Halodoc Technology’s Post

Checkout how we at Halodoc Technology optimized Airflow DAG code here.

This is my portfolio project for Exploratory Data Analysis and Web Scraping using Python.

Checkout our latest blog post where we guide you through creating AWS athena views effortlessly using CDK. Elevate your data querying game! #Athena #CDK #DataEngineering #TechBlog #Python #AWS https://lnkd.in/g5B-zXbq

Handling Missing and Duplicate Data :::: ● Dropping Rows with Null Values: df.na.drop() ● Filling Null Values: df.na.fill(value) ● Dropping Duplicate Rows: df.dropDuplicates() ● Replacing Values: df.na.replace(["old_value"], ["new_value"]) #data #pandas #pyspark #dataengineering #python #aws #azure #etl

No wonder FastAPI makes things very simple. Now let's make things simple for FastAPI and Amazon Web Services (AWS) . Here is my new blog with full code on how to easily create centralized logging for FastAPI and store them on AWS CloudWatch: #cloudwatch #aws #fastapi #logging #monitoring #python https://lnkd.in/d_EtG7ty

🎓 Proud to announce that I've completed the "Learning Apache Airflow" course led by Janani Ravi, a renowned Google Cloud Architect and Data Engineer. This advanced course has been a game-changer for me, providing deep insights into the world of workflow automation. Through this training, I've mastered the art of designing and scheduling intricate workflows, managing task dependencies, and automating batch processes—all within the robust framework of Apache Airflow. I've learned to program these workflows programmatically using Python, which enhances my ability to create flexible and scalable automation solutions. The course also covered essential features like conditional branching and the mechanisms of catch-up and backfill, which are crucial for maintaining data integrity and consistency in automated tasks. I'm now well-equipped to streamline IT operations and drive efficiency in any tech-driven environment. 🔗 View My Certificate https://lnkd.in/eSEeesXi #ApacheAirflow #ITAutomation #WorkflowManagement #ProfessionalGrowth

🎉 Excited to introduce AtomicExecutionControl, now available on PyPI! 🎉 In our journey towards more efficient and reliable distributed applications, managing atomic operations and preventing race conditions are pivotal challenges, especially across AWS services like Lambda, Fargate, and EC2. That's where AtomicExecutionControl steps in. This Python library is crafted with the complexities of distributed systems in mind, offering a robust solution to ensure that each task in your application is executed exactly once, mitigating risks of duplicate processing and enhancing overall efficiency. Key Features: * Atomic Execution: Guarantee exclusive processing for each task. * Status Management: Real-time tracking of task execution status. * Timeout Handling: Automatic handling of execution stalls and failures. * Easy Integration: Seamlessly integrate with your existing AWS infrastructure. Getting Started is as Simple as: pip install atomic_execution_control Whether you're orchestrating microservices, ensuring data integrity, or managing event-driven workflows, AtomicExecutionControl is designed to make your applications more robust and 'atomic'. I’m looking forward to seeing how it can streamline your projects and solve the critical challenges of task coordination in distributed applications. Your feedback, questions, and contributions are what will help this project grow and improve. Let’s make our distributed systems more reliable together! 👉 Check it out and let me know your thoughts! Also, feel free to reach out if you encounter any issues or have suggestions for improvement. Contributions are always welcome! GitHub: https://lnkd.in/emsuHK_y PyPI: https://lnkd.in/e9uxH_Jv #OpenSource #Python #Serverless #AWS #DataEngineering #CloudComputing #DistributedSystems #AtomicExecution

🚀 Project Update: Building a Serverless REST API with AWS Lambda, API Gateway, and DynamoDB Using Python 🚀 I’m thrilled to share my latest project, where I developed a serverless REST API using key AWS services: AWS Lambda for efficient, scalable backend processing API Gateway to securely expose the API DynamoDB for high-performance, noSQL data storage Using Python, I built endpoints that perform seamless CRUD operations, making it a highly available and cost-effective solution. This project allowed me to deepen my skills in cloud-native application development and serverless architecture. 🔗 Check out the project here: https://lnkd.in/gzmxb-k6 Looking forward to applying these skills in future projects and connecting with others in the cloud community! 🌐📊 #AWS #Serverless #Python #CloudComputing #APIGateway #DynamoDB #AWSLambda #RESTAPI #Project

