre-Processing

🔗 https://edx.org/learn/data-science/harvard-university-data-science-wrangling

📌2. Statistics:

🔗 https://edx.org/learn/data-science/harvard-university-data-science-inference-and-modeling

📌3. Python:

🔗 https://edx.org/learn/python/harvard-university-cs50-s-introduction-to-programming-with-python

📌4. Data Visualization:

🔗 https://edx.org/learn/data-visualization/harvard-university-data-science-visualization

📌5. Machine Learning:

🔗 https://edx.org/learn/machine-learning/harvard-university-data-science-machine-learning

📌6. Computer Science:

🔗 https://pll.harvard.edu/course/cs50-introduction-computer-science

📌7. Game Development:

🔗 https://pll.harvard.edu/course/cs50s-introduction-game-development

📌8. Programming:

🔗 https://pll.harvard.edu/course/cs50s-introduction-programming-scratch

📌9. Web Programming:

🔗 https://learndigital.withgoogle.com/digitalgarage/course/effective-networking

📌10. Artificial Intelligence:

🔗 https://pll.harvard.edu/course/cs50s-introduction-artificial-intelligence-python/2023-05

📌11. AI for Beginners:

🔗 https://microsoft.github.io/AI-For-Beginners/

📌12. Data Science for Beginners:

🔗 https://microsoft.github.io/Data-Science-For-Beginners/#/

📌13. Machine Learning for Beginners:

🔗 https://microsoft.github.io/ML-For-Beginners/#/

📌14. R Programming Fundamentals:

🔗 https://online.stanford.edu/courses/xfds112-r-programming-fundamentals

📌15. Algorithms: Design and Analysis:

🔗 https://online.stanford.edu/courses/soe-ycsalgorithms1-algorithms-design-and-analysis-part-1

Large language models(LLMs) like GPT-4 are changing the AI world , but connecting them to outside data is still difficult.


Enter 𝗟𝗹𝗮𝗺𝗮𝗜𝗻𝗱𝗲𝘅 - a groundbreaking data framework designed specifically for LLMs.

Developed by Jerry Liu, it was conceived to address the challenges of integrating private or domain-specific data into LLM applications.

🗓️ Join me on Monday, 𝗦𝗲𝗽𝘁𝗲𝗺𝗯𝗲𝗿 𝟮𝟱𝘁𝗵, 𝗮𝘁 𝟭𝟬:𝟬𝟬 𝗮𝗺 𝗣𝗗𝗧 for an insightful and FREE session that will teach you how to build a powerful GenAI App with Llama Index

👉 𝗥𝗲𝗴𝗶𝘀𝘁𝗲𝗿 𝗵𝗲𝗿𝗲: https://bit.ly/brijai

🐧🔧 25 Essential Linux Commands 🔧🐧
1. ls (list directory contents) 📂
2. cd (change directory) 🔄
3. pwd (print working directory) 📍
4. cp (copy files or directories) 📋
5. mv (move/rename files or directories) 🚚
6. rm (remove files or directories) 🗑️
7. mkdir (make directories) 🏗️
8. rmdir (remove empty directories) 🚮
9. touch (create empty files) 🖐️
10. cat (concatenate and print file content) 🐱
11. echo (display a line of text) 📢
12. grep (search text using patterns) 🔍
13. man (display manual pages) 📚
14. sudo (execute commands as superuser) 👮
15. chmod (change file permissions) 🔒
16. chown (change file owner and group) 👥
17. ps (report a snapshot of current processes) 📷
18. top (display dynamic real-time process viewer) 🎩
19. kill (terminate processes) ☠️
20. tar (archive files) 📦
21. find (search for files in a directory hierarchy) 🔎
22. nano, vi, emacs (text editors) 📝
23. apt, yum, zypper, dnf (package managers) 📦
24. ssh (secure shell for network services) 🛡️
25. git (version control system) 🌲

GitHub Repositories I wish existed earlier in my career

Covering
• Software Engineering
• Interview Prep
• ML Projects
• Data Engineering Projects

✳️ Complete-Machine-Learning-
• 60 days of Data Science and ML with project Series
• github.com/Coder-World04/…

✳️ Complete-System-Design
• Complete System Design with Implemented Case Studies and Code
• github.com/Coder-World04/…

✳️ Complete-Data-Structures-and-Algorithms
• Complete Data Structures and Algorithms and System Design Series
• github.com/Coder-World04/…

✳️ CML-AI-Research-Papers---Solved
• ML/AI Research Papers Solved
• github.com/Coder-World04/…

✳️ Complete-Data-Engineering
• Complete Data Engineering with Projects Series
• github.com/Coder-World04/…

✳️ Complete-ML-Ops
• Complete ML Ops With Projects Series
• github.com/Coder-World04/…