Sreemanta Kesh’s Post

🚀 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

Hey everyone! 👋 I'm excited to share my latest Medium blog post where I delve into solving a common data engineering challenge using AWS services. 🌟 🔗 (https://lnkd.in/di-g8S9y) Why this blog?  In my role as a data science engineer, creating scalable ETL pipelines is a frequent necessity. While AWS offers a suite of powerful tools, integrating them effectively can be complex. This blog provides a technical, step-by-step guide to building a robust ETL pipeline using AWS Lambda, S3, AWS Glue, and Amazon Redshift. What you'll learn: - Leveraging AWS Lambda and S3 for efficient data collection. - Transforming data seamlessly with AWS Glue. - Loading and querying data in Amazon Redshift. - Monitoring and optimizing your ETL pipeline using AWS CloudWatch and Step Functions. Many industry experts may already be familiar with these concepts, so this is geared towards beginners starting their journey in Data and ML engineering utilizing AWS. I hope this guide proves helpful for anyone aiming to enhance their data engineering workflows and fully utilize AWS capabilities. Check it out, and I’d love to hear your feedback and thoughts! 🙌 #AWS #DataEngineering #ETL #CloudComputing #BigData #TechBlog #MediumBlog #DataScience #AmazonRedshift #AWSLambda #AWSGlue #S3

🚀 Leveraging AWS Lambda for Data Engineering Success 🚀 As a Data Engineer with 5 years of experience, I've had the opportunity to work with numerous cloud services, but AWS Lambda has consistently stood out as a game-changer. Here’s why AWS Lambda should be a cornerstone in your data engineering toolkit: 🔹 Serverless Efficiency: With AWS Lambda, you can run code without provisioning or managing servers. This serverless architecture allows for seamless scaling and high availability, all while minimizing costs. 🔹 Event-Driven Processing: AWS Lambda excels in event-driven applications. Whether you’re processing data streams in real-time from Kinesis, transforming data from S3, or handling API requests via API Gateway, Lambda's integration capabilities streamline complex workflows. 🔹 Cost-Effective Scaling: Pay only for what you use. Lambda's pricing model ensures you’re not paying for idle resources, which is a significant advantage for batch processing jobs or unpredictable workloads. 🔹 Versatile Language Support: Lambda supports multiple programming languages, including Python, Node.js, Java, and Go. This versatility allows you to choose the best language for your specific data processing tasks. 🔹 Seamless Integration: Lambda integrates effortlessly with other AWS services like S3, DynamoDB, RDS, and more. This makes it easier to build comprehensive data pipelines and ETL processes that are robust and scalable. 🔹 Quick Deployment: Rapid deployment and update cycles mean you can iterate quickly and respond to changing data requirements faster than ever. One of my favorite use cases is leveraging AWS Lambda to automate ETL pipelines. By triggering Lambda functions based on S3 events, I’ve been able to automate the extraction, transformation, and loading of large datasets, significantly reducing manual intervention and improving data processing efficiency. For those looking to optimize their data workflows and enhance their cloud infrastructure, AWS Lambda is a must-have tool. It’s a powerhouse for data engineers aiming to build efficient, scalable, and cost-effective solutions. If you’re interested in learning more about AWS Lambda or sharing your experiences, let’s connect! 🚀🔗 #DataEngineering #AWS #Lambda #Serverless #CloudComputing #ETL #BigData #TechInnovation #DataPipeline #AWSLambda

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

AWS and the Future of Data Engineering 🌩️ Empowering Data Engineering with AWS In the era of big data, Amazon Web Services (AWS) has become a game-changer for data engineering. With its vast suite of tools and services, AWS provides a reliable, scalable, and cost-effective platform for building end-to-end data solutions. 🔑 Key AWS Services for Data Engineering: 1️⃣ Amazon S3: The backbone for data storage, offering scalability and durability for all types of data. 2️⃣ AWS Glue: Simplifying ETL processes with serverless data integration. 3️⃣ Amazon Redshift: A powerful, fully managed data warehouse for analytics at scale. 4️⃣ Kinesis and Kafka on AWS: Real-time data streaming for actionable insights. 5️⃣ Athena: Query data directly from S3 using SQL, no infrastructure required! 💡 Why AWS for Data Engineering? Scalability: From startups to enterprises, AWS grows with your data. Integration: Seamlessly connects with third-party tools and ecosystems. Cost Efficiency: Pay-as-you-go pricing ensures you only pay for what you use. Global Reach: Build systems that work across regions with minimal latency. AWS isn’t just a toolkit—it’s a platform for innovation. As I dive deeper into AWS data engineering, I’m excited by the possibilities of creating faster, smarter, and more efficient data workflows. What’s your favorite AWS service for data engineering, and how are you using it? Let’s share ideas and grow together! #AWS #DataEngineering #CloudComputing #ETL #BigData

