Data Warehousing Architecture

Still building data platforms without clear design patterns? That’s where most pipelines break. This visual is a powerful reminder that data engineering isn’t about tools — it’s about patterns. Modern data systems scale not because of Spark, Snowflake, or Kafka… They scale because the right architectural patterns are applied at the right time. 🧩 What this image breaks down clearly 🔹 Ingestion Design Patterns • Batch ingestion for cost-efficient historical loads • Streaming ingestion for real-time use cases • CDC for low-latency, low-impact data movement 🔹 Storage Design Patterns • Data Lake for raw, flexible storage • Data Warehouse for curated analytics • Lakehouse for combining flexibility + performance 🔹 Transformation Patterns • ETL for schema-first, compliance-heavy systems • ELT for agile analytics and scalability • Incremental processing to avoid reprocessing everything 🔹 Orchestration & Workflow • DAG-based pipelines for complex dependencies • Event-driven pipelines for real-time architectures 🔹 Reliability & Fault Tolerance • Idempotent pipelines (safe re-runs) • Retry & dead-letter queues • Backfill patterns for safe historical reprocessing 🔹 Data Quality & Governance • Validation checks (nulls, ranges, constraints) • Schema evolution without breaking consumers • Data lineage for trust, debugging, and compliance 🔹 Serving & Consumption • Semantic layers to abstract complexity • API-based serving instead of direct table access 🔹 Performance & Scalability • Partitioning for faster queries • Caching to reduce compute and latency 🔹 Cost Optimization • Tiered storage for retention compliance • On-demand compute to avoid idle spend 🎯 Why this matters If you’re: • Designing a modern data platform • Scaling analytics for multiple teams • Migrating to cloud or lakehouse • Building real-time or AI-ready pipelines 👉 These patterns matter more than any single tool choice. 📌 Bookmark this. 📤 Share it with your data team. Question for you: Which of these patterns has saved you the most pain in production — and which one do teams usually ignore until it’s too late? #DataEngineering #DataArchitecture #AnalyticsEngineering #BigData #CloudData #ModernDataStack #Lakehouse #DataGovernance

{{Modern enterprises don’t just collect data — they operationalize it.}} This AWS + Snowflake ETL architecture is designed for scalable, secure, and business-ready data pipelines across industries like financial services, e-commerce, healthcare, and SaaS. It supports batch and near-real-time ingestion, ensures data quality, and powers business intelligence & AI/ML initiatives. >>Where We Use This Architecture Financial Services → Fraud detection, credit risk scoring, regulatory compliance reporting. E-Commerce → Real-time customer behavior analytics, personalization, inventory optimization. Healthcare → Patient data integration, operational efficiency dashboards, predictive care analytics. SaaS Products → Usage analytics, product performance metrics, customer churn prediction. >> Architecture Walkthrough > Data Sources Relational: RDS (Postgres), operational DBs Streaming: Kafka, Kinesis APIs: External & 3rd-party data feeds >>Ingestion Layer AWS DMS → Continuous replication from databases AWS Glue (Ingest) → Scheduled batch ETL jobs Kinesis → Real-time data streaming from applications > Landing & Raw Zone (S3) Data stored in Landing (raw) and Bronze layers for full history & auditability >Processing Layer Databricks (PySpark) & EMR Spark for large-scale transformations Great Expectations for automated data quality checks > Orchestration & Automation Airflow (MWAA) for dependency-based scheduling AWS Step Functions & Lambda for event-driven workflows >Data Warehouse (Snowflake) Staging → Core → Business Marts modeled with dbt for version control & testing >Consumption Layer Power BI, Looker, Ad-hoc SQL for self-service analytics & decision-making > Monitoring & DevOps CloudWatch for real-time pipeline health monitoring GitHub Actions + Terraform for CI/CD & infrastructure as code ^Business Impact^ >Faster Time-to-Insight → From 12 hours down to 1 hour for complex ETL runs > Better Data Quality → 95%+ pass rate on automated data checks > Scalability → Handles 100M+ rows/day without performance degradation > Audit & Compliance → Full lineage and historical tracking for regulations like GDPR, HIPAA, PCI-DSS #DataEngineering #Snowflake #AWS #Databricks #ETL #DataPipeline #Airflow #dbt #CloudArchitecture #DataQuality #BigData #AnalyticsEngineering #MachineLearning #C2C #C2H #UsITrecruiters

The Evolution of Data Architectures: From Warehouses to Meshes As data continues to grow exponentially, our approaches to storing, managing, and extracting value from it have evolved. Let's revisit four key data architectures: 1. Data Warehouse    • Structured, schema-on-write approach    • Optimized for fast querying and analysis    • Excellent for consistent reporting    • Less flexible for unstructured data    • Can be expensive to scale    Best For: Organizations with well-defined reporting needs and structured data sources. 2. Data Lake    • Schema-on-read approach    • Stores raw data in native format    • Highly scalable and flexible    • Supports diverse data types    • Can become a "data swamp" without proper governance    Best For: Organizations dealing with diverse data types and volumes, focusing on data science and advanced analytics. 3. Data Lakehouse    • Hybrid of warehouse and lake    • Supports both SQL analytics and machine learning    • Unified platform for various data workloads    • Better performance than traditional data lakes    • Relatively new concept with evolving best practices    Best For: Organizations looking to consolidate their data platforms while supporting diverse use cases. 4. Data Mesh    • Decentralized, domain-oriented data ownership    • Treats data as a product    • Emphasizes self-serve infrastructure and federated governance    • Aligns data management with organizational structure    • Requires significant organizational changes    Best For: Large enterprises with diverse business domains and a need for agile, scalable data management. Choosing the Right Architecture: Consider factors like: - Data volume, variety, and velocity - Organizational structure and culture - Analytical and operational requirements - Existing technology stack and skills Modern data strategies often involve a combination of these approaches. The key is aligning your data architecture with your organization's goals, culture, and technical capabilities. As data professionals, understanding these architectures, their evolution, and applicability to different scenarios is crucial. What's your experience with these data architectures? Have you successfully implemented or transitioned between them? Share your insights and let's discuss the future of data management!

