Anurag Bhardwaj

🌟𝐖𝐞'𝐫𝐞 𝐇𝐢𝐫𝐢𝐧𝐠: 𝐏𝐲𝐭𝐡𝐨𝐧 𝐀𝐏𝐈 + 𝐄𝐥𝐚𝐬𝐭𝐢𝐜𝐬𝐞𝐚𝐫𝐜𝐡 𝐃𝐞𝐯𝐞𝐥𝐨𝐩𝐞𝐫𝐬! TekPillar®🌟 🔧𝐏𝐨𝐬𝐢𝐭𝐢𝐨𝐧 : Python API+ Elastic Search 📍𝐋𝐨𝐜𝐚𝐭𝐢𝐨𝐧 : Bangalore  📅𝐄𝐱𝐩𝐞𝐫𝐢𝐞𝐧𝐜𝐞 : 5+ Years      #KeyResponsibilities: 👉Develop custom search and analytics applications using FastAPI and Elasticsearch. 👉Index data from various sources, including big data systems like Databricks and Snowflake. 👉Query Elasticsearch data and create robust REST APIs. Implement AI/ML use cases and optimize Elasticsearch performance. 👉Create reusable libraries for index templates, custom analyzers, and scripts. 👉Collaborate with data engineers on pipelines and ETL jobs. 👉Guide developers on search, filtering, and aggregation requirements. 👉Research and prototype new Elasticsearch features. 🔗 𝐀𝐩𝐩𝐥𝐲 𝐇𝐞𝐫𝐞:  📞8799447189 | ✉️ [email protected] #Python #Elasticsearch #FastAPI #BigData #Azure #TechJobs #Bangalore #Hiring #tekpillarjobs

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5 Apache Spark Courses to Accelerate Big Data Analytics for Data Scientists - Shiksha: Apache Spark is a valuable tool for data scientists, allowing them to analyze large datasets efficiently. Spark provides an accessible platform ... #bigdata #cdo #cto

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#hiring There are the key skills and experiences highlighted in the job description for data Engineers that you can create blog posts about: 1. Building Data Pipelines: Discuss your experience in designing, developing, and maintaining data pipelines. Include specifics about the tools and technologies you’ve used (e.g., Apache Airflow, AWS Data Pipeline, etc.), challenges you faced, and how you overcame them. 2. Writing SQL Scripts: Share examples of complex SQL queries you've written. Explain how your SQL skills have helped in data extraction, transformation, and loading (ETL) processes. Discuss any optimizations you’ve made to improve query performance. 3. Python Programming: Highlight projects where you used Python for data manipulation, automation, or integration tasks. Mention specific libraries (e.g., Pandas, NumPy, SQLAlchemy) and frameworks you’ve used and describe the outcomes of these projects. 4. Data Warehousing: Describe your experience with data warehousing solutions such as Amazon Redshift, Google BigQuery, or Snowflake. Talk about the design and implementation of data warehouses, your understanding of data modeling concepts, and how you’ve handled large datasets. 5.Exciting Analytics Projects: Provide detailed case studies of analytics projects you’ve worked on. Explain the problem, your approach, the technologies used, and the impact of your work. Highlight any innovative solutions or significant insights derived from your projects. 6. Availability to Start: Although not a technical skill, mentioning your availability to start in a couple of weeks shows your readiness and enthusiasm for the role. In a blog post, you could discuss how you’re preparing for a new role and what excites you about upcoming opportunities. Eyse - https://lnkd.in/dQsUYc59

