Business Compass LLC’s Post

Building a Scalable Data Transformation Pipeline on AWS: A Practical Guide https://lnkd.in/dHY2Vk9c Amazon Web Services (AWS) #aws #awslambda #businesscompassllc

In the dynamic world of cloud computing, data is everywhere. Choosing the right ETL (Extract, Transform, Load) service is crucial for efficient data processing. 🤔Struggling to decide between AWS Data Pipeline and AWS Glue? You're not alone! Our latest blog post breaks down the strengths and use cases of each, ensuring you can make an informed decision that best suits your needs. 🔍Explore the in-depth comparison here: https://lnkd.in/dd3eYj5a Gain insights into which tool will streamline your data workflow effectively. Happy reading! 📖💡 #DataEngineering #AWS #ETL #CloudComputing #TechSolutions

Another one fresh off the press 🖨️ Here's a step-by-step guide written by lead Data Engineer @ CloudEQS, Manpreet S., for setting up a microbatch schedule in Matillion's SaaS platform, Data Productivity Cloud. Historically, this design was done leveraging Amazon Web Services (AWS) services (SQS & Lambda). This required a complex set up, additional cloud costs, and external dependencies to have a ETL job run in a continuous loop. With this new design, users no longer require SQS & Lambda to have Matillion jobs run in a microbatch schedule. #ETL #Matillion #Microbatch #AWS #DataEngineering https://lnkd.in/eePMnxdw

Real-Time Data Search with DynamoDB and Amazon OpenSearch Using AWS CDK: A Zero-ETL Solution https://lnkd.in/dEABPMkq Amazon Web Services (AWS) #aws #awslambda #businesscompassllc

⏳ Time is money, and educated decisions rely on actionable insights. Read the article from Pekka Malmirae & Niklas Granqvist on how to speed up and automate data delivery with serverless data pipelines on AWS – AWS Glue, Amazon S3, Amazon Athena, AWS QuickSight, and AWS StepFunctions. https://lnkd.in/dakuv2xm #aws #dataengineering #glue #s3 #athena #quicksight #stepfunctions

With the vast array of tools and services available on AWS, it's easy to get overwhelmed or forget which ones to use for specific tasks. If you’ve ever found yourself wondering which service to choose for data storage, processing, or analytics, you’re not alone! In this post, I’ll break down some of the essential AWS services that can simplify your data workflows 🟢 Data Storage Amazon S3 (Simple Storage Service): S3 is ideal for storing large volumes of data. You can write and read input, output, and intermediate data easily, making it a flexible solution for a wide variety of storage needs. 🟢 Data Processing AWS EC2 (Elastic Compute Cloud): A versatile computing service that provides scalable virtual servers. It runs 24/7 unless manually stopped and is well-suited for processing large datasets. 🟢AWS Lambda: A serverless computing option that automatically handles infrastructure concerns like scaling and termination. While great for tasks under 15 minutes, for longer processes, EC2 is a better alternative. 🟢AWS ECR (Elastic Container Registry): This service allows you to store, manage, and deploy containerized applications. It ensures smooth deployment by packaging code and dependencies together, making cross-environment transitions seamless. 🟢 ETL (Extract, Transform, Load) AWS Glue: A managed service that simplifies ETL operations with a Python-based interface and Spark engine, enabling memory-based data processing. It supports streaming data as well, such as continuous video or text inputs. 🟢 Data Warehousing & Analytics AWS Redshift: A fully managed data warehouse solution. It stores, reads, writes, and modifies large datasets efficiently, making it suitable for complex queries and heavy workloads. 🟢AWS Athena: A serverless query service that allows you to analyze data directly from S3 using standard SQL. It's primarily used for data analytics but doesn't support data modifications. 🟢 Monitoring & Integration CloudWatch: This service tracks logs and monitors server health and resource usage. It helps in scaling applications by triggering alerts when thresholds are crossed. 🟢AWS SNS (Simple Notification Service): Sends real-time notifications (via email, Slack, etc.) for events like failed processes or file deletions in S3. SNS can trigger Lambda to respond to these events. 🟢AWS SQS (Simple Queue Service): A managed queuing system for handling data flows. It ensures smooth communication between services by storing data when producers are faster than consumers. 🟢 Data Visualization Final insights and reports can be visualized using tools like AWS QuickSight or other BI tools like Power BI, which integrate with AWS Redshift for real-time dashboards and analytics. #AWS #CloudComputing #DataStorage #DataProcessing #S3 #EC2 #AWSLambda #AWSGlue #DataWarehouse #Redshift #Athena #CloudWatch #SNS #SQS #ETL #BigData #QuickSight #DataEngineering #ServerlessComputing #CloudIntegration

Are you drowning in data chaos? Turn it into actionable insights with #AWSGlue and #AmazonAthena. Automate data prep, analyze with ease, and build a scalable, cost-effective data lake—all within AWS. Learn more: Read the blog 👇👇 https://lnkd.in/diKR2JX7 #DataAnalytics #AWS #TechSolutions #DataManagement Amazon Web Services (AWS) #everyone #follow Techwrix

Check out this technical article that I authored along with Chandramouli Krishnan and Saurabh Bhutyani! The architecture highlights a Modern Data Sharing strategy on Amazon Web Services (AWS). Learn how to use AWS Data Exchange, Amazon EMR, and Amazon Athena combined with Apache Hudi to maintain a highly scalable and operationally efficient lakehouse!