Most data strategies fail for one reason: They are built on outdated architecture assumptions. In 2026, the question is no longer “Do we need a data warehouse or a data lake?” That debate is already over. Modern data systems are composed, event-driven, and AI-aware. 𝐇𝐞𝐫𝐞 𝐢𝐬 𝐡𝐨𝐰 𝐥𝐞𝐚𝐝𝐢𝐧𝐠 𝐭𝐞𝐚𝐦𝐬 𝐚𝐫𝐞 𝐭𝐡𝐢𝐧𝐤𝐢𝐧𝐠 𝐚𝐛𝐨𝐮𝐭 𝐝𝐚𝐭𝐚 𝐚𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞 𝐧𝐨𝐰: → 𝐖𝐚𝐫𝐞𝐡𝐨𝐮𝐬𝐞 𝐢𝐬 𝐬𝐭𝐢𝐥𝐥 𝐫𝐞𝐥𝐞𝐯𝐚𝐧𝐭 • Strong for governed analytics and reporting • But no longer the center of gravity → 𝐋𝐚𝐤𝐞 𝐢𝐬 𝐧𝐨𝐰 𝐟𝐨𝐮𝐧𝐝𝐚𝐭𝐢𝐨𝐧𝐚𝐥 • Cheap storage for raw and semi-structured data • Rarely used standalone → 𝐋𝐚𝐤𝐞𝐡𝐨𝐮𝐬𝐞 𝐡𝐚𝐬 𝐛𝐞𝐜𝐨𝐦𝐞 𝐝𝐞𝐟𝐚𝐮𝐥𝐭 • Combines storage + compute flexibility • Backbone for BI + AI workloads → 𝐒𝐭𝐫𝐞𝐚𝐦𝐢𝐧𝐠-𝐟𝐢𝐫𝐬𝐭 𝐢𝐬 𝐫𝐢𝐬𝐢𝐧𝐠 𝐟𝐚𝐬𝐭 • Real-time data is becoming the baseline • Critical for AI, personalization, fraud detection → 𝐊𝐚𝐩𝐩𝐚 𝐨𝐯𝐞𝐫 𝐋𝐚𝐦𝐛𝐝𝐚 • Treat everything as streams • Simpler operational model at scale → 𝐃𝐚𝐭𝐚 𝐌𝐞𝐬𝐡 (𝐨𝐫𝐠 𝐩𝐫𝐨𝐛𝐥𝐞𝐦, 𝐧𝐨𝐭 𝐣𝐮𝐬𝐭 𝐭𝐞𝐜𝐡) • Domain ownership of data products • Requires cultural and governance maturity → 𝐃𝐚𝐭𝐚 𝐅𝐚𝐛𝐫𝐢𝐜 (𝐜𝐨𝐧𝐭𝐫𝐨𝐥 𝐩𝐥𝐚𝐧𝐞 𝐭𝐡𝐢𝐧𝐤𝐢𝐧𝐠) • Metadata-driven integration across systems • Focus on governance + discoverability → 𝐄𝐯𝐞𝐧𝐭-𝐝𝐫𝐢𝐯𝐞𝐧 𝐚𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞𝐬 • Decouple producers and consumers • Foundation for scalable, reactive systems → 𝐀𝐈-𝐧𝐚𝐭𝐢𝐯𝐞 𝐝𝐚𝐭𝐚 𝐬𝐭𝐚𝐜𝐤𝐬 • Vector DBs, feature stores, model pipelines • Data architecture now directly powers AI systems → 𝐂𝐨𝐦𝐩𝐨𝐬𝐚𝐛𝐥𝐞 𝐬𝐭𝐚𝐜𝐤 • Decoupled storage, compute, and serving • Avoid vendor lock-in, increase flexibility → 𝐑𝐞𝐯𝐞𝐫𝐬𝐞 𝐄𝐓𝐋 𝐜𝐥𝐨𝐬𝐞𝐬 𝐭𝐡𝐞 𝐥𝐨𝐨𝐩 • Push data back into operational systems • Turn insights into actions The shift is clear: Data architecture is no longer about where data lives. It is about how data flows, is governed, and creates value in real time. P.S. Which of these architectures is becoming central in your stack today? Follow Ashish Joshi for more insights

𝗧𝗵𝗲 𝘀𝗵𝗶𝗳𝘁 𝗳𝗿𝗼𝗺 𝗱𝗮𝘁𝗮 𝘄𝗮𝗿𝗲𝗵𝗼𝘂𝘀𝗲𝘀 𝘁𝗼 𝗹𝗮𝗸𝗲-𝗰𝗲𝗻𝘁𝗿𝗶𝗰 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲𝘀 𝗶𝘀𝗻’𝘁 𝗮 𝘁𝗿𝗲𝗻𝗱. It’s survival. Modern data teams aren’t struggling because of 𝘁𝗼𝗼𝗹𝘀 — they’re struggling because of 𝘀𝗰𝗮𝗹𝗲, 𝘀𝗽𝗲𝗲𝗱, 𝗮𝗻𝗱 𝗰𝗼𝗺𝗽𝗹𝗲𝘅𝗶𝘁𝘆. And that’s where architecture decisions quietly make or break platforms. The real question is not: ❌ 𝗪𝗵𝗮𝘁’𝘀 𝗽𝗼𝗽𝘂𝗹𝗮𝗿 𝗿𝗶𝗴𝗵𝘁 𝗻𝗼𝘄? ✅ 𝗪𝗵𝗮𝘁 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗳𝗶𝘁𝘀 𝘁𝗵𝗲 𝗽𝗿𝗼𝗯𝗹𝗲𝗺? Here’s how I usually think about architecture choices ⬇️ 𝗠𝗲𝗱𝗮𝗹𝗹𝗶𝗼𝗻 (𝗕𝗿𝗼𝗻𝘇𝗲–𝗦𝗶𝗹𝘃𝗲𝗿–𝗚𝗼𝗹𝗱) A disciplined, layered approach that brings order to chaos. Works best when data quality, incremental loads, and reliability matter more than speed alone. 𝗗𝗮𝘁𝗮 𝗠𝗲𝘀𝗵 Domain-driven ownership with federated governance. Extremely powerful — but only when ownership, standards, and contracts are real (not aspirational). 