Nagendra Poojary’s Post

🚀 Day 9: Most Important AWS Lambda Interview Questions You Need to Know 🚀 Lambda Concepts:   - How do you manage concurrency in Lambda functions?   - What are Lambda layers, and how do you use them?   - How can you implement custom error handling in AWS Lambda?    Error Handling and Monitoring:   - How does AWS Lambda handle errors and retries?   - What are the best practices for logging and monitoring Lambda functions?   - How can you use AWS CloudWatch to monitor Lambda performance?   - Explain how you can handle Lambda function timeouts effectively. Lambda and Data Engineering:   - How do you use AWS Lambda for ETL (Extract, Transform, Load) tasks?   - Explain how you would process a large dataset in S3 using Lambda functions.   - What are the challenges of using Lambda for data processing, and how do you overcome them?   For a deeper dive into AWS Lambda and to advance your data engineering career, explore the article below: 👉 [AWS Lambda Interview Guide: Key Questions Every Data Engineer Should Know](https://lnkd.in/gj4PFa2Y) Follow for daily insights and updates! 🔥 #DataEngineeringEdge #AWSLambda #BigData #Spark #Serverless #DataEngineering

🔍 Common Mistakes Data Engineers Make (and How to Fix Them!) In the fast-paced world of data engineering, mistakes are part of the learning curve. Here are 6 common pitfalls I’ve encountered and how you can navigate around them: 1. Overloading Lambda Functions with Heavy Workloads Mistake: Trying to perform heavy data processing or large ETL tasks using AWS Lambda. Solution: Use AWS Lambda for lightweight, event-driven tasks only. For complex ETL jobs, leverage AWS Glue or set up an Apache Spark cluster on Amazon EMR for scalable data processing. 2. Ignoring S3 Bucket Policies and Permissions Mistake: Failing to set appropriate permissions on S3 buckets, leading to data breaches or restricted access. Solution: Regularly audit your S3 bucket policies. Use AWS IAM roles to enforce least privilege and configure bucket policies for granular control over data access. 3. Poor Data Partitioning in Redshift or Athena Mistake: Not partitioning data effectively, resulting in slower queries and higher costs. Solution: Understand your access patterns and use partitioning in Amazon Redshift or AWS Athena. For example, partition data based on time (day, month) if most queries are time-based. This will optimize performance and reduce costs. 4. Not Handling Schema Evolution Properly in Data Lakes Mistake: Assuming that data schemas won’t change over time, leading to downstream errors. Solution: Use schema-on-read services like AWS Glue or Lake Formation that support schema evolution. Leverage tools like AWS Glue Crawlers to automatically detect changes and update your schema registry. 5. Inadequate Monitoring and Alerting Mistake: Deploying data pipelines without proper monitoring, making it hard to detect issues quickly. Solution: Set up CloudWatch alarms and use AWS CloudTrail to monitor pipeline activity and security events. Implement custom metrics for critical ETL steps and create dashboards for real-time visibility. 6. Underestimating the Importance of Cost Management Mistake: Running extensive queries or ETL jobs without considering their cost impact. Solution: Use AWS Cost Explorer and AWS Budgets to monitor and control your spending. Consider using reserved instances or spot instances for long-running jobs, and take advantage of AWS Savings Plans for predictable workloads. Mistakes are inevitable, but being aware of them is the first step to becoming a better data engineer and an overall better person. What mistakes have you encountered in your journey? #DataEngineering #AWS #BigData #CloudComputing #ETL #MachineLearning #CareerGrowth #TechTips

As data engineers, we are constantly on the lookout for tools and services that can streamline our workflows, enhance our productivity, and scale with our growing data needs. One service that has been making waves in the community lately is AWS Glue. 🌟 Why AWS Glue? AWS Glue is a fully managed ETL (extract, transform, and load) service that makes it easy to prepare and transform data for analytics. Here are a few reasons why it’s a game-changer for data engineers: 🔹 Serverless Architecture: Say goodbye to infrastructure management. AWS Glue automatically provisions the environment and resources required to complete your ETL jobs. 🔹 Scalability: Whether you’re working with gigabytes or petabytes of data, AWS Glue scales effortlessly to meet your needs. 🔹 Ease of Use: With a simple visual interface and built-in transformations, it’s easy to design and manage your ETL processes. Plus, it supports both Python and Scala, giving you the flexibility to work with the language you’re most comfortable with. 🔹 Integration: Seamlessly integrates with other AWS services like S3, Redshift, RDS, and more, enabling a smooth and efficient data pipeline. 🔹 Cost-Effective: Pay only for the resources you consume. AWS Glue’s cost-effective pricing model ensures you get the best value for your money. As we continue to harness the power of big data, AWS Glue is proving to be an invaluable asset in our toolkit. It’s helping us transform raw data into actionable insights, faster and more efficiently than ever before. #DataEngineering #AWS #AWSGlue #BigData #ETL #CloudComputing #DataScience #TechInnovation

𝗡𝗲𝘄 𝗼𝗻 𝗧𝗵𝗲 𝗕𝗶𝗴 𝗗𝗮𝘁𝗮 𝗦𝗵𝗼𝘄: AWS Data Engineering Mock Interview We've just wrapped up an intense mock interview on AWS Data Engineering, now available on our YouTube channel - The Big Data Show! Anyone preparing for AWS data engineering should give it a go, even if you are working on Azure/GCP this interview will give you a good understanding of type of questions that can be asked as more or less all cloud providers provide same services under different umbrella. High Level Topic Covered: 1. DMS - How DMS works and its use case for Data Migration. 2. Data Reconciliation: Verify if destination data is up to mark with source dataset, very much important for data migration project specifically. 3. SNS: How can we integrate SNS to downstream consumers to notify of any changes upstream and other use cases. 4. S3: why we have global namespacing in S3, bucket lifecycles, how to protect your s3 bucket (important from security point of view) 5. Securing PII data: PII data is something which needs to be handled with due care, what are different strategy to safeguard it. 6. Dynamo DB vs Elastic Serch 7. Schema Evolution- How Glue Crawler and Catalog manages schema 8 Glue Serverless vs EMR Serverless 9 Design pipeline to trigger email in case of schema changes through glue service. 9. Athena: How Athena queries the data and manages metadata, can we have athena query data from bucket in different region. 10. Data Lineage: How to manage Data Lineage of your pipeline 11. AWS: Unlocking the capabilities of AWS Glue. 12. Problem Solving: Challenging your SQL and system design skills in Data Engineering. 13. System Design: Complete discussion on designing an Adhoc pipeline using AWS services. and many other questions. This should give you a good idea on what you can expect in DE interview, do give it a watch and you won't regret. Watch now on YouTube channel - 𝗧𝗵𝗲 𝗕𝗶𝗴 𝗗𝗮𝘁𝗮 𝗦𝗵𝗼𝘄 and let us know your thoughts in the comments! There are many other mock interviews recorded on channel, do give it a watch. Channel link in comments. Follow Nisha Bansal Ankur Ranjan and subscribe to the channel to not miss any updates. #dataengineering #mockinterview #awscloud #security

AWS Glue is a serverless data integration service. With AWS Glue, data scientists, analysts and developers can discover, prepare, and combine data for various purposes. Examples include analytics, machine learning (ML), and application development. In this hands on course learned below topics. - Which problems does AWS Glue solves. - The benefits of AWS Glue. - Architecture and Use cases. - Loading sample customers and sales data into S3 bucket. - Using AWS Glue to Crawl and Catalog data. - Using AWS Glue studio to Perform ETL on data - Loading Dataset into AWS Glue DataBrew. - Creating a profile job in DataBrew and setup data quality ruleset. - Creating a project in AWS Glue DataBrew. - Building a Recipe in AWS Glue DataBrew.

🚀 Why End-to-End Data Pipelines are Crucial? In today’s data-driven world, having an efficient data pipeline is not just a luxury—> it’s a necessity! 💡 An end-to-end pipeline is essential for efficiently managing and processing data from its source to its final destination, ensuring that it’s usable for analysis and decision-making. Creating an end-to-end data pipeline in AWS can be accomplished through various services and architectures, depending on your specific use case, data sources, and processing needs. Let us dive deep into the concept of creating "Serverless ETL Pipeline with AWS Lambda and AWS Glue". 💠 Components: 🔶 Amazon S3 --> As the primary storage. 🔶 AWS Lambda --> To trigger ETL jobs or process data upon events. 🔶 AWS Glue --> For data cataloging and ETL. 💠 Workflow: 1️⃣ Data is uploaded to S3. 2️⃣ An S3 event triggers a Lambda function. 3️⃣ The Lambda function invokes an AWS Glue ETL job. 4️⃣ Processed data is stored back in S3 or another service. End-to-end pipelines in AWS help you turn data into actionable insights efficiently and at scale. It’s all about making data work for you! 🔍💼 #AWS #DataEngineering #CloudComputing #BigData #Automation #DataPipeline #DataIntegration #RealTime #Scalability

📅 Day 12: Most Important AWS Lambda Interview Questions You Need to Know AWS Lambda Basics:  - Can you explain the core working mechanism of AWS Lambda and how it fits into serverless architecture?  - Why are environment variables so important in AWS Lambda, and how do they impact function behavior and security? Lambda Function Development:  - What are the key steps involved in creating and deploying an AWS Lambda function, from code to execution?   - How do you efficiently manage dependencies in AWS Lambda to ensure smooth function execution and minimal cold starts?  - What’s the difference between synchronous and asynchronous invocations in AWS Lambda, and when should each be used? Triggering AWS Lambda Functions:  - What are the various ways to trigger AWS Lambda functions, and how do you decide which method to use?   - How would you configure an S3 event to trigger an AWS Lambda function, and what use cases does this support? Performance & Scaling:  - How does AWS Lambda automatically scale to meet varying workloads, and what factors influence this scalability?   - What is the AWS Lambda cold start problem, and what are the best strategies to reduce cold start latency?  - What techniques can be used to optimize the execution time of an AWS Lambda function for improved performance and cost efficiency? For a deeper dive into AWS Lambda and to advance your data engineering career, explore the article below: [AWS Lambda Interview Guide: Key Questions Every Data Engineer Should Know](https://lnkd.in/gj4PFa2Y) Follow for daily insights and updates! #DataEngineeringEdge #AWSLambda #BigData #Spark #Serverless #DataEngineering

