Cloud Storage for Big Data Analytics

Imagine you have 5 TB of data stored in Azure Data Lake Storage Gen2 — this data includes 500 million records and 100 columns, stored in a CSV format. Now, your business use case is simple: ✅ Fetch data for 1 specific city out of 100 cities ✅ Retrieve only 10 columns out of the 100 Assuming data is evenly distributed, that means: 📉 You only need 1% of the rows and 10% of the columns, 📦 Which is ~0.1% of the entire dataset, or roughly 5 GB. Now let’s run a query using Azure Synapse Analytics - Serverless SQL Pool. 🧨 Worst Case: If you're querying the raw CSV file without compression or partitioning, Synapse will scan the entire 5 TB. 💸 The cost is $5 per TB scanned, so you pay $25 for this query. That’s expensive for such a small slice of data! 🔧 Now, let’s optimize: ✅ Convert the data into Parquet format – a columnar storage file type 📉 This reduces your storage size to ~2 TB (or even less with Snappy compression) ✅ Partition the data by city, so that each city has its own folder Now when you run the query: You're only scanning 1 partition (1 city) → ~20 GB You only need 10 columns out of 100 → 10% of 20 GB = 2 GB 💰 Query cost? Just $0.01 💡 What did we apply? Column Pruning by using Parquet Row Pruning via Partitioning Compression to save storage and scan cost That’s 2500x cheaper than the original query! 👉 This is how knowing the internals of Azure’s big data services can drastically reduce cost and improve performance. #Azure #DataLake #AzureSynapse #BigData #DataEngineering #CloudOptimization #Parquet #Partitioning #CostSaving #ServerlessSQL

“𝗦𝟯, 𝗔𝗗𝗟𝗦, 𝗚𝗖𝗦? 𝗝𝘂𝘀𝘁 𝘀𝘁𝗼𝗿𝗮𝗴𝗲, 𝗿𝗶𝗴𝗵𝘁?” Not quite. Here’s a better way to think about it 👇 𝗖𝗹𝗼𝘂𝗱 𝗦𝘁𝗼𝗿𝗮𝗴𝗲 — 𝗠𝗼𝗿𝗲 𝗧𝗵𝗮𝗻 𝗝𝘂𝘀𝘁 𝗮 𝗙𝗶𝗹𝗲 𝗗𝘂𝗺𝗽 Cloud storage is like a hotel for your data. It checks in from various sources — APIs, apps, pipelines. Some stay temporarily (like staging or temp files) Others are long-term guests (like audit logs or historical records) You control who can access it (IAM), what they can do (read/write), and how long it stays (retention policies) There’s even housekeeping involved — with lifecycle rules, versioning, deduplication, and cost optimization. ⚠️ 𝗪𝗵𝗮𝘁 𝗣𝗲𝗼𝗽𝗹𝗲 𝗧𝗵𝗶𝗻𝗸 𝗗𝗘𝘀 𝗗𝗼: "Just dump the data to S3 and move on." ✅ 𝗪𝗵𝗮𝘁 𝗔𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗛𝗮𝗽𝗽𝗲𝗻𝘀:   • Design folder structures for efficient querying and partitioning   • Choose the right storage class (Standard, Infrequent Access, Glacier)   • Use optimal file formats (Parquet, ORC) and compression (Snappy, Zstandard)   • Set access controls, encryption, and auditing (IAM roles, KMS, logging)   • Enable direct querying (Athena, Synapse, BigQuery on GCS)   • Integrate storage across cloud platforms (multi-cloud architectures)   • Automate lifecycle management to control cost and reduce clutter   • Leverage features like S3 Select, signed URLs, and Delta format for smart access 📌 Takeaway: Cloud storage isn’t where data ends up — it’s where the journey begins. How you design and manage it defines the performance, scalability, and reliability of everything downstream. #data #engineering #reeltorealdata #python #sql #cloud