𝗧𝗿𝗮𝗱𝗶𝘁𝗶𝗼𝗻𝗮𝗹 𝗗𝗮𝘁𝗮 𝗪𝗮𝗿𝗲𝗵𝗼𝘂𝘀𝗲 Still undefeated for analytics-heavy workloads. Fast SQL, complex joins, historical reporting, and a clear single source of truth. From 𝗟𝗮𝗺𝗯𝗱𝗮, 𝗞𝗮𝗽𝗽𝗮, 𝗗𝗮𝘁𝗮 𝗩𝗮𝘂𝗹𝘁, 𝗟𝗮𝗸𝗲𝗵𝗼𝘂𝘀𝗲 and beyond — every architecture exists because someone hit a real limitation and had to solve it. There is no 𝗯𝗲𝘀𝘁 architecture. There is only 𝗳𝗶𝘁. A few lessons from building and supporting real platforms: • Architecture should follow business constraints, not hype cycles • Hybrid approaches (Lakehouse, Mesh + Medallion) are far more common than “pure” models • The best designs reduce pipeline fragility — not just query latency If you’re choosing today, ask yourself: – Need strong governance with modern BI? → Medallion – Query performance and historical analytics matter most? → Data Warehouse – Large org with clear domain ownership? → Data Mesh Good architecture doesn’t show off. It quietly enables 𝘀𝗰𝗮𝗹𝗲, 𝘁𝗿𝘂𝘀𝘁, 𝗮𝗻𝗱 𝗰𝗵𝗮𝗻𝗴𝗲. 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 𝗶𝘀 𝘁𝗵𝗲 𝗹𝗼𝗻𝗴-𝘁𝗲𝗿𝗺 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻 𝘆𝗼𝘂 𝗺𝗮𝗸𝗲 𝗼𝗻 𝗱𝗮𝘆 𝗼𝗻𝗲. Curious — 𝗵𝗼𝘄 𝗱𝗼 𝘆𝗼𝘂 𝗱𝗲𝗰𝗶𝗱𝗲 𝘄𝗵𝗶𝗰𝗵 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 𝘁𝗼 𝘂𝘀𝗲 𝗶𝗻 𝘆𝗼𝘂𝗿 𝗽𝗿𝗼𝗷𝗲𝗰𝘁𝘀? What worked, and what didn’t? ♻️ 𝗥𝗲𝗽𝗼𝘀𝘁 𝗶𝗳 𝘁𝗵𝗶𝘀 𝘄𝗮𝘀 𝗵𝗲𝗹𝗽𝗳𝘂𝗹. 🔔 𝗙𝗼𝗹𝗹𝗼𝘄 Aishwarya Pani 𝗳𝗼𝗿 𝗺𝗼𝗿𝗲 𝗶𝗻𝘀𝗶𝗴𝗵𝘁𝘀 𝗼𝗻 𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴.

Most data engineers argue about tools. Elite ones argue about architecture. And the scary part? Most teams don’t realize they picked the wrong one until 18 months later. I’ve reviewed 200+ data systems in the last 3 years. 90% weren’t failing because of bad engineering. They were failing because the architecture didn’t match: • Org structure • Data volume • Latency expectations • Team maturity Same 3 architectures keep showing up. But only 1 in 5 engineers truly know when to use which. Here’s the real breakdown 👇 🕸️ 𝗗𝗔𝗧𝗔 𝗠𝗘𝗦𝗛 — “𝗔𝘂𝘁𝗼𝗻𝗼𝗺𝘆 𝗮𝘁 𝗦𝗰𝗮𝗹𝗲” Domain-owned data products. Decentralized responsibility. Federated governance. ✅ 𝗨𝘀𝗲 𝘄𝗵𝗲𝗻: • You have multiple mature domain teams • Data ownership conflicts are slowing delivery • Central data team has become a bottleneck ❌ 𝗔𝘃𝗼𝗶𝗱 𝗶𝗳: • Your teams aren’t technically mature • You lack strong governance standards Mesh without discipline = distributed chaos. 🏛️ 𝗗𝗔𝗧𝗔 𝗪𝗔𝗥𝗘𝗛𝗢𝗨𝗦𝗘 — “𝗖𝗼𝗻𝘁𝗿𝗼𝗹 & 𝗖𝗼𝗻𝘀𝗶𝘀𝘁𝗲𝗻𝗰𝘆” Centralized. Structured. Trusted. ETL → Curated models → BI layer. ✅ 𝗨𝘀𝗲 𝘄𝗵𝗲𝗻: • You need reliable reporting • Executives care about one version of truth • Compliance & governance matter ❌ 𝗔𝘃𝗼𝗶𝗱 𝗶𝗳: • You need ultra-low latency ML pipelines • Teams require rapid experimentation • Warehouses optimize for trust, not speed. 🥇 𝗠𝗘𝗗𝗔𝗟𝗟𝗜𝗢𝗡 — “𝗦𝗽𝗲𝗲𝗱 𝘄𝗶𝘁𝗵 𝗦𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲” Bronze → Silver → Gold. Batch + streaming. AI-ready pipelines. ✅ 𝗨𝘀𝗲 𝘄𝗵𝗲𝗻: • You need ML/AI at scale • Raw + refined data must coexist • Real-time insights matter ❌ 𝗔𝘃𝗼𝗶𝗱 𝗶𝗳: • You don’t actually need layered complexity • Not every company needs Gold tables on day one. 🎯 𝗧𝗛𝗘 𝗥𝗘𝗔𝗟 𝗩𝗘𝗥𝗗𝗜𝗖𝗧 There is no “best” architecture. There is only: • The one your org can execute well • The one your teams can sustain • The one aligned with your business velocity Pick wrong — and you’ll spend 2 years refactoring pipelines instead of building value. Pick right — and architecture becomes invisible. And that’s the goal. If this made you rethink your stack, repost ♻️ Because someone in your org is about to make this decision. 💬 What’s your company running on — Mesh, Warehouse, Medallion… or a hybrid nobody admits? 👀 #DataEngineering #DataArchitecture #DataMesh #Lakehouse #DataWarehouse #BigData #Analytics #MLOps #TechLeadership

