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/…

The landscape of vector databases is shifting rapidly, influencing the way engineering teams approach AI and data pipelines.

As organizations grapple with optimizing architecture for generative AI, understanding the nuances of vector databases becomes critical.

🗓️ Don't miss out! This 𝗪𝗲𝗱𝗻𝗲𝘀𝗱𝗮𝘆, 𝗦𝗲𝗽𝘁𝗲𝗺𝗯𝗲𝗿 𝟮𝟳𝘁𝗵, at 𝟭𝟬:𝟬𝟬 𝗮𝗺 𝗣𝗗𝗧, join the esteemed Sanjeev Mohan, former VP at Gartner, for a complimentary and enlightening session.

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

Gain valuable knowledge on constructing AI pipelines and creating Vector Embeddings.

Your journey into the depths of AI understanding begins here! 🚀


𝗪𝗵𝗮𝘁 𝗬𝗼𝘂’𝗹𝗹 𝗟𝗲𝗮𝗿𝗻:

• Technical deep-dive into vector embeddings and their pivotal role in modern AI architectures.
  
• Key considerations in constructing efficient AI pipelines and integrating vector search capabilities.
  
• Best practices in evaluating and selecting vector-enabled databases for scalable applications.
  
• Architectural and performance nuances of leading vector databases in the market.
  
• Strategies to ensure seamless deployment, security, and operational excellence with vector databases.

I have posted a comprehensive road map to becoming a data engineer. Your feedback is highly appreciated - https://www.linkedin.com/posts/brijpandeyji_%3F%3F-%3F%3F%3F%3F%3F-%3F%3F%3F%3F%3F%3F%3F-%3F%3F-%3F%3F-activity-7114220499018072064-Trde

Free Full Stack Certifications Courses to try in 2023:

🔸Python
https://freecodecamp.org/learn/scientific-computing-with-python/

http://developers.google.com/edu/python

🔸JavaScript
https://hackerrank.com/skills-verification/javascript_intermediate

http://learn.microsoft.com/training/paths/build-javascript-applications-typescript

🔸SQL
https://hackerrank.com/skills-verification/sql_advanced

http://online.stanford.edu/courses/soe-ydatabases0005-databases-relational-databases-and-sql

🔸Data Science
https://mylearn.oracle.com/ou/learning-path/become-an-oci-data-science-professional-2023/121944

http://cognitiveclass.ai/courses/data-science-101

🔸HTML, CSS
https://freecodecamp.org/learn/2022/responsive-web-design

http://cs50.harvard.edu/web/

🔸DevOps
https://mylearn.oracle.com/ou/learning-path/become-an-oci-devops-professional-2023/121756

🔸Machine Learning
https://freecodecamp.org/learn/machine-learning-with-python

http://developers.google.com/machine-learning/crash-course

🔸Java
https://data-flair.training/courses/free-java-course/

http://learn.microsoft.com/shows/java-for-beginners/

🔸Neo4j
https://graphacademy.neo4j.com/courses/neo4j-certification/

🔸React
https://hackerrank.com/skills-verification/react_basic

🔸Angular
https://hackerrank.com/skills-verification/angular_intermediate

🔸C#
http://learn.microsoft.com/users/dotnet/collections/yz26f8y64n7k07
https://hackerrank.com/skills-verification/c_sharp_basic

🔸Go
https://hackerrank.com/skills-verification/golang_intermediate

🔸Security
https://mylearn.oracle.com/ou/learning-path/become-a-cloud-security-professional-2023/121923

🔸Backend (API Dev)
https://freecodecamp.org/learn/back-end-development-and-apis/

🔸Software Engineering
http://techdevguide.withgoogle.com/paths/principles/

🔸DSA
http://techdevguide.withgoogle.com/paths/data-structures-and-algorithms/

🔸OS, Networking
http://ocw.mit.edu/courses/6-033-computer-system-engineering-spring-2018/

🔸Interview Prep (FAANG)
http://techdevguide.withgoogle.com/paths/interview/

Anomaly detection, in the simplest terms, is identifying data points, events, or observations that deviate from the expected norm or pattern in a dataset.

Imagine you're looking at a pattern of dots; anomaly detection is like finding the one dot that is out of place - either too far from the others, a different color, size, etc.


𝗪𝗵𝗲𝗿𝗲 𝗮𝗻𝗱 𝗪𝗵𝘆 𝗗𝗼 𝗪𝗲 𝗨𝘀𝗲 𝗔𝗻𝗼𝗺𝗮𝗹𝘆 𝗗𝗲𝘁𝗲𝗰𝘁𝗶𝗼𝗻?

The primary purpose is to identify unusual patterns that do not conform to expected behavior.

It's crucial for preemptively identifying issues, ensuring quality, safeguarding against fraud, and protecting systems from potential threats.

𝗝𝗼𝗶𝗻 𝗺𝗲 for a fun and easy-to-understand session on using 𝗞𝗮𝗳𝗸𝗮 & Vectors to spot unusual patterns (or anomalies) in massive amounts of data, especially in the Internet of Things (IoT) world!

📅 Save the Date: 𝗧𝗵𝘂𝗿𝘀𝗱𝗮𝘆, 𝗢𝗰𝘁𝗼𝗯𝗲𝗿 𝟱𝘁𝗵 𝟭𝟬:𝟬𝟬𝗮𝗺 𝗣𝗗𝗧

👉 𝗥𝗲𝗴𝗶𝘀𝘁𝗲𝗿 𝗵𝗲𝗿𝗲: https://bit.ly/brij-ai
https://www.linkedin.com/posts/brijpandeyji_anomaly-detection-in-the-simplest-terms-activity-7114780214605303809-JXZa?