AWS Storage Solutions for Data Analytics

I'm always on the lookout for "AWS" scale customer case studies 😎 !! This recent blog about how Ancestry tackled one of the most impressive data engineering challenges I've seen recently - optimizing a 100-billion-row Apache Iceberg table that processes 7 million changes every hour. The scale alone is staggering, but what's more impressive is their 75% cost reduction achievement. 𝐓𝐡𝐞 𝐀𝐖𝐒-𝐏𝐨𝐰𝐞𝐫𝐞𝐝 𝐒𝐨𝐥𝐮𝐭𝐢𝐨𝐧 Their architecture combines Amazon EMR on EC2 for Spark processing, Amazon S3 for data lake storage, and AWS Glue Catalog for metadata management. This replaced a fragmented ecosystem where teams were independently accessing data through direct service calls and Kafka subscriptions, creating unnecessary duplication and system load. 𝐖𝐡𝐲 𝐈𝐜𝐞𝐛𝐞𝐫𝐠 𝐌𝐚𝐝𝐞 𝐭𝐡𝐞 𝐃𝐢𝐟𝐟𝐞𝐫𝐞𝐧𝐜𝐞 Apache Iceberg's ACID transactions, schema evolution, and partition evolution capabilities proved essential at this scale. The team implemented merge-on-read strategy and Storage-Partitioned Joins to eliminate expensive shuffle operations, while custom partitioning on hint status and type dramatically reduced data scanning during queries. 𝐄𝐧𝐭𝐞𝐫𝐩𝐫𝐢𝐬𝐞-𝐒𝐜𝐚𝐥𝐞 𝐑𝐞𝐬𝐮𝐥𝐭𝐬 This solution now serves diverse analytical workloads - from data scientists training recommendation models to geneticists developing population studies - all from a single source of truth. It demonstrates how modern table formats combined with AWS managed services can handle unprecedented data scale while maintaining performance and controlling costs. More details in the blog at https://lnkd.in/gN-mvdUE #bigdata #iceberg #aws #ancestry #analytics #scale #apache

Problem It Solves Accessing large volumes of data from Amazon S3 Standard can introduce latency and throughput bottlenecks, especially in ML, analytics, and high-performance computing workloads that need repeated or rapid access to the same data. Blog Summary The blog introduces a solution that uses Amazon S3 Express One Zone as a caching layer for S3 Standard. It sets up a data transfer pipeline using AWS Step Functions and AWS DataSync to move frequently accessed data into S3 Express. This reduces access time and boosts performance significantly. In a test, ~2.9 TiB of data was transferred in 4 minutes 25 seconds at a cost of ~$20, enabling faster and lower-latency compute access. https://lnkd.in/e9m4YHmH Pablo Scheri

Day 1 – AWS Data Engineering Foundations (In-Depth): 1. What Does an AWS Data Engineer Actually Do? A Data Engineer on Amazon Web Services (AWS) is responsible for: Designing scalable data architectures Building reliable batch & streaming pipelines Ensuring data quality, security, and governance Optimizing performance and cost Enabling analytics, BI, and ML teams 2. AWS Analytics Ecosystem: #LayeredArchitectureView: Data Sources  ├── Databases (RDS, DynamoDB)  ├── Files / APIs  ├── Streaming Events     ↓ Ingestion Layer  ├── Batch: S3 uploads, DMS  ├── Streaming: Kinesis, MSK     ↓ Storage Layer (Data Lake)  ├── Amazon S3  ├── Raw / Curated / Analytics zones     ↓ Processing Layer  ├── AWS Glue (Serverless Spark)  ├── EMR (Spark / Hadoop)     ↓ Analytics Layer  ├── Athena (SQL on S3)  ├── Redshift (Data Warehouse)     ↓ Orchestration & Monitoring  ├── Step Functions  ├── CloudWatch     ↓ Visualization  ├── QuickSight 3. ETL vs ELT (Interview Favorite) - ETL (Traditional) Transform before loading Used in on-prem systems Less flexible - ELT (Modern Cloud – AWS Style) Load raw data into S3 Transform later using Glue / Redshift SQL Scales better #Interview: In AWS, we mostly follow ELT—store raw data in S3 first, then transform using Glue or Redshift—because storage is cheap and compute is scalable.  4. Data Lake vs Data Warehouse (In Depth) Data Lake (Amazon S3) Stores raw + processed data Schema-on-read Cheap & scalable. Data Warehouse (Amazon Redshift) Stores structured, curated data Schema-on-write Optimized for BI queries. Real-World Enterprise Scenario (End-to-End) #Scenario: Retail Analytics Platform #Problem: Company wants: Daily sales reports Near real-time dashboards Historical trend analysis #ArchitectureDesign: 1. Source Systems POS systems Online transactions 2. Ingestion Batch uploads → S3 Real-time events → Kinesis 3. Storage S3 /raw, /curated, /analytics 4. Processing Glue cleans & converts to Parquet EMR for heavy joins if needed 5. Analytics Athena for ad-hoc queries Redshift for dashboards 6. Visualization QuickSight for business users 7. Governance IAM roles Encryption Glue Data Catalog 5. Security & Governance (Often Missed) Core Security Principles: IAM roles (not users) Least privilege access Encryption at rest & in transit Governance Tools: IAM → access control Glue Data Catalog → metadata Lake Formation → fine-grained permissions #InterviewLine: Security and governance are built into the architecture, not added later. AWS data engineering is about designing layered, secure, and scalable architectures where S3 acts as the data lake, Glue/EMR handle processing, Athena/Redshift enable analytics, and IAM governs access, supporting both batch and streaming use cases. #AWS #DataEngineering #CloudComputing #BigData #DataLake #ETL #ELT #Analytics #AWSGlue #AmazonS3 #AmazonRedshift #Athena #Kinesis #EMR #QuickSight #CloudArchitecture #TechCareers ArchitectureDiagram