𝐃𝐚𝐭𝐚 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞 𝐓𝐡𝐚𝐭 𝐀𝐜𝐭𝐮𝐚𝐥𝐥𝐲 𝐌𝐚𝐭𝐭𝐞𝐫𝐬: 𝟔 𝐏𝐚𝐭𝐭𝐞𝐫𝐧𝐬 𝐒𝐡𝐚𝐩𝐢𝐧𝐠 𝐌𝐨𝐝𝐞𝐫𝐧 𝐃𝐚𝐭𝐚 𝐒𝐲𝐬𝐭𝐞𝐦𝐬 Data warehouse vs data lake was yesterday's debate. Today's decision is between six architectures each solving a different problem. Here is what matters and when to use each. 𝟏. 𝐋𝐀𝐊𝐄𝐇𝐎𝐔𝐒𝐄 • Combines data lakes and warehouses with open formats and separate compute.  • Serves as the standard backbone for modern analytics. • Sources to Ingestion to Storage + Table Format to BI + AI Pick Lakehouse when you need both analytics and AI on the same data without maintaining two separate systems. This is the default starting point for most modern data teams. 𝟐. 𝐒𝐓𝐑𝐄𝐀𝐌𝐈𝐍𝐆-𝐅𝐈𝐑𝐒𝐓 • Built around continuous data streams instead of batch processing.  • Powers real-time use cases like personalization, fraud detection, and AI. • Event Sources to Stream Bus to Processing to Apps Pick Streaming-First when batch processing is too slow. If your business decisions depend on data that is minutes or seconds old not hours this is your architecture. 𝟑. 𝐃𝐀𝐓𝐀 𝐌𝐄𝐒𝐇 • Promotes decentralized ownership where teams manage their own data products.  • Blends organizational structure with data architecture. • Domain Sources to Data Products to Platform to Consumers Pick Data Mesh when your organization is large enough that centralized data teams have become a bottleneck. Each domain owns its data as a product. This is an organizational shift as much as a technical one. 𝟒. 𝐀𝐈-𝐍𝐀𝐓𝐈𝐕𝐄 • Designed specifically for machine learning and generative AI systems.  • Supports low-latency inference with consistent online and offline data. • Sources to Feature Store to Models to Applications Pick AI-Native when ML and GenAI are your primary workloads. The feature store ensures training and serving use the same data eliminating the train-serve skew that breaks models in production. 𝟓. 𝐂𝐎𝐌𝐏𝐎𝐒𝐀𝐁𝐋𝐄 𝐒𝐓𝐀𝐂𝐊 • Separates storage, compute, and serving for flexibility.  • Enables using multiple tools without heavy vendor dependence. • Sources to Storage to Compute to APIs Pick Composable Stack when you want to avoid vendor lock-in and need the flexibility to swap tools at any layer. Best for teams with strong engineering talent who want maximum control. 𝟔. 𝐑𝐄𝐕𝐄𝐑𝐒𝐄 𝐄𝐓𝐋 • Moves processed data back into operational platforms.  • Bridges the gap between insights and real-world actions. • Warehouse to Transform to Sync to Apps Pick Reverse ETL when your analytics insights are stuck in dashboards nobody checks. This pattern pushes data from your warehouse back into CRM, marketing, and operational tools where teams actually work. Which architecture pattern is your team building on? ♻️ Repost this to help your network get started ➕ Follow Jaswindder for more #DataArchitecture #DataEngineering #DevOps

