David Armando Lopez Noria

The last module I have completed is called "Introduction to Relational Databases". I have seen some concepts such as: 📝 Attributes of relational databases. 📝 Types of database constraints. 📝 Identify records with key constraints. 📝 Keys and superkeys. 📝 Foreign keys. 📝 Serial surrogate key. 📝 Model 1:N relationships with foreign keys. 📝 Referential integrity. Next chapter 🎯"Database Design"

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Checkout what Fabrice Deseyn has to say about implementing data & AI use cases on Databricks and what is takes to become a databricks champion. Glad I can work with such an expert at Cegeka!

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I'm excited to share that I recently completed the Advantages of Gen AI & LLM training! 🚀 This program provided a solid foundation in the Azure Databricks Data Intelligence Platform, showing how it unifies data engineering, warehousing, analytics, BI, and AI within an open and governed lakehouse. I learned how this approach leads to reduced TCO (Total Cost of Ownership) and faster innovation, and explored the deep integration of Azure Databricks with the Microsoft ecosystem. #AzureDatabricks #DataIntelligence #GenAI #LLM #Lakehouse #Databricks

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Hello everyone! This is my friend Matthew, Global Developer Advocate on Databricks, responsible to produce and share great content about Apache Spark, Delta Lake, Unity Catalog e MLflow 🔥 I met him through LinkedIn because of the quality content about Delta and Spark and we started talking via message, in San Francisco I decided to meet him in person. Today I want to talk about something important: ENGLISH! English is one of the most important things you need to learn if you want to have a experience in a global company or experience going to a global conference and connecting with people! Few days ago I received SAD news, someone canceled their enrollment in an MBA that I teach simply because my class slides are in english 🤯 I was very sad, not for me, but for opportunity that this person will lose while running away from this. Other example, yesterday I had the opportunity to meet Bill Zhang, Product Director from Cloudera and if I didn't have English, this would NEVER have been possible. Do you want to have experience working in a global data team? English for yesterday!!! Reflect and think about this! Like, comment and share this post if you want Matthew Powers, CFA in BRAZIL next year! Lets make noise!

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Y, retomando viejas costumbres, vamos con proyectos #Apache The foundation for your next high-performance database. Standard SQL: Industry-standard SQL parser, validator and JDBC driver. Query optimization: Represent your query in relational algebra, transform using planning rules, and optimize according to a cost model. Any data, anywhere: Connect to third-party data sources, browse metadata, and optimize by pushing the computation to the data. https://lnkd.in/dqhMGzU

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I am thrilled to announce that I am now a Databricks Certified Machine Learning Professional! 🎉 Throughout this journey, I've gained valuable skills in MLOps, MLflow, PySpark, and Databricks features. This certification has not only equipped me with the knowledge to build and manage robust machine learning models, but also the ability to deploy and monitor these models in a production environment. #Databricks #MachineLearningEngineering #DatabricksLearning

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Hola a todos! Just back from the #Databricks Data+AI World Tour in #Madrid. This was my first professional conference in Spanish, and it was a great experience to put my language skills to the test 💪. The event centered around Gen AI and Databricks architectures, emphasizing how Databricks makes data and AI accessible. Once your data is in Databricks, taking the next step into Generative AI feels very straightforward. One standout case came from Telefonica, where they’re building an AI agent to handle customer complaints. The agent would analyze calls, retrieve relevant data (like invoices), and prepare a resolution – a practical and impactful use of AI in customer service. And a note for conference organizers: I noticed they were serving beer at lunch, only to realize it was alcohol-free! I’ve heard there’s a Wine Summit next to Web Summit in Lisbon...maybe a Beer Summit could be a future upgrade. 🍻

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Working with Categorical Data in Python #python

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Linear Classifiers in Python #python

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I just passed the Databricks Machine Learning Professional certification 🤓 This certification is great if you are interested in open-source tools: most of the exam is about MlFlow, SparkML, Delta, and - naturally - how it all fits together in the Databricks platform 📊 Studying for it is also quite a smooth experience: the videos and labs at the Databricks academy are super well elaborated and cover all the topics of the exam, and by complementing that with the documentation and a few books you get all you need to pass. My only criticism of Databricks is that the free version of the platform (Databricks Community Edition) is very limited, especially when it comes to machine learning. That may be fine if you are working on a project and have access to a premium/enterprise environment, but even then we can't just "play around" a customer's tenant 😢 If you want more tips about Databricks, feel free to ask or check my study annotations: https://lnkd.in/dpjPQ6Q2 #machinelearning #cloudcomputing

