ZipRecruiter’s Post

Great article by Roi Yarden Building Data pipelines comes with its set of challenges. One of the most important decisions is to understand what patterns suit the business evolving needs and allow for a system to scale. This can prevent very costly mistakes.

Day 7: Level Up: Building Data Pipelines with Python! #DataEngineering #Python Data pipelines automate the data journey. This post explores scripting data manipulation tasks in Python to build your own pipelines: Scripting Data Manipulation: Automate repetitive data tasks using Python code. Data Pipeline Frameworks: Manage complex pipelines and dependencies with tools like Apache Airflow. Benefits of Data Pipelines in Python: Automate data ingestion, cleaning, and transformation Ensure consistent data flow Improve efficiency and scalability Python libraries like pandas and PySpark are your tools for data manipulation within pipelines. This is your journey to becoming a data engineer. Follow us for daily updates and our upcoming post on data warehousing concepts! #data pipelines #bigdata pipelines #pythonprogramming #dataengineering #linkedinlearning

Just wrapped up an enriching journey through the Python Data Science Toolbox (Part 1) course. 🐍📊 Ready to harness the power of data for informed decision-making. Onward to Part 2! 🚀 #DataScience #Python #ContinuousLearning

DATACAMP - Introduction to Functions in Python - Associate Data Scientist in Python track 3h training Defining a function Docstrings Multiple function parameters Nested functions Scope in functions (global, local, built-in) Default and flexible (*args, **kwargs) arguments Lambda functions Error-handling #datacamp #data #python

Unlocked Python's Data Science Magic! 📊✨ Just nailed the "Python Data Science Toolbox (Part 1)" course on DataCamp. From honing essential Python skills to mastering data cleaning for impactful analysis, I'm geared up to wield Python's power for data-driven insights. Let's dive into the world of data science! 🚀🎓 #datasciencejourney #datacamp #python3

Just wrapped up the Python Data Science Toolbox Part 1 on DataCamp! 🚀 Excited to have strengthened my foundational skills in Python for data science. Grateful for the comprehensive learning experience and looking forward to applying these tools in real-world projects. #DataScience #Python #ContinuousLearning

🚀 PYSPARK Challenge - [2] 🌟 Hello #DataEngineers and #PYSPARK enthusiasts! 💻 Sometimes the DAG is so big that it doesn’t fit in the memory anymore, or the number of stages and tasks is very large, which causes that we may get an error: java.lang.StackOverflowError or java.lang.OutOfMemoryError How to handle this scenario? Please do comment if you know any other way to handle this. Feel free to explore, contribute, and share the knowledge! Happy coding! 🚀 -------------------------------------------------------------------------------- #DataEngineering #BigData #CodingChallenge #LinkedInLearning #pyspark #databricks #dataanalytics #datascience #technology #creativity #jobinterviews #innovation #education #TechCommunity #pytechnologies

I'm excited to share that I have completed the Python Data Science Toolbox (I) course at DataCamp ! #Data #DataCamp #Python

🚀 Day 56 of #100DaysOfCode 🚀 📅 [23/03/2024] Today, I decided to reinforce my understanding of basic data structures in Python, specifically focusing on arrays. Arrays are fundamental in programming, serving as a building block for more complex data handling and algorithms. Given their importance, it's crucial to master operations like concatenation, which involves combining arrays to form a new one. To put this into practice, I worked on a simple task where I merged two arrays. Initially, I created two separate arrays: the first containing the elements [1, 2, 3, 4, 5] and the second array with the elements [6, 7, 8]. The goal was to combine these two arrays into one continuous array, extending the sequence of numbers in a logical and ordered manner. The concept of array concatenation might seem straightforward, but it's a powerful tool in data manipulation. This operation is not only pertinent in sorting and storing data but also in preparing data for analysis, where merging datasets is a common requirement. Using Python's ability to handle lists (which I utilized as arrays in this example), I simply appended the second array to the first using the '+=' operator, which is an efficient method to extend the list in place. This operation resulted in a new array of [1, 2, 3, 4, 5, 6, 7, 8], showcasing a seamless integration of both datasets. Reflecting on this activity, it’s clear how such basic operations form the foundation of more complex programming tasks. Whether it's manipulating datasets for analysis or preparing data for algorithms, understanding how to efficiently combine and modify data structures is indispensable. As I continue with my coding challenge, I’m excited to delve deeper into Python and explore more complex data structures and algorithms. Each day is a stepping stone towards becoming more proficient in my coding skills. Sharing this journey on LinkedIn has not only helped me document my learning curve but also connect with like-minded individuals who are on similar learning paths. The encouragement and insights from the community are invaluable as I push forward with the challenge. #PythonProgramming #ArrayManipulation #DataStructures #CodeEfficiency #ProgrammingSkills #TechJourney #CodingProgress #100DaysOfCode #PythonProgramming #DataProcessing #100daysofcodechallenge #PythonProgramming #ObjectOrientedProgramming #100DaysOfCode #CodingChallenge #ProgrammingJourney #SoftwareDevelopment #CodingCommunity #TechSkills #LearnToCode #DeveloperLife #CodeNewbie #CodingAdventure #PythonDeveloper #TechEducation #ProgrammingLanguages #CodeIsArt #ProgrammingTips #LinkedInLearning #OpentoWork #pythondeveloper