Modern ELT in Action: AWS + Snowflake + dbt 🚀❄️ This architecture perfectly shows how modern data teams build scalable, reliable analytics pipelines using ELT instead of traditional ETL. Here’s what’s happening at a glance : 📥 Data Ingestion Raw data (JSON files, uploads, events) lands in AWS S3 and is loaded directly into Snowflake staging tables. No heavy transformations upfront. ⚙️ Orchestration Apache Airflow schedules and orchestrates the entire workflow, ensuring pipelines run reliably and in the right order. 🔁 Transformation with dbt dbt transforms data inside Snowflake using SQL—turning raw data into trusted analytics models. 🥉 Bronze Layer – Raw, source-aligned data 🥈 Silver Layer – Cleaned, validated, standardized data 🥇 Gold Layer – Business-ready tables for analytics and reporting 📊 Outcome Faster pipelines, simpler architecture, better data quality, and analytics teams working directly on trusted data. This is why ELT + dbt has become the backbone of the modern data stack—less complexity, more scalability, and analytics that actually deliver value. #Snowflake #dbt #DataEngineering #ELT #ModernDataStack #AWS #Airflow #AnalyticsEngineering #DBT InfoDataWorx #DataWarehouse #CloudData #SQL #DataModeling #BigData #DataAnalytics

Day 10 – End-to-End Enterprise Data Architecture #InterviewQuestion Design an end-to-end data architecture for a large-scale system. #Step1 – Start With a Strong Story In one of our projects, we designed a scalable data architecture to process data from multiple sources such as transactional systems, APIs, and streaming platforms. The goal was to build a reliable pipeline that supports both real-time analytics and batch processing. #Step2 – High-Level Architecture End-to-End Flow Source Systems ↓ Ingestion Layer ↓ Data Lake (Bronze) ↓ Processing Layer (Silver) ↓ Aggregation Layer (Gold) ↓ Data Warehouse ↓ BI / Analytics #Step3 – Components Explained (Simple Way) 1. Source Systems Where data comes from: Databases (MySQL, PostgreSQL) APIs Logs Streaming (Kafka) 2. Ingestion Layer Moves data into the system. Tools: Kafka (real-time) Batch ingestion (files, APIs) CDC tools. 3. Data Lake (Bronze Layer) Stores raw data. Storage: AWS S3 / ADLS / GCS Format: Parquet / Delta.  4. Processing Layer (Silver Layer) Cleans and transforms data. Tools: PySpark / Spark Operations: Deduplication Filtering Joins. 5. Gold Layer (Business Layer) Final curated data. Examples: Sales summary Customer analytics. 6. Data Warehouse Used for analytics. Examples: Snowflake Redshift BigQuery. 7. BI Layer Used by business users. Tools: Power BI Tableau. In our project, we designed an end-to-end data architecture where data was ingested from multiple sources using both batch and streaming mechanisms. The raw data was stored in a data lake in the Bronze layer. We used Spark for data transformation and built Silver and Gold layers for cleaned and aggregated data. The final data was loaded into a data warehouse and used for analytics through BI tools. Karthik K. #DataEngineering #DataArchitecture #DataPipeline #PySpark #ApacheSpark #BigData #ETL #DataEngineer #DeltaLake #SystemDesign #TechLearning #InterviewPreparation #DataAnalytics. End-to-End Code: