Mahesh Reddy Palem’s Post

🔍 #ApacheSpark Best Practice Don't use count() when you don't need to return the exact number of rows 👉 When you don't need to return the exact number of rows use: DataFrame inputJson = sqlContext.read().json(...); if (inputJson.takeAsList(1).size() == 0) {...} or if (inputJson.queryExecution.toRdd.isEmpty()) {...} 👉 instead of: if (inputJson.count() == 0) {...} def isEmpty(): Boolean = withScope {    partitions.length == 0 || take(1).length == 0  } #Tagged for better reach Sivakumar Babuji Ravi Kumar Soni  Anand Vaishampayan Rajesh Kumar Maheshpal Singh Rathore    #TradingDataEngineer #MarketAnalysis #DataIntegration #AlgorithmicTrading #DataEngineering #FinancialData #BigDataAnalytics #TradingSystems #DataProcessing #QuantitativeFinance #DataScience #ApacheSpark #BigData #DataEngineering #DataProcessing #Optimization #BestPractices

Studying core concepts, in data engineering is a must when you handle large amount of data. I find reading, is the best way, to quickly grasp when ur on the clock. https://lnkd.in/dZAAVUJP this is one of the websites besides the official documents, for #apachespark which I would like to share ✌️.

"Thrilled to share updates on my latest project: a decision tree implementation! 🌳 Excited to dive into the details of how this powerful tool can streamline decision-making processes. Stay tuned for insights and progress updates! #DataScience #DecisionTree #MachineLearning" #project mushroom classification #highlights The file comprises mushroom features for classification into various categories. Each column represents an object, which has been encoded using label encoding. Utilizing this processed data, a decision tree has been constructed for classification purposes.

#DataGovernance lessons from Beck’s Hybrids.💭 | Learn how Beck’s Hybrids stood up its data governance practice from scratch, in time to take advantage of its growing scale of data.🚀 In this article, author Kash Mehdi, VP of Growth at DataGalaxy, takes us through Beck’s Data Governance Strategy and sheds light on how it rallied the organization, selected its partner, and set itself up for success. 🔗Read the article now to get the three crucial takeaways shared by Beck’s data management team: https://hubs.ly/Q02r3CZk0

🔍 Unveil Data Transparency with Keypup's Dataset Dashboard! Unlock the secrets of your engineering data with Keypup's Dataset Exploration Dashboard. Dive deep into issues, pull requests, commits, and more for unparalleled insights and streamlined software development. 🌟 #DataDepth #EngineeringEmpowerment https://lnkd.in/eqmzzZue

🚀 Exciting Update About the Evolution of Our Demo Data Pipeline! 🚀 Over the past few months, we've been intensively working on automating data analytics using CI/CD. We've also extended it with Meltano and introduced GoodData-dbt integration. Today, we proudly present the culmination of this journey in the CI/CD Data Pipeline Blueprint v1.0. The key concepts of the blueprint include: 🔍 Simplicity: Quick onboarding via docker-compose or Makefile. Easily add new data with a simple pull request. 🔄 Openness: Swap connectors and loaders effortlessly with Meltano, transforming the extraction process. 🤝 Consistency: Stay consistent with Git, code reviews, and seamless rollbacks for smooth data management. 🛡️ Safety: Ensure a secure environment with local development, CI/CD, and isolated environments (DEV, STAGING, PROD). 🚀✨ For more on our data pipeline blueprint, check out the full article: https://hubs.ly/Q02jD1qG0 #DataPipeline #CI/CD #DataAnalytics #Meltano #dbt #GoodData #OpenSource #BlueprintEvolution

The 9th session of MySkill's Fullstack Intensive Bootcamp on Data Analysis covered aggregate functions and the workaround to display selected columns that include aggregate functions. Attached in this post is my learning portfolio from the session. #LearnAtMySkill

I’ve been working on something I’d like to share. After helping small and medium-sized businesses with their data needs, I realized many don’t have access to a full data science team but still need to understand their data. This led me to create LitLytics - a lightweight analytics platform powered by modern LLMs. With LitLytics, anyone can easily perform data analysis — no technical expertise required. From generating an analytics pipeline to getting insights, the whole process is automated, and the cost is just a fraction of a cent per document. In the video I give a quick demo showing how LitLytics: - Automatically generates an analytics pipeline from a simple task description - Allows you to check and customize the pipeline - Supports easy data uploads and execution of the analysis - Provides results and time/cost estimates I’d love to get your feedback: - Would this be useful for your business? - Should I consider making it open source? I appreciate your thoughts! #DataScience #DataAnalysis #LLM #Analytics #OpenSource

🌟 Understanding the key stages within RAG is crucial for building robust applications. Here are the five essential stages you should be aware of: ◾ Loading: Extracting data from various sources and integrating it into your pipeline is the initial step. LlamaHub offers a wide array of connectors to facilitate this process seamlessly. ◾ Indexing: Creating a structured database that enables efficient data querying is paramount. For LLMs, this involves generating vector embeddings and other metadata strategies to enhance data retrieval accuracy. ◾ Storing: Once data is indexed, storing the index and associated metadata becomes imperative to prevent redundant indexing processes in the future. ◾ Querying: Leveraging LLMs and LlamaIndex data structures allows for diverse querying techniques, including sub-queries, multi-step queries, and hybrid approaches tailored to specific indexing strategies. ◾ Evaluation: Assessing the effectiveness of your pipeline in comparison to alternative strategies or after modifications is key. Evaluation provides objective insights into the accuracy, reliability, and speed of query responses. 🔠 Mastering these stages within RAG sets the foundation for optimizing data processing and enhancing the functionality of your applications. #RAG #DataProcessing #ApplicationDevelopment #TechInsights 🚀🔍

Hey LinkedIn fam! 👋 I wanted to share a game-changer in the world of data engineering: Prefect 🌐💡 🔗 Prefect for Data Pipelines Orchestration Are you tired of wrangling with complex data pipelines and orchestrating workflows? Look no further! I've recently delved into the world of Prefect, and I'm blown away by its capabilities in orchestrating seamless and scalable data workflows. 🚀 👨💻 My Experience with Prefect In the past 6 months, I've been using Prefect to orchestrate my data pipelines, and the results speak for themselves. It has streamlined my workflow, reduced debugging time, and given me greater confidence in my data processing. 🚨 Quick Shoutout to the Prefect Community The Prefect community is incredibly supportive and responsive. If you ever run into challenges, there's a wealth of knowledge and assistance just a click away. 🌐 Get Started with Prefect Ready to supercharge your data workflows? Check out Prefect and see the difference for yourself. Don't just manage your data; orchestrate it with Prefect! #DataEngineering #DataOrchestration #Prefect #WorkflowAutomation #DataScience