Decoupled Spring Boot Analytics with AWS Athena & QuickSight

Site Reliability Engineering (SRE) for Data Warehousing (DWH) applies software engineering principles to the management, monitoring, and optimization of data storage and analytics systems. While SRE is often associated with DevOps and application uptime, applying SRE to DWH ensures that data pipelines are reliable, data is available on time, and analytical queries perform efficiently. Key Aspects of SRE for DWH: SLIs/SLOs for Data: Instead of traditional application uptime, SRE for DWH focuses on data-centric metrics: Data Freshness (Latency): When is the data available for analytics? Data Quality/Integrity: Are there nulls or unexpected values? Pipeline Reliability: Did the ETL/ELT process complete successfully? Automation of Data Pipelines: Reducing manual intervention (toil) in ELT/ETL processes using tools like Airflow, dbt, or cloud-native tools (Azure Data Factory, AWS Glue). Monitoring and Observability: Using tools like Prometheus, Grafana, Datadog, or cloud-native monitoring (Cloud Monitoring) to gain visibility into pipeline health and query performance. Incident Response & Post-Mortems: When a data pipeline breaks, SRE teams conduct post-mortems to find the root cause, ensuring the same failure doesn't happen again. Capacity Management: Managing the storage and compute, especially for cloud warehouses like Snowflake or BigQuery, to balance cost with performance. Common DWH Technologies Managed by SRE: Cloud Data Warehouses: Snowflake, Google BigQuery, Amazon Redshift, Azure Synapse Analytics. ETL/Data Pipeline Tools: Apache Airflow, dbt, Informatica.  Benefits of SRE in Data Warehousing: Improved Data Trust: Consistent, high-quality, and timely data for business intelligence. Reduced Operational Overhead: Automated monitoring and alerting reduce the need for manual troubleshooting. Scalability: Handling larger, faster-moving data volumes without sacrificing performance. Core Concepts of SRE for Data Warehouse Data Reliability Engineering (DRE): A specialized form of SRE for data platforms, using tools like Airflow (orchestration) and Snowflake or BigQuery(storage/compute). Data Quality as Reliability: Instead of just "server up/down," SRE in DWH monitors if data arrived on time, if records are duplicated, or if schemas have broken, which are treated as "incidents". SLIs/SLOs for Data: SRE defines Service Level Indicators (SLIs) for data, such as "percentage of data pipelines completed by 8:00 AM" or "accuracy of data in the dashboard". Key Responsibilities of SRE in Data Engineering Automating Data Pipelines: Replacing manual interventions in ETL/ELT processes with automated, self-healing code. Monitoring and Alerting: Setting up proactive monitoring for data freshness, pipeline failures, and latency in data loading. Incidents and Post-Mortems: Investigating data quality issues (e.g., missing data in a report) by performing root cause analysis, similar to a server outage analysis. #SRE #AI #Datawarehouse #DWH #ETL

The Medallion Architecture: Strategic Asset or Operational Overhead? The Medallion Architecture, popularised by Databricks, is widely adopted as a default pattern for modern Lakehouse platforms. By structuring data into Bronze, Silver, and Gold layers, it offers a clear, disciplined approach to data refinement. But adopting all three layers should be a deliberate engineering decision, not a reflex driven by industry trends. The Architecture Breakdown Medallion structures data into three progressive layers: => Bronze (Raw): Ingests source data as-is. Typically immutable, append-only, and replayable, allowing recovery and reprocessing without re-querying operational systems. => Silver (Cleansed and Conformed): The integration layer. Applies schema enforcement, standardisation, deduplication, and core business rules. This is where data becomes analytically trustworthy. => Gold (Curated): Optimised, domain-specific datasets designed for BI, reporting, and downstream ML/AI workloads. Performance and usability take priority here. When the Pattern Wins The Medallion approach is highly effective for teams that prioritise lineage, auditability, and scalability: => Root-cause analysis: With proper lineage, issues in Gold trace back through Silver to Bronze, isolating whether problems originated in the source data or in the transformation logic. => Reprocessing at scale: Changes in logic don’t require re-pulling data from APIs or OLTP systems => Regulatory compliance: Immutable raw data supports audit, reconciliation, and data retention requirements. => Growing complexity: Multiple sources, domains, and teams benefit from clear layer boundaries. The Trade-offs for Small or Agile Teams For simpler use cases, rigid medallion implementation creates unnecessary overhead: => Complexity tax: Each layer adds orchestration, monitoring, latency, storage and compute cost. => Reduced agility: Clean, low-volume sources don’t always justify multiple transformation hops. => Premature optimisation: Designing for hypothetical future scale often delays real business outcomes. A Pragmatic, Balanced Approach The goal is progressive refinement, not mandatory multi-stage hops. If a full Medallion structure hinders productivity: => Start with "Bronze-to-Gold": Skip the intermediate Silver layer if your transformations are simple. => Value Over "Best Practice": Prioritise a structure that serves current query patterns over solving theoretical problems you don't have yet. => Rename for Clarity: Use terms that fit your organisation (e.g., landing/core/mart) to ensure stakeholders understand the architecture. The Medallion Architecture is a powerful pattern for data quality at scale. But the best architecture balances governance and correctness with delivery speed and team capacity, not the one that follows convention. Build for the problems you have, not necessarily the scale you aspire to.