AWS Data Engineering has 4 levels to it: – Level 1: Ingesting & Storing Data Start by learning the foundations of AWS data services: - S3 for data lakes (folders, prefixes, lifecycle policies) - AWS Glue Crawlers + Data Catalog for schema discovery - Kinesis / AWS DMS for streaming + CDC ingestion - IAM basics (roles, policies, S3 bucket access) With just these basics, you can already build working ETL pipelines. – Level 2: Transforming & Querying Data Move from storing data to making it usable: - Glue ETL & PySpark jobs (batch transformation) - AWS Lambda for lightweight event-driven processing - Athena + S3 for serverless SQL on data lakes - Redshift for warehousing and complex analytics This is where your data becomes queryable and structured for analytics – Level 3: Building Scalable Data Platforms Upgrade from pipelines to full data platforms: - Lakehouse architecture with Iceberg/Delta on S3 - Glue Workflow/Step Functions for orchestration - Data partitioning, file formats (Parquet, ORC, Avro) - Performance tuning (compaction, distribution keys, sort keys) Here’s where you shift from “data exists” to “data is optimized and reliable.” – Level 4: Operating at Scale Finally, learn to make your platforms efficient, secure, and enterprise-ready: - EMR + Spark clusters for high-volume processing - Data quality + observability (Great Expectations, Deequ, CloudWatch) - Cost optimization (S3 tiers, Redshift RA3, Glue job tuning) - Security & compliance (KMS encryption, VPC endpoints, Lake Formation, GDPR/SOC2) - Streaming at scale with Kinesis Data Streams / Firehose / MSK What else would you add?

Catastrophic risk modeling means living in a world of gigabytes, terabytes, and sometimes petabytes per analytics run. I talked with Karthick Shanmugam from Verisk, a market leader in risk modeling for insurance and reinsurance, about how they’re handling that scale on AWS. Their architecture uses: Amazon S3 + Apache Iceberg as the scalable, open data storage layer  Amazon Redshift as the analytical processing engine – https://lnkd.in/eW5Y_Qnc Amazon QuickSight for visualization – https://lnkd.in/eukavW7T Amazon EC2 and the broader AWS ecosystem around it They’re analyzing massive risk datasets and seeing performance improvements on the order of 10-15x (depending on the use case) when using Redshift to aggregate and visualize data for customers. His team is moving from tightly coupled storage + compute to separating storage (S3 + Iceberg) and compute (Redshift), so storage can evolve independently while customers choose the right compute for their needs. If you’re in a similar high-scale analytics space, Karthik’s recommendation is to use an open table format on S3 and pair it with a strong analytical engine like Amazon Redshift to get both flexibility and speed.