Series #001 - Data Engineering Tools As a data engineer, I'm always on the lookout for tools that simplify my workflow without sacrificing power or flexibility. That's why I've become a big fan of AWS Glue. What sets AWS Glue apart: - Serverless Simplicity: No infrastructure to manage? Yes, please! Glue handles everything, allowing you to focus on building data pipelines instead of configuring servers. - Visual and Code-Based Options: Whether you are quickly prototyping with the visual interface or diving into custom ETL scripts with Spark, Glue has you covered. - Schema Discovery and Crawlers: Glue's ability to automatically infer schemas and crawl data sources saves you a ton of time and manual effort. - Integration with the AWS Ecosystem: Glue plays nicely with other AWS services like S3, Athena, and Redshift, making it a seamless part of your data infrastructure. Real-World Use Cases: You can leverage AWS Glue to: - Build a data lake on S3: Ingesting, cleaning, and transforming data from various sources into a well-structured data lake for analytics. - Automate ETL jobs: Creating event-driven workflows that trigger Glue jobs when new data arrives, ensuring timely updates. - Enrich data with machine learning: Leveraging Glue's integration with SageMaker to incorporate ML models into my data pipelines. Overall, AWS Glue has become an indispensable tool in data engineering toolkit. It's a powerful, flexible, and cost-effective way to build serverless ETL pipelines on AWS. #AWSGlue #ETL #DataEngineering #Serverless #DataLake

🌟 Reflecting on the journey of a data engineer: sometimes, the smallest insights can lead to the biggest lessons. 💡 Recently, while delving into AWS Glue for a project, I encountered an unexpected charge of $1.41. At first glance, it might seem negligible, but this moment sparked a profound realization about the intricacies of cloud computing and data engineering. In the realm of data architecture, precision is paramount. Whether it's optimizing ETL pipelines or harnessing the power of data lakes, every detail counts. The incident with AWS Glue underscored the importance of meticulous planning and monitoring, ensuring that every resource usage aligns with operational goals. As data engineers, we navigate through complexities, balancing innovation with cost-efficiency. Each interaction with cloud services like AWS Glue offers insights into scalability, reliability, and performance optimization. These experiences are not just about technical proficiency but also about strategic decision-making and resource management. So, what does it truly cost to become a data engineer? It's more than financial expenditures; it's about investing in knowledge, resilience, and a commitment to continuous learning. Embracing challenges like my $1.41 lesson with AWS Glue reinforces our ability to adapt and evolve in a rapidly transforming digital landscape. Let's continue to explore, innovate, and share our experiences as we shape the future of data engineering together. #DataEngineering #AWSGlue #CloudComputing #TechInnovation #ContinuousLearning #DataArchitectures #DigitalTransformation

🚀 AWS for Data Engineering: Key Concepts I’ve Learned So Far! 💡 Recently, I’ve been diving into an End-to-End Data Engineering project by Darshil Parmar on AWS, and it's been an incredible learning journey! Here are some of the essential AWS concepts I’ve picked up along the way: 🔐 Data Security and Governance: AWS IAM (Identity and Access Management): This service helps manage access to AWS resources securely by creating users, groups, and roles with fine-grained permissions. A key tool for enforcing security policies and access control across AWS services. 💾 Data Storage: Amazon S3: Object storage for large volumes of unstructured data like log files, backups, and more. A perfect solution for building scalable data lakes. AWS Glue Data Catalog: A centralized repository that manages metadata for data stored in S3, Redshift, and other AWS services, providing schema structure for efficient data management. 🔄 Data Ingestion and ETL (Extract, Transform, Load): AWS Glue: A serverless ETL service that transforms, cleans, and moves data between different stores (S3, Redshift, RDS), enabling the creation of scalable ETL pipelines. 📊 Data Processing and Analytics: Amazon Athena: A serverless query service to run SQL directly on data in S3. Perfect for ad-hoc querying, log analytics, and exploring data lakes. AWS Lambda: A serverless compute service that runs code in response to events. Ideal for event-driven ETL workflows and real-time data transformations using Python, Node.js, or Java. 🔍 Monitoring and Management: Amazon CloudWatch: A monitoring and observability service that tracks system health, logs, and performance metrics. It’s an essential tool for monitoring data pipelines and performance. These AWS services are helping me streamline data management, ETL processes, and analytics, deepening my passion for data engineering even further! If I’m missing any other important aspects of AWS for data engineering, I’d love to hear your thoughts in the comments! Amazon Web Services (AWS) #AWS #DataEngineering #CloudComputing #BigData #Serverless #ETL #AmazonS3 #AWSGlue #AmazonAthena #CloudWatch #Lambda #TechJourney