A2C’s Post

Sometimes it can be confusing as which service does what in each cloud provider. This nice cheat sheet by Datacamp provides a comparison of the main services needed for data and AI-related work, from data engineering to data analysis and data science, to creating data applications.

Absolutely thrilled to complete a comprehensive course on Cloud Technology and its transformative synergy with data! In today's business landscape, the true power lies in the convergence of cutting-edge cloud solutions and vast datasets. Throughout the course, I delved into the essence of data, exploring its historical significance in decision-making and understanding its pivotal role in fueling machine learning advancements. Excited to share my newfound knowledge on structured and unstructured data, databases, data warehouses, and data lakes. #CloudTechnology #DataDrivenInnovation #GoogleCloud #MachineLearning #ContinuousLearning 🌐💻📈

Precisely achieves AWS and Analytics Competency status: Precisely, experts in data integrity, has achieved Amazon Web Services (AWS) Data and Analytics Competency status. http://dlvr.it/T0NZLK

Howdy folks 👋 🚀 Day 15 of #30DayCloudChallenge 🖥 ☁ 🔍 Unlocking Insights with Amazon S3 and Athena: A Step-by-Step Guide Are you looking to efficiently analyze data stored in Amazon S3? Look no further! In this post, I'll guide you through leveraging Amazon Athena, a powerful serverless query service, to gain valuable insights from your S3 data. Let's dive in: Step 1: Set Up Your Dataset in Amazon S3 bucket. Step 2: Discover and Catalog Your Data with Amazon Glue. Step 3: Define Data Transformations with Amazon Glue. Step 4: Generate Athena Data Catalog. Step 5: Analyze Data Using Amazon Athena. Step 6: Query the table. By harnessing the power of Amazon Glue, S3, and Athena, you can streamline your data analytics pipeline from ingestion to insights, accelerating decision-making and driving business outcomes. Ready to unleash the full potential of your data? Let's embark on this data journey together! 💼📊 Stay tuned for more tips and tricks to optimize your AWS infrastructure management. Happy cloud computing! ☁️🚀 #30DaysCloudChallenge #PEPCloudChallenge #AWSCloud #LinkedInLearning #AmazonGlue #AmazonS3 #AmazonAthena

I'm excited to announce I've just completed the course "Modernizing Data Lakes and Data Warehouses with Google Cloud"! I'm now better equipped to: Distinguish between data lakes and warehouses, understanding ideal use cases for each. Explore Google Cloud's data storage solutions for both, including BigQuery for data warehousing. Appreciate the role of data engineers in building effective data pipelines and the impact this has on business operations. See the clear benefits of migrating data engineering to the cloud environment. This course has been a valuable step in my data journey, and I'm eager to apply this knowledge! #GoogleCloud #DataEngineering #DataWarehouse #DataLake

Data is only valuable if you can access and analyze it effectively. AWS Glue provides the bridge you need: Improved Data Quality: Cleanse and transform your data to ensure accuracy and reliability for analysis. Simplified Data Sharing: Share data securely and easily with different teams within your organization. Seamless Analytics Integration: Integrate AWS Glue with analytics services like Amazon Athena and Amazon Redshift for deeper insights. Machine Learning Ready: Prepare your data for machine learning initiatives with AWS Glue's machine learning capabilities. AWS Glue empowers you to unlock the hidden potential of your data, driving data-driven decision making across your organization. More about this topic on AWS official site. And if you want to find out more about cloud cost optimization visit https://umbrelly.cloud/!

🚀 Exciting News! 🌐✨ Thrilled to share my latest article: "A Comprehensive Guide to Building a Resilient Data Pipeline Using Amazon Web Services." 🛠️📊 In this comprehensive guide, we delve into the intricacies of building a high-performance data pipeline using AWS, offering step-by-step insights, best practices, and advanced techniques. 🚀 Whether you're a data engineer, analyst, or cloud enthusiast, this article is your go-to resource for mastering data resilience and scalability in the AWS ecosystem. Read the full guide here: https://lnkd.in/dZjGNRQz Key Takeaways: ✅ Evolution of Data Engineering ✅AWS Service Integration ✅ Advanced Techniques for a Dynamic Pipeline Architecture ✅ Efficient Data Collection and Processing ✅ Scalable and Cost-Effective ETL Processes and more! Feel free to share your thoughts, experiences, and questions in the comments. Let's spark a conversation around optimizing data workflows in the cloud! 💬🚀 #DataEngineering #AWS #DataPipeline #CloudComputing #TechInnovation #LinkedInArticle #DataOptimization

Just finished the course “Data Science on Google Cloud Platform: Designing Data Warehouses” by Kumaran Ponnambalam! Check it out: https://lnkd.in/eQhZufXv #datawarehousing #googlecloudplatform.

Just finished the course “Data Science on Google Cloud Platform: Designing Data Warehouses” by Kumaran Ponnambalam! Check it out: https://lnkd.in/eYsiVvkQ #datawarehousing #googlecloudplatform.

🌟Batch Processing in Big Data using AWS Cloud🌟   ·     Batch processing in big data involves processing data in large blocks or batches that have been collected over a specific time period. It is a method of analyzing and manipulating significant volumes of data at once, rather than processing it in real-time or as it arrives.   ·     Batch processing is commonly used when immediate real-time analytics results are not required, but it is essential to process large volumes of data efficiently. It is suitable for scenarios where data can be collected over time and processed in batches, such as aggregating daily sales data or generating reports. 📊   To implement batch processing using AWS, we can use various services such as Amazon EMR, Amazon Redshift, and AWS Glue etc.   Here is a simple diagrammatic representation of Batch processing using the AWS cloud #awscloud #aws #awsdataengineer #bigdata #bigdatadeveloper #awsservices #apachespark