1️⃣ Data Warehouse The Data Warehouse architecture follows a structured flow: sources → ETL → centralized warehouse. Data from operational systems is extracted, transformed into consistent schemas, and loaded into a relational repository optimized for analytical queries. Platforms like Snowflake and Amazon Redshift are commonly used for this purpose. This model excels at historical reporting, business intelligence dashboards, and regulatory reporting where structured, clean, and curated data is essential. 2️⃣ Data Lake A Data Lake architecture prioritizes flexibility and scalability. Data flows from various structured and unstructured sources into a centralized storage layer, often cloud object storage such as Amazon S3. Data is stored in raw and refined zones, allowing organizations to retain original formats while enabling transformation later. Technologies like Apache Hadoop support large-scale storage and processing. This approach is cost-efficient and well-suited for machine learning, exploration, and big data workloads. 3️⃣ Lambda Architecture Lambda Architecture combines batch and real-time processing into three layers: Batch Layer, Speed Layer, and Serving Layer. The batch layer processes large volumes of historical data for accuracy, while the speed layer handles real-time streams for low-latency results. Frameworks such as Apache Spark often power batch processing, while streaming engines like Apache Flink handle the speed layer. The serving layer merges both outputs to present a unified data view. This architecture balances completeness and timeliness but can be operationally complex. 4️⃣ Kappa Architecture Kappa Architecture simplifies Lambda by eliminating the separate batch layer. All data is treated as a continuous stream, processed through a single streaming engine. Event logs stored in systems like Apache Kafka act as the system of record. Reprocessing historical data simply means replaying streams. This model reduces architectural duplication and is ideal for organizations prioritizing real-time analytics with simpler operational overhead. 5️⃣ Data Mesh Data Mesh represents a paradigm shift from centralized data ownership to domain-oriented decentralization. Each business domain treats its data as a product, owning its pipelines, quality, and documentation. Instead of a monolithic data team, governance is federated while enabled by a self-service platform. Though not a specific technology, Data Mesh often leverages cloud-native tools and modern orchestration platforms like Kubernetes to empower autonomous teams. This approach scales organizationally as much as technically.

Recently I built a portfolio project using Azure showcasing pipelines built for real-world data engineering use cases including API ingestion, incremental loading, event-driven file routing, metadata-driven ingestion, and Delta-based transformation. Project Overview : In this project, I focused on designing production-style pipeline patterns instead of one-off data movement jobs. The repository demonstrates how ADF can be used to handle common enterprise data engineering needs across APIs, databases, and cloud storage. Rather than building one-off pipelines, this project focuses on production-style design patterns that improve maintainability, reduce manual effort, and support growth across multiple source systems. What the Project showcases: Some of the key highlights of the project: - Build parameterized ADF pipelines that can be reused across tables, files, and source systems - Design incremental data loads to reduce unnecessary full refreshes Implement REST API ingestion with dynamic pagination logic - Create event-driven file ingestion workflows using routing and control flow activities - Develop metadata-driven ingestion frameworks that reduce hardcoding - Use Mapping Data Flows to transform raw data into curated Delta-formatted outputs - Orchestrate multi-step workflows using parent-child pipeline execution - Design cloud ETL pipelines that are aligned with common enterprise data engineering practices Business Use Cases Covered: The pipelines in this repository are designed around practical business scenarios such as: - Automating ingestion from SaaS or REST API sources - Loading only new or changed records from source systems - Routing incoming files from landing zones into the correct processing layer - Standardizing ingestion for multiple source files with different schemas - Transforming raw data into structured datasets for dashboards and reporting Scheduling repeatable batch workflows for operational analytics Engineering Value: This project demonstrates the ability to design ADF solutions that are: - Reusable through parameterization and modular orchestration - Scalable through metadata-driven logic and dynamic processing - Operationally efficient through incremental loading - Maintainable through reduced hardcoding and pattern-based design - Analytics-ready through transformation into curated destination layers Summary: This project represents a practical implementation of Azure Data Factory for enterprise-style ETL orchestration. It showcases control-flow design, parameterized ingestion, metadata-driven processing, API extraction, and curated transformation patterns that are directly relevant to cloud data engineering and analytics platform development. #AzureDataFactory #DataEngineering #ETL #ELT #Azure #CloudDataEngineering #DataPipelines #AnalyticsEngineering #DeltaLake #LinkedInProjects #Opentowork 👉 Github Repo: https://lnkd.in/gsnTHzEY