𝗪𝗵𝗮𝘁'𝘀 𝘁𝗵𝗲 𝗙𝘂𝘀𝘀 𝗔𝗯𝗼𝘂𝘁 𝗔𝗺𝗮𝘇𝗼𝗻 𝗦𝟯 𝗧𝗮𝗯𝗹𝗲𝘀? At AWS re:Invent 2024, Amazon S3 Tables generated a lot of buzz. Claims of up to 3x faster queries and 10x higher transactions per second for analytics workloads were interesting to note. 𝗪𝗵𝗮𝘁 𝗔𝗿𝗲 𝗔𝗺𝗮𝘇𝗼𝗻 𝗦𝟯 𝗧𝗮𝗯𝗹𝗲𝘀? S3 Tables combine the power of Apache Iceberg with the scalability of Amazon S3. Think of them as a managed service for storing and querying tabular data optimized for analytics. 𝗜𝗰𝗲𝗯𝗲𝗿𝗴 is an open-source table format designed for managing large-scale datasets, while the underlying data in these tables is often stored in 𝗔𝗽𝗮𝗰𝗵𝗲 𝗣𝗮𝗿𝗾𝘂𝗲𝘁 files, a columnar file format optimized for analytics. 𝗪𝗵𝘆 𝘁𝗵𝗲 𝗘𝘅𝗰𝗶𝘁𝗲𝗺𝗲𝗻𝘁? • 𝗕𝗹𝗮𝘇𝗶𝗻𝗴 𝗙𝗮𝘀𝘁: Experience significantly faster query performance and increased throughput than traditional approaches. • 𝗦𝗶𝗺𝗽𝗹𝗶𝗳𝗶𝗲𝗱 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁: Say goodbye to manual tasks! S3 Tables automate maintenance like compaction and cleanup. • 𝗘𝗻𝗵𝗮𝗻𝗰𝗲𝗱 𝗗𝗮𝘁𝗮 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁: Leverage features like schema evolution, row-level transactions, and time travel for greater flexibility. 𝗛𝗼𝘄 𝗗𝗼𝗲𝘀 𝗜𝘁 𝗪𝗼𝗿𝗸? • S3 Tables use a new type of bucket called a table bucket, explicitly designed for managing Iceberg tables. • These buckets organize data into namespaces and tables, making managing structured datasets at scale easy. • You can query these tables using familiar tools like Amazon Athena, Redshift, Apache Spark, or other engines that support Iceberg. 𝗪𝗵𝗼 𝗕𝗲𝗻𝗲𝗳𝗶𝘁𝘀? • 𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝘀 & 𝗔𝗻𝗮𝗹𝘆𝘀𝘁𝘀: Gain faster insights for quicker decision-making. • 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝘁𝗶𝘀𝘁𝘀 & 𝗠𝗟 𝗣𝗿𝗮𝗰𝘁𝗶𝘁𝗶𝗼𝗻𝗲𝗿𝘀: Accelerate model training with efficient access to massive datasets. • 𝗕𝗜 𝗧𝗲𝗮𝗺𝘀: Streamline reporting with seamless integration into popular AWS analytics tools. 𝗘𝘅𝗮𝗺𝗽𝗹𝗲: Imagine analyzing daily sales data for an e-commerce company. S3 Tables could enable real-time insights into sales trends, allowing for rapid adjustments to marketing campaigns. 𝗙𝗶𝗻𝗮𝗹 𝗧𝗵𝗼𝘂𝗴𝗵𝘁𝘀 Amazon S3 Tables bridge the gap between traditional data lakes and modern data warehouses. Combining the scalability of S3 with the structure of Iceberg tables and the efficiency of Parquet files eliminates trade-offs between performance and simplicity. This is worth exploring if you're working on analytics workloads or building a lakehouse architecture.  I've only scratched the surface here! #AWS #awscommunity

Staring at the AWS console, it's easy to get lost in a sea of 200+ services. When I first approached data engineering on AWS, I made a classic mistake: trying to memorize what each service does in isolation. It was overwhelming and, frankly, the wrong way to look at it. The real "a-ha" moment came when I stopped thinking about individual services and started following the data. It turns out, a single piece of data has a complex lifecycle, and each stage requires a purpose-built tool. Here’s the end-to-end data flow I'm mapping out: 1. The Entry Point (Ingestion) This is where data is born or enters the ecosystem. It’s not one-size-fits-all. It could be transactional data from Amazon RDS, a real-time stream from Amazon Kinesis, or a massive batch migration using AWS DMS. 2. The Central Hub (Storage) Before any major processing, all raw data from all those sources lands in Amazon S3. This is the durable, flexible, and massively scalable "single source of truth." It's the core of a modern data lake. 3. The Factory (Transformation) Raw data is messy and rarely useful on its own. This is where AWS Glue or EMR come in. They are the engines that catalog, clean, and transform that raw data into a pristine, analysis-ready format. 4. The Storefront (Serving) Once transformed, who needs it? This access layer serves the right data to the right user: Analysts get Amazon Redshift for complex BI dashboard queries. Applications get Amazon DynamoDB (for low-latency) or Amazon RDS (for relational access). Data Scientists get Amazon Athena to query data directly in S3 for ad-hoc analysis. My key insight? S3 (as the lake) and Glue (as the catalog) are the true heart of this entire system. They create a decoupled architecture that lets all these other specialized compute and query services plug in and play their part. It's a fundamental shift in thinking.

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

🚀 Modern Data Platform on AWS – From Ingestion to Analytics This architecture showcases how a scalable and secure data platform can be built on AWS by combining cloud-native services with strong automation and governance. 🔹 Ingestion: Data flows from Salesforce and external databases using Amazon AppFlow and AWS Glue 🔹 Storage: Amazon S3 acts as the central data lake with fine-grained access control via AWS Lake Formation 🔹 Processing & Transformation: ELT pipelines orchestrated on Amazon EKS using tools like Argo, dbt, and Kubeflow 🔹 Analytics: Amazon Redshift with Spectrum enables seamless querying across warehouse and data lake 🔹 Security & Governance: Managed through AWS Firewall Manager and Lake Formation permissions 🔹 Automation: Infrastructure provisioned using AWS CDK and deployed via GitLab CI runners This kind of design enables scalability, cost efficiency, strong governance, and faster analytics delivery—while keeping operations fully automated and secure. 💡 A great example of how cloud-native services come together to support enterprise-grade data platforms. #AWS #DataEngineering #CloudArchitecture #DataPlatform #Analytics #ELT #BigData