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Data Engineers required 1. Building Data Pipelines:   - Designing and Implementing Efficient Data Pipelines: A Case Study    - Share an in-depth case study of a data pipeline built, discussing the tools and technologies used, challenges faced, and solutions implemented.   - Best Practices for Data Pipeline Development    - Outline best practices for building robust, scalable data pipelines, including architecture considerations and performance optimization. 2. Writing SQL Scripts:   - Advanced SQL Techniques for Data Engineers    - Discuss complex SQL queries, optimizing SQL performance, and using SQL for data transformation.   - Common Pitfalls in SQL and How to Avoid Them    - Highlight common mistakes made when writing SQL scripts and provide tips on how to avoid them. 3. Writing Python Scripts:   - Automating Data Tasks with Python: Real-World Examples    - Demonstrate the use of Python to automate data processing tasks, including examples of code and its impact on efficiency.   - Python Libraries Every Data Engineer Should Know    - Review essential Python libraries for data engineering, such as pandas, NumPy, and PySpark, with examples of how to use them. 4. Data Warehousing:   - Building and Managing Data Warehouses: Lessons Learned    - Share experiences on setting up and managing data warehouses, including design considerations and tool choices.   - Data Warehousing Strategies for Scalable Analytics    - Discuss strategies for ensuring data warehouses can scale with growing data and user demands. 5. Experience:   - Journey as a Data Engineer: Key Milestones and Learnings    - Reflect on a professional journey, highlighting significant projects and key takeaways from 2+ years of experience.   - How to Transition into Data Engineering: Tips and Resources    - Provide advice for those looking to enter the field, drawing from experiences and challenges faced. 6. Availability:   - While availability to start soon is more of a logistical detail, readiness can be subtly indicated in the introductions or conclusions of the posts, for example, mentioning excitement for new opportunities starting next month. 7. Analytics Projects:   - Driving Business Insights with Data Analytics: Project Showcase**    - Showcase specific analytics projects, explaining the problem, approach, and outcomes.   - The Role of Data Engineering in Successful Analytics Projects    - Discuss the critical role data engineering plays in analytics, emphasizing how these skills contribute to the success of projects. Eyse - https://lnkd.in/dQsUYc59

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One lesser-known, newer feature of spreadsheet software is the growing integration of machine learning (ML) and AI-powered automation. For example, Google Sheets has a feature called ""Explore"" that uses AI to automatically generate insights from your data, such as visualizations, trends, or recommendations, without requiring manual data analysis. Similarly, Excel's ""Ideas"" feature also uses AI to provide data insights by identifying patterns and helping users analyze large datasets quickly. Another innovation is the growing capability of no-code automation tools in spreadsheet software, which allows users to set up workflows, data imports, and integrations with other applications—without needing to write code. This is particularly powerful when handling repetitive tasks. These advancements show how spreadsheet software is becoming more than just a tool for calculations and is now transforming into a sophisticated data analysis and automation platform. #automation #machinelearning #aiintegration #googlesheets #excel #dataanalysis #spreadsheetinnovations

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Important Pyspark Interview Question!! Interviewer: Can you explain what Adaptive Query Execution (AQE) is in the context of PySpark? Candidate: Yes, Adaptive Query Execution (AQE) is a feature introduced in Apache Spark to optimize query execution dynamically based on the runtime statistics of data. It essentially allows Spark to adapt its query execution plans during runtime to improve performance. Interviewer: That's correct. How does AQE work under the hood? Candidate: AQE employs runtime statistics, such as data skewness, data size, and data distribution, to make decisions about query execution. It dynamically adjusts various optimization strategies, such as join strategies, shuffle partitions, and broadcast joins, based on these statistics. For example, if it detects data skewness, it might choose a different join strategy to balance the workload across the cluster. Interviewer: Can you give me an example of how AQE can improve query performance in PySpark? Candidate: Certainly. Let's say we have a join operation between two DataFrames, and during runtime, AQE detects that one of the DataFrames is much smaller than the other. Instead of performing a regular shuffle join, AQE might decide to broadcast the smaller DataFrame to all the nodes in the cluster, reducing the shuffle data and improving performance. Interviewer: That's a good example. What are some of the key benefits of using AQE in PySpark? Candidate: One of the main benefits is improved query performance. By dynamically adjusting execution plans based on runtime statistics, AQE can optimize query performance for various workloads and data distributions. This can lead to faster query execution times and better resource utilization. Additionally, AQE can handle data skewness and other runtime issues more effectively, leading to more stable and predictable query performance. Interviewer: Excellent explanation. #pyspark #bigdata #dataengineer