𝗔𝗺𝗮𝘇𝗼𝗻 𝗦𝟯 𝗕𝘂𝗰𝗸𝗲𝘁𝘀 𝘃𝘀. 𝗦𝟯 𝗧𝗮𝗯𝗹𝗲𝘀: 𝗔 𝗗𝗲𝗲𝗽 𝗗𝗶𝘃𝗲 𝘄𝗶𝘁𝗵 𝗨𝘀𝗲 𝗖𝗮𝘀𝗲𝘀 When working with cloud data storage, Amazon S3 is one of the most popular services. However, there’s often confusion between S3 Buckets and S3 Tables. Let’s break down the differences, their purposes, and when to use them. 𝗔𝗺𝗮𝘇𝗼𝗻 𝗦𝟯 𝗕𝘂𝗰𝗸𝗲𝘁𝘀 An S3 bucket is a storage container for objects (files). It is used to store any type of unstructured or semi-structured data. Buckets can store unlimited data and provide scalable, high-durability storage. 𝗖𝗼𝗺𝗺𝗼𝗻 𝗨𝘀𝗲 𝗖𝗮𝘀𝗲𝘀: Data Lakes: Storing vast amounts of raw, structured, and semi-structured data. Static Website Hosting: Serving static content such as HTML, images, and videos. Backup and Disaster Recovery: Storing backups with lifecycle policies for automated archival to cheaper tiers like Glacier. Media Storage and Distribution: Hosting media files for streaming or downloading 𝗔𝗺𝗮𝘇𝗼𝗻 𝗦𝟯 𝗧𝗮𝗯𝗹𝗲𝘀 (𝗚𝗹𝘂𝗲 & 𝗔𝘁𝗵𝗲𝗻𝗮 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻) An S3 table is a logical structure representing data stored in an S3 bucket. S3 tables are used with services like AWS Glue and Amazon Athena to provide a schema-based view over data files. Unlike S3 buckets, tables allow querying with SQL-like syntax. 𝗖𝗼𝗺𝗺𝗼𝗻 𝗨𝘀𝗲 𝗖𝗮𝘀𝗲𝘀: Data Analytics: Querying large datasets directly in S3 using Athena, without data movement. Ad-hoc Reporting: Fast, on-demand analytics over semi-structured formats (like JSON or Parquet). Data Lake Querying: Seamlessly integrate with Redshift Spectrum for combining relational and object-based data. Cost-efficient Analysis: Perform big data analytics without the cost and complexity of maintaining a database. 𝗞𝗲𝘆 𝗗𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝗰𝗲𝘀 S3 Buckets store objects, while S3 Tables represent a schema on top of data stored in S3. S3 buckets are great for storage, backups, and media, while S3 tables are optimized for querying and analytics. S3 tables leverage tools like Athena for running SQL queries directly on data without moving it. 𝗔 𝗺𝗼𝗱𝗲𝗿𝗻 𝗱𝗮𝘁𝗮 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 𝘂𝘀𝗲𝘀 𝗯𝗼𝘁𝗵: Store raw and processed data in S3 buckets. Create S3 tables for analysis, using Glue for cataloging and Athena for querying. Both are essential tools for building scalable, flexible, and cost-effective data solutions. Which one do you use most often in your projects? Let’s discuss your use cases! #AWS #CloudStorage #DataAnalytics #DataLakes #Athena #Glue #BigData #S3Tables

AWS Data Platform Reference Architecture! In today's data-driven world, organizations need a robust data platform to handle the growing volume, variety, and velocity(3 V’s) of data. A well-designed data platform provides a scalable, secure, and efficient infrastructure for data management, processing, and analysis. It transforms raw data into actionable insights that can inform strategic decision-making, drive innovation, and achieve business objectives. Let's delve into some key components of this architecture: ✅Centralized Data Repository: Amazon S3 acts as a centralized storage hub for both structured and unstructured data, ensuring durability, availability, and scalability. ✅Streamlined Data Transformation: AWS Glue simplifies the process of extracting, transforming, and loading (ETL) data into usable formats, preparing it for downstream analysis. ✅Powerful Data Analytics: Amazon Redshift, a fully managed data warehouse, supports complex SQL queries on large datasets, enabling organizations to gain deep insights from their data. ✅Efficient Big Data Processing: Amazon EMR, a cloud-native big data platform, handles massive data volumes using frameworks like Hadoop, Spark, and Hive. ✅Real-time Data Streaming: Amazon Kinesis enables real-time ingestion, buffering, and analysis of data streams from various sources, powering real-time applications and insights. ✅Event-driven Automation: AWS Lambda offers serverless computing, executing code in response to events, automating tasks and triggering other services. ✅Simplified Search and Analytics: Amazon Elasticsearch Service provides a managed search and analytics service, making it easy to analyze logs, perform text-based search, and enable real-time analytics. ✅Seamless Data Visualization and Sharing: Amazon Quicksight empowers users to explore and share data insights through interactive visualizations and reports. ✅Automated Data Workflow Orchestration: AWS Data Pipeline automates and orchestrates data-driven workflows across various AWS services, ensuring consistency and simplifying data management. ✅Machine Learning Made Easy: Amazon SageMaker simplifies the process of building, training, and deploying machine learning models for data analysis and predictions. ✅Centralized Metadata Management: The AWS Glue Data Catalog serves as a central repository for metadata, storing information about data sources, transformations, and schemas, facilitating data discovery and management. ✅Data Governance for Quality and Trust: Data governance ensures data quality, security, compliance, and privacy through policies, procedures, and controls, maintaining data integrity and compliance. Empowering a Data-driven Future A data platform architecture transforms data into valuable assets, enabling informed decisions and business growth. Source: AWS Tech blogs Follow - Chandresh Desai, Cloudairy #cloudcomputing #data #aws