🚀 Enhance Your Data Engineering Skills with AWS! 🌐 I recently came across an insightful video titled "Top AWS Services A Data Engineer Should Know" (https://lnkd.in/gFPjHZ3S) that provides an excellent overview of key AWS services every data engineer should be familiar with. 🎯 Highlights of the video: Amazon S3: The go-to storage solution for scalable and secure data storage. AWS Glue: Simplify ETL processes and data integration with features like: Glue Catalog: A centralized metadata repository for your datasets. Glue Crawlers: Automatically discover and catalog datasets. Amazon Redshift: A powerful data warehousing service for advanced analytics. Amazon Athena: Query data directly from S3 using SQL, making analytics fast and serverless. AWS Lambda: Execute code in a serverless environment, perfect for automating tasks and event-driven workflows. AWS Step Functions: Orchestrate complex workflows with ease through visual workflows. Amazon EventBridge: Build event-driven architectures by connecting different AWS services or SaaS applications effortlessly. Amazon Simple Notification Service (SNS): A fully managed pub/sub messaging service for sending notifications to distributed systems. Amazon Simple Queue Service (SQS): A reliable and scalable message queuing service to decouple components of your applications. Amazon Managed Workflows for Apache Airflow (MWAA): Manage and execute workflows with a fully managed Airflow service. Amazon EMR: Process big data efficiently with Hadoop and Spark. Amazon Kinesis: Seamlessly handle real-time data streaming. Amazon QuickSight: Deliver interactive dashboards and data visualizations effortlessly. Amazon CloudWatch: Monitor your AWS resources and applications in real-time with metrics, logs, and alarms to ensure operational excellence. The video does a great job of explaining how these services fit into modern data engineering workflows, making it a must-watch for aspiring and seasoned professionals alike. 💡 Whether you're building robust data pipelines, managing large-scale analytics, or delivering actionable insights, mastering these services can be a game-changer for your career. #AWS #DataEngineering #CloudComputing #AWSLambda #GlueCatalog #StepFunctions #Athena #Airflow #EventBridge #SNS #SQS #CloudWatch #QuickSight #CareerGrowth

🌐 Why AWS Keeps Surprising Me as a Data Engineer 🌐 Working with data at scale isn’t easy. But with AWS, it’s almost like I have a whole toolkit ready for every challenge thrown my way. Each project feels like a new way to push boundaries—and AWS consistently helps me turn ambitious ideas into reality. Here’s what stands out to me about AWS: 🔸 Automated ETL with Glue & Lambda– I’ve cut down processing time and manual tasks by over 40% with these. The time saved goes back into analysis and real problem-solving.   🔸 Storage on S3– Whether it's raw, processed, or archived data, S3 has become my go-to. It's secure, scalable, and keeps costs low—without sacrificing accessibility.   🔸 Real-Time Insights with Redshift Spectrum– Running complex queries across huge datasets directly on S3 is a game changer. The insights flow faster, helping everyone make decisions with fresh data. Takeaway:AWS is more than just tools; it’s the foundation that lets me (and my team) tackle big data challenges, keep costs in check, and keep moving forward without slowing down. If you work with AWS, what’s your favorite part of the ecosystem? I’d love to hear how it’s helping you innovate! 🔍💡 #AWS #DataEngineering #CloudSolutions #ETL #Automation #Innovation #BigData #DataAnalytics #CloudComputing #DataPipelines #Serverless #DataTransformation #Scalability #MachineLearning #DataIntegration #DataScience #DigitalTransformation #TechInnovation #DataInfrastructure #CloudStorage