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🔢 𝗣𝘆𝗦𝗽𝗮𝗿𝗸 𝗖𝗵𝗲𝗮𝘁 𝗖𝗼𝗱𝗲 𝗦𝗲𝗿𝗶𝗲𝘀 - 𝗣𝗮𝗿𝘁 𝟳: 𝗪𝗶𝗻𝗱𝗼𝘄 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀 Welcome to Part 7 of the PySpark Cheat Code series! 🚀 Today, we’re diving into window functions—a powerful tool for running calculations across a set of rows related to the current row, often used for ranking, cumulative sums, and moving averages. 🔑 𝗞𝗲𝘆 𝗪𝗶𝗻𝗱𝗼𝘄 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀 𝗶𝗻 𝗣𝘆𝗦𝗽𝗮𝗿𝗸 1️⃣ 𝘿𝙚𝙛𝙞𝙣𝙚 𝙖 𝙒𝙞𝙣𝙙𝙤𝙬 𝙔𝙤𝙪 𝙣𝙚𝙚𝙙 𝙩𝙤 𝙙𝙚𝙛𝙞𝙣𝙚 𝙖 𝙬𝙞𝙣𝙙𝙤𝙬 𝙨𝙥𝙚𝙘𝙞𝙛𝙞𝙘𝙖𝙩𝙞𝙤𝙣 𝙪𝙨𝙞𝙣𝙜 𝙒𝙞𝙣𝙙𝙤𝙬.𝙥𝙖𝙧𝙩𝙞𝙩𝙞𝙤𝙣𝘽𝙮() 𝙖𝙣𝙙 𝙒𝙞𝙣𝙙𝙤𝙬.𝙤𝙧𝙙𝙚𝙧𝘽𝙮(). from pyspark.sql import Window import pyspark.sql.functions as F # Define a window by Location and order by Age window_spec = Window.partitionBy("Location").orderBy("Age") 2️⃣ 𝙍𝙖𝙣𝙠 𝙁𝙪𝙣𝙘𝙩𝙞𝙤𝙣 𝙐𝙨𝙚 𝙧𝙖𝙣𝙠() 𝙩𝙤 𝙖𝙨𝙨𝙞𝙜𝙣 𝙖 𝙧𝙖𝙣𝙠 𝙩𝙤 𝙧𝙤𝙬𝙨 𝙬𝙞𝙩𝙝𝙞𝙣 𝙖 𝙥𝙖𝙧𝙩𝙞𝙩𝙞𝙤𝙣. # Apply rank function over the window df = df.withColumn("rank", F.rank().over(window_spec)) df.show() 3️⃣ 𝙍𝙤𝙬 𝙉𝙪𝙢𝙗𝙚𝙧 𝙐𝙨𝙚 𝙧𝙤𝙬_𝙣𝙪𝙢𝙗𝙚𝙧() 𝙩𝙤 𝙖𝙨𝙨𝙞𝙜𝙣 𝙖 𝙪𝙣𝙞𝙦𝙪𝙚 𝙧𝙤𝙬 𝙣𝙪𝙢𝙗𝙚𝙧 𝙬𝙞𝙩𝙝𝙞𝙣 𝙖 𝙥𝙖𝙧𝙩𝙞𝙩𝙞𝙤𝙣. # Apply row_number function df = df.withColumn("row_number", F.row_number().over(window_spec)) df.show() 4️⃣ 𝘾𝙪𝙢𝙪𝙡𝙖𝙩𝙞𝙫𝙚 𝙎𝙪𝙢 𝘾𝙖𝙡𝙘𝙪𝙡𝙖𝙩𝙚 𝙧𝙪𝙣𝙣𝙞𝙣𝙜 𝙩𝙤𝙩𝙖𝙡𝙨 𝙤𝙧 𝙘𝙪𝙢𝙪𝙡𝙖𝙩𝙞𝙫𝙚 𝙨𝙪𝙢𝙨 𝙬𝙞𝙩𝙝𝙞𝙣 𝙖 𝙥𝙖𝙧𝙩𝙞𝙩𝙞𝙤𝙣. # Cumulative sum of Age df = df.withColumn("cumulative_sum", F.sum("Age").over(window_spec)) df.show() 🔍 Example Output: +-----------+--------+-------------+----+----------+--------------+ | Name|Age| Location|rank|row_number|cumulative_sum| +-------+---+-------------+----+----------+--------------+ |Charlie| 25| Chicago| 1| 1| 25| | Alice| 28| New York| 1| 1| 28| | Bob| 32|San Francisco| 1| 1| 32| +-------+---+-------------+----+----------+--------------+ 🔥 Window functions are essential for performing complex operations on large datasets. Stay tuned for Part 8, where we’ll cover handling missing data in PySpark! #PySpark #BigData #DataEngineering #DataScience #Spark #Python #TechCheatCode #WindowFunctions #DataAnalysis

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Open Letter to Peru: Control Your AI Future!: I spent a fabulous week in Peru, keynoting the 2024 Data & AI Summit, lecturing at the University of Technology and Engineering (UTEC), and meeting many marvelous folks curious to learn about the role that AI can play in their personal and professional lives. This journey has motivated me to share my thoughts on what… Read More »Open Letter to Peru: Control Your AI Future! The post Open Letter to Peru: Control Your AI Future! appeared first on Data Science Central. #DataScience #BigDataAnalytics #AI

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I'm happy to share that I've passed the exam for Databricks Data Engineer Professional Certification! This certification can be challenging and most of the questions are real-world data engineering problems and scenarios. To prepare, It requires hands-on experience and thoroughly review the Databricks and Spark documentation.The exam tested knowledge of key concepts, including: -Lakehouse architecture -Spark Structured Streaming -Data Modeling -Databricks Workflow and Access Control -Testing and Deployment  #DataEngineering #Certification #ProfessionalDevelopment #DataScience #Spark

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I'm thrilled to share some exciting news: I have earned the Databricks Certified Data Engineer Professional certification! 🎉 This achievement marks an important milestone in my journey with Databricks and data engineering. Thank you to everyone who has supported me along the way! #Databricks #DataEngineer #DatabricksLearning

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Thrilled to share that I've earned the Databricks Lakehouse Fundamentals Certificate! This program delves into the integration of ACID transactions and data governance from enterprise data warehouses with the flexibility of data lakes. The result? Seamless BI and ML capabilities on all data. Excited to apply these insights in real-world projects! #Databricks #DataLakehouse #DataGovernance #BI #MachineLearning

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I’ve just completed the Introduction to Snowflake Course on DataCamp! Learning a new skill was quick, interactive, and fun! Plus, everything you need is entirely in-browser. Discover which of the 500+ data and AI courses can help you build a stronger career.

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