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Day 12 - 30-Day Data Engineering Interview Preparation Series PySpark (#Day12): 20 PySpark interview questions that can be relevant for a data engineering role 👇 📍 What is PySpark, and how does it differ from Apache Spark? 📍 Explain the architecture of Spark. 📍 How do you initialize a SparkSession in PySpark? 📍 What are RDDs in PySpark, and how do they work? 📍 What are the transformations and actions available in RDDs? 📍 How do you create RDDs in PySpark? 📍 What are DataFrames in PySpark, and how do they differ from RDDs? 📍 How do you perform operations on DataFrames? 📍 Explain the concept of a Dataset in PySpark. 📍 How do you work with SparkSQL in PySpark? 📍 Explain how to perform SQL operations on DataFrames. 📍 What are the advantages of using SparkSQL? 📍 How do you read and write data in various formats (CSV, JSON, Parquet) using PySpark? 📍 Explain the process of handling schema evolution in PySpark. 📍 What is Catalyst Optimizer in PySpark? 📍 How do you optimize a PySpark application? 📍 Explain the concept of lazy evaluation in PySpark. 📍 What is the significance of caching and persistence in PySpark? 📍 How do you handle skewed data in PySpark? 📍 What are broadcast variables and accumulators in PySpark? 🚨 We have launched our most affordable and industry-oriented "Complete Data Engineering 3.0 With Azure" bootcamp and ADMISSIONS ARE OPEN! 🔥 Only 7 days left and 80 seats remaining! 🚀 Enrollment link is in the first comment 👇 Cheers - Grow Data Skills Shashank Mishra 🇮🇳 SHAILJA MISHRA🟢 Sahil Choudhary Shubhankit Sirvaiya Aman Kumar 🙂 #DataEngineering #SQL #Hive #Hadoop #Kafka #Pyspark #AdvancedSQL #DataScience #BigData #TechInterview #InterviewPreparation #CareerGrowth #TechSkills #SQLQueries #azure

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Want to transform your legacy BI platforms to identify patterns and outliers? Amazon QuickSight enables just that with Machine Learning-induced dashboards to let analytics take centerstage. Watch the webinar to know more. https://lnkd.in/gb46Y5er #AmazonQuickSight #BusinessIntelligence #AWS #MachineLearning #Data #ML #BusinessIntelligence #DataAnalytics

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What are the most critical location intelligence use cases for NBFCs? 🤔 💡 Leveraging #spatial awareness unlocks remarkable operations efficiency for NBFCs. Imagine the data-rich insights that can be derived just by eliminating data 📊 silos and overlaying #geospatial intelligence over the disparate data sources. 41% of financial leaders use #locationintelligence for authentication and function optimization. This means there is tremendous scope for #NBFCs to unlock the real value of location intelligence. We've highlighted the top use cases for NBFCs that can accelerate 🚀 #digitaltransformation and optimize the lending process. Here's an #infographic explaining location intelligence use cases for NBFC firms. https://lnkd.in/gvC4CP9v #NBFC #BFSI #Locationintelligence #geospatialinsights #geospatialanalytics #Dista #digitaltransformation

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Data Social: Mumbai Edition is coming up soon! Have you registered yet? Our panelists, Manisha Jaiswal from Snowflake, Sumit Bhatia from kipi.bi, and Atmesh M. from Chalo are all ready to share their insights and perspectives on the latest trends and growth in the data space! Learn more about them below 👇 and if you haven't registered yet, do it now at this link - https://lnkd.in/gMvBqFnR

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**𝐋𝐞𝐯𝐞𝐥 𝐔𝐩 𝐘𝐨𝐮𝐫 𝐃𝐚𝐭𝐚 𝐒𝐤𝐢𝐥𝐥𝐬 𝐰𝐢𝐭𝐡 𝐒𝐧𝐨𝐰𝐟𝐥𝐚𝐤𝐞 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠!** A huge shoutout to my friends, Vishal Kaushal , a Snowflake Squad member, and Sachin Mittal, a 4x Snowflake Data Superhero, who are starting Snowflake Training: Zero to Hero course starting May 18th! . **Why Snowflake? ** Snowflake is more than just a data warehouse. It's a powerful, cloud-native data platform that empowers data engineers to build next-generation data solutions. Here's why learning Snowflake can be a game-changer for your career. By mastering Snowflake, you'll equip yourself with the skills to design and implement robust, scalable data solutions that are in high demand across industries. This positions you as a valuable asset in the ever-evolving world of data engineering. This comprehensive training, will equip you with the knowledge and skills to become a Snowflake master. Here's what you'll gain: Solid Foundation: Grasp the core concepts and functionalities of Snowflake. Hands-on Learning: Get practical experience through real-world exercises and projects. Unlimited doubt support Real time Projects : 2 End to End Real time projects , 1 using Matillion as well. Ready to take your data engineering skills to the next level? Don't miss out on this amazing opportunity! Sign up for the Snowflake Training: Zero to Hero course starting May 18th. 👉🏻 Enroll Here - https://lnkd.in/gcfxzNx4 📅 Live classes starts from 18th May 2024 Discount Coupon : LIVE20 PS : This is not a paid post. #Snowflake #DataEngineering #CloudDataWarehouse #Training #Upskilling #DataSuperhero

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Ready to harness the power of AI while ensuring ethical practices? Dive into Fractal's course 'Responsible AI in the Generative AI Era' —a game-changer in today's digital landscape. In just a few hours, you'll unlock the secrets to navigating the complex world of AI ethics, learning from top industry experts. With interactive modules and real-world case studies, you'll gain invaluable insights into mitigating risks, addressing biases, and fostering accountability. Enroll on Coursera: https://lnkd.in/dBvVxs5M Join thousands of professionals worldwide who are shaping the future of AI responsibly. Don't miss out—enroll today and lead the charge toward a more ethical AI future! According to Forbes, 96% of people consider ethical and Responsible AI to be important. #ResponsibleAI #Coursera #FractalCourses

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Explore possibilities of AI at DevCon this week with us in Bengaluru! We'll be there exploring data strategy, and machine learning models, propelling the creation of dynamic real-time AI applications and analytics. 🇮🇳 You will also gain invaluable insights into implementing RAG without adding further complexity to your data architecture and bringing SQL + NoSQL+ Vector under one data platform. More: https://bit.ly/3XkWcOm #data #events #SingleStore #devs #AI

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Hi, We are posting an interview question today, that was recently asked in Fractal for Data Engineer role. We will cover the SQL code and intuition in this PART 1 and we will next post the Pyspark version of the code in a PART 2 article Hope you have a great Saturday! Don't forget to take it easy today, and have funnnn. Keep following us for moreee! We are MAX(DATA) remember the name! Thanks. #sql #interview #DataEngineer #Pyspark #Practice #DataScience

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The Govt asks QCommerce firms to prove their claim of delivering within 10 minutes. Imagine you are a Data Analyst working as part of Blinkit's data team. How would you approach this problem? What questions would you ask your data? What is the right metric to work with? What concepts would be required to attempt this problem? Welcome to the world of Descriptive and Inferential Statistics with Hypothesis testing being the key to the lock. Remember? When p is low, null should go! Discover another such scenarios across industry domains like E-commerce and Insurance : https://lnkd.in/dkjASWqS Whether you are in individual data professional or are leading high performing data teams, upskilling your analysts in statistical thinking is critical to enable them solve real world problems like this. Discover our 4D framework of upskilling and learn more about our offerings at our stall at #DES2024 #descriptivestats #inferentialstats #experientiallearning #dataanalytics #statistics

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#hiring #analytics Zepto is looking for Data Analytics role! Are you the data wizard? The detective of supply? The hero who can turn inventory chaos into smooth, stocked shelves? We're on the hunt for a Core Analytics wizard with 5+ years of experience (yes, you’re a pro by now!) to help us tackle some of the trickiest puzzles in inventory management, replenishment, availability, assortment, and planning and much more. If you: ✅ Can talk “EOQ” and “Stockouts” better than small talk ✅ Have cracked the Supply Side conundrums no one else dares touch ✅ Are looking for puzzles to solve that actually matter >> Comment down your e-mail ID to get application details #DataAnalysis #BusinessAnalytics #DataDriven #SeniorAnalyst #MarketInsights #DataVisualization #AnalyticsManager #StrategicDecisionMaking #FinancialAnalysis #DataScience [Note: AptivRecruit does not have any direct hiring partnership with Zepto and hence do not have any role in the hiring process. We will be providing you with application details only.]

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Scattered data, siloed processes. 📊 Rocketium, an agile CreativeOps platform, faced a common hurdle — data scattered across multiple tools and teams. This meant constant integration efforts to streamline processes and ensure seamless, efficient data management.🔧 Recognizing the need for a centralized solution, Anurag Dwivedi, Rocketium's Co-founder, turned to DevRev's AI-powered platform: https://dvrv.ai/4cbgePk This unified solution allowed them to break down data silos and seamlessly integrate teams and processes. Looking ahead, Anurag and the Rocketium team are excited for a future where DevRev helps them achieve perfect harmony between teams and processes, all within a single platform. #DevRev #Rocketium #AgentOS #CustomerSuccess #CustomerSuccessStories

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Get set. Strap in with Tableau. Steer towards insights and watch your business data race into overdrive 🏁 Ketan Jain Brijesh K Bhatia Anshul Prashar Abhishek Gupta Siddhant Sharma Chitral Chadda #tableau #data #dataviz #datafam #tableauai #tableaudata #dataanalytics #datavisualization #forecasting #prediction #Businessfuture #futureofbusiness #datafuture #automation #dataanalysis #dashboard #tableaupartner #Tableauindia #autoindustry #autombusiness

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