🌐 Transforming Semi-Structured Data into a Structured Data Pipeline with AWS 🚀 In today's data-driven world, transforming semi-structured data into a structured format is crucial for extracting meaningful insights. Here's a step-by-step guide on how we achieved this using AWS services: S3, Glue Crawler, AWS Glue, and AWS Lambda. 🌟 Data Storage with AWS S3: We began by storing our semi-structured data in AWS S3. S3 provides a highly scalable and durable storage solution, making it ideal for handling vast amounts of data from various sources. Metadata Extraction with AWS Glue Crawler: To automatically infer the schema of our semi-structured data, we used AWS Glue Crawler. This service crawls the data stored in S3, identifies the structure, and creates metadata in the Glue Data Catalog. This metadata serves as the foundation for our ETL processes. Data Transformation with AWS Glue: With the schema defined, we leveraged AWS Glue to transform our semi-structured data into a structured format. AWS Glue's serverless architecture and its support for both Python and Scala make it a powerful tool for complex data transformations. Automating Workflows with AWS Lambda: To ensure our data pipeline runs seamlessly, we utilized AWS Lambda to automate the triggering of Glue jobs. AWS Lambda allows us to execute code in response to events, ensuring that our ETL processes are initiated as soon as new data is available in S3. Pipeline Orchestration: By integrating these services, we created a robust, automated pipeline that transforms semi-structured data into structured data, ready for analysis and reporting. This process not only improves data quality but also accelerates time-to-insight. By combining the power of AWS S3, Glue Crawler, AWS Glue, and AWS Lambda, we built a scalable and efficient data pipeline that meets the demands of modern data analytics. 💡 #DataEngineering #AWS #DataPipeline #BigData #ETL #CloudComputing #DataTransformation #S3 #Glue #Lambda #TechInnovation

🚀 **Unlock the Power of Data with AWS Glue!** 🚀 As data continues to grow at an unprecedented rate, organizations are seeking efficient and scalable solutions to manage, transform, and analyze their data. That’s where **AWS Glue** comes in—an ETL (Extract, Transform, Load) service designed to make it easy to prepare and integrate data for analytics, machine learning, and application development. Here’s why AWS Glue stands out: 1️⃣ **Serverless and Scalable:** With AWS Glue, you don't need to manage infrastructure. It automatically scales based on your workload, making it cost-effective and efficient. 2️⃣ **Data Catalog:** Glue provides a central metadata repository for your data, making it easier to discover, search, and understand your data assets. 3️⃣ **Seamless Integration:** It works smoothly with other AWS services like S3, Redshift, RDS, and more, allowing you to build end-to-end data pipelines effortlessly. 4️⃣ **Data Transformation:** With its built-in support for Spark, Glue allows for robust and flexible data transformations, from simple data cleaning to complex joins and aggregations. 5️⃣ **Automation with Jobs:** You can schedule and automate data processing tasks, ensuring that your data is always up-to-date without manual intervention. Whether you're preparing data for analytics, running large-scale ETL jobs, or building machine learning models, AWS Glue is a game-changer for **data engineering** and **data-driven decision-making**. #AWS #AWSGlue #DataEngineering #BigData #ETL #DataTransformation #CloudComputing #DataScience

🚀 Leveraging real-world projects to sharpen my skills! I recently took a significant step in enhancing our customer feedback systems by developing an application that captures user input directly into DynamoDB. 🔍 From there, I crafted custom scripts to seamlessly transfer this data to Amazon S3, effectively partitioning it by event_name for improved accessibility and data analysis capabilities. 📊 Utilizing AWS Glue, I transformed the collected data into the Parquet format, which not only optimizes query performance but also significantly cuts down on storage costs. This data is meticulously organized into different folders within the same S3 bucket, ensuring efficient data handling and retrieval. 🛠️ With a solid understanding of the data schema, I went ahead and set up a Glue database and table, enabling precise data manipulation in Amazon Athena as illustrated in the screenshots I’ve attached. 📈 This structured and optimized data is now fully prepped for in-depth analysis in Amazon QuickSight, allowing our team to generate actionable, real-time insights that enhance our understanding of customer preferences and feedback. 🎯 Projects like this have been crucial in my achievement of the AWS Certified Data Engineer - Associate certification. They have allowed me to apply theoretical knowledge in practical, impactful ways. 💼 I’m keen to connect with fellow tech enthusiasts and discuss how we can drive innovation using cloud technologies. Let’s explore potential collaborations where I can bring my expertise in data engineering and AWS services. After knowing the schema, I created glue database and table. As you can see, I can manipulate the data in Athena the way I wanted and this data is ready for visualization in QuickSight. #dataengineering #developer #softwareenginer #cloud #aws #s3 #dynamodb #glue #lakeformation #lakehouse #athena #quicksight #sql #python #pyspark