🔹 𝗠𝘂𝘀𝘁-𝗞𝗻𝗼𝘄 𝗣𝘆𝘁𝗵𝗼𝗻 𝗣𝗮𝗰𝗸𝗮𝗴𝗲𝘀 𝗳𝗼𝗿 𝗔𝗪𝗦 𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝘀 Python is the go-to language for data engineers, especially when working on AWS. Here’s a list of essential Python packages that enhance data processing, automation, and machine learning on AWS: 1️⃣ 𝘽𝙤𝙩𝙤3: AWS’s official SDK for Python, allowing seamless access to AWS services like S3, DynamoDB, Lambda, and more. Essential for automating AWS operations. 2️⃣ 𝙋𝙖𝙣𝙙𝙖𝙨: Provides powerful data structures for efficient data analysis and manipulation. Perfect for preparing data before loading it into AWS services like Redshift. 3️⃣ 𝙋𝙮𝙎𝙥𝙖𝙧𝙠: Spark’s Python API, useful for big data processing on Amazon EMR. Scales data analysis across large datasets in distributed environments. 4️⃣ 𝙎𝙌𝙇𝘼𝙡𝙘𝙝𝙚𝙢𝙮: A SQL toolkit and ORM that integrates well with AWS RDS and Redshift, simplifying database interactions and data transformations. 5️⃣ 𝙨3𝙛𝙨: Simplifies file operations on S3, allowing direct file reading/writing from S3 buckets, which is invaluable for data preprocessing. 6️⃣ 𝘼𝙒𝙎 𝙇𝙖𝙢𝙗𝙙𝙖 𝙋𝙤𝙬𝙚𝙧𝙩𝙤𝙤𝙡𝙨 𝙛𝙤𝙧 𝙋𝙮𝙩𝙝𝙤𝙣: A set of utilities that make developing Lambda functions easier, with pre-built logging, tracing, and metrics collection. 7️⃣ 𝘿𝙖𝙨𝙠: A parallel computing library that scales well on AWS EC2 and EMR. Ideal for handling larger-than-memory datasets and distributed processing. 8️⃣ 𝙍𝙚𝙙𝙨𝙝𝙞𝙛𝙩-𝙎𝙌𝙇𝘼𝙡𝙘𝙝𝙚𝙢𝙮: Extends SQLAlchemy to work specifically with Redshift, making it easier to query and load data directly into Redshift tables. 9️⃣ 𝘼𝙥𝙖𝙘𝙝𝙚 𝘼𝙞𝙧𝙛𝙡𝙤𝙬 𝙬𝙞𝙩𝙝 𝘼𝙒𝙎 𝙄𝙣𝙩𝙚𝙜𝙧𝙖𝙩𝙞𝙤𝙣𝙨: Airflow is widely used for orchestrating ETL workflows. AWS provides managed Airflow with built-in integrations for seamless scheduling and monitoring. 🔟 𝙎𝙘𝙧𝙖𝙥𝙮: A web scraping library that can pull in data from external sources, ready to be processed and loaded into AWS databases or data lakes. #AWSDataEngineering #PythonForData #DataEngineeringTools #CloudAutomation #BigData #ETLProcesses #S3 #DataPipeline #AWSLambda #Redshift #DataAnalysis #ServerlessPython #DataIntegration #Airflow #AWSAutomation #CloudComputing #DataPreparation #MachineLearning #PythonPackages #CloudArchitecture

Generally Available: Index Advisor in Azure Cosmos DB helps optimize your index policy for NoSQL queries Reduce RU-costs and improve query speeds with Index Advisor! Learn more in this article. https://lnkd.in/eMSJWS2b