🚀 System Design Essentials: A Thread on Backend Architecture Patterns As engineers, we constantly face decisions that shape our systems' scalability, reliability, and performance. Here's a deep dive into critical architectural patterns every backend engineer should master: 📊 EVENT SOURCING & CQRS Event Sourcing treats state changes as a sequence of events, creating an immutable audit trail. Instead of storing current state, you store every change that led to it. Perfect for financial systems, audit logs, and temporal queries. CQRS (Command Query Responsibility Segregation) splits read and write operations into separate models. This enables independent scaling, optimized data models for different use cases, and better performance under high load. 💾 DATA LAKE vs DATA WAREHOUSE Data Lake: Raw, unstructured storage for all data types. Think of it as a vast repository where data scientists explore and discover patterns. Schema-on-read, cost-effective, supports big data analytics. Data Warehouse: Structured, processed data optimized for business intelligence. Schema-on-write, OLAP queries, aggregated metrics. Your go-to for dashboards and reports. 🔍 SEARCH SYSTEMS (Elasticsearch) Elasticsearch powers modern search at scale using inverted indices. Key concepts: - Full-text search with relevance scoring - Distributed architecture with sharding - Near real-time indexing - Aggregations for analytics Used by GitHub, Netflix, and Uber for log analytics and search. 🎯 RECOMMENDATION SYSTEMS From Netflix to Amazon, recommendations drive engagement: - Collaborative Filtering: User-item interactions - Content-Based: Item attributes and user preferences - Hybrid Approaches: Best of both worlds - Real-time vs Batch processing trade-offs ⚡ RATE LIMITER ALGORITHMS Protecting your APIs from abuse: Token Bucket: Tokens refill at a fixed rate. Allows bursts but maintains average rate. Used by AWS, Stripe. Leaky Bucket: Requests processed at constant rate. Smooths out traffic spikes. Fixed Window: Simple counter reset at intervals. Can have edge case issues. Sliding Window: Combines fixed window benefits with smoother rate limiting. Most accurate but memory-intensive. ⏰ TIME-BASED SYSTEMS Cron Systems: Unix-style job scheduling with cron expressions. Distributed cron needs leader election and failure handling. Scheduler Design: Think Airflow, Kubernetes CronJobs - DAG-based dependencies - Retry mechanisms - Monitoring and alerting - State management 🎯 KEY TAKEAWAYS 1. Choose Event Sourcing when audit trails matter 2. Use CQRS when reads/writes have different scaling needs 3. Data Lakes for exploration, Warehouses for analytics 4. Elasticsearch for full-text search at scale 5. Rate limiters are essential for API stability 6. Schedulers need robust failure handling What's your favorite pattern? Drop a comment! 👇 #SystemDesign #SoftwareEngineering #BackendDevelopment #DistributedSystems #SoftwareArchitecture #Engineering #TechLeadership

Databricks pipeline architecture on AWS S 1 – Source Systems Components: APIs / DB Logs & Files Explanation: All raw transactional or event data comes from source systems. Data can be batch files, streaming events, or API feeds. This is the entry point of the pipeline. Key point: Data should be ingested as soon as it’s available to meet SLA. S 2 – S3 (Landing Zone/Raw Storage) Components: S3 bucket (raw data landing zone) Explanation: Acts as the raw layer storage, storing all incoming data. Used for audit trail and reprocessing if pipelines fail. Partitioning can be done by ingestion date. Key point: Use S3 event notifications or SQS for scalable ingestion. S 3 – Real-Time Stream Components: Streaming ingestion path Real-time processing for ML/alerts Explanation: Data can go directly into the Silver/Gold layers via micro-batch streaming. This path handles low-latency processing Uses checkpointing for fault tolerance. Key point: Supports real-time alerts and ML scoring before writing to Gold. S 4 – Bronze Layer (Raw Delta Lake) Components: Raw Delta tables Partition: ingestion_date ETL job cluster (50–100 nodes) Explanation: Stores all raw data without transformation. Key point: The bronze layer is the single source of truth for raw data. S 5 – Silver Layer (Clean / Deduplicated Delta Lake) Components: Clean Delta tables ETL job cluster Explanation: Cleans and validates data. Key point: The silver layer is where SCD Type 2 or deduplication logic is applied. S 6 – Gold Layer (Aggregated / KPI Layer) Components: Aggregated Delta tables Partition: region Explanation: The gold layer contains aggregated, business-ready data. Key point: The gold layer is ready for analytics and ML scoring. S 7 – BI & Analytics Components: Serverless SQL Warehouse Explanation: Gold layer feeds BI tools and dashboards. Key point: Separation of compute for analytics reduces load on ETL jobs. S 8 – Real-Time ML / Alerts Components: ML / Fraud Detection Explanation: Streams from the Bronze or Silver layer go to ML scoring pipelines. Key point: Real-time path complements batch ETL for near-instant insights. S 9 – AWS Infrastructure Components: EC2 spot instances Explanation: ETL job clusters run on memory-optimized EC2 nodes, often spot for cost savings. Key point: Automated, scalable, and cost-efficient compute and storage. S 10 – Security & Governance Components: Unity Catalog CloudWatch Databricks monitoring Explanation: Unity Catalog handles RBAC, row-level and column-level security, and PII masking. Key point: The governance layer is essential for enterprise-level data security. ✅ Summary of Data Flow Raw data from sources→S3 landing Bronze Delta: Raw ingestion+partition by ingestion_date Silver Delta: Clean+dedup+SCD/enrichment Gold Delta: Aggregated KPIs+ML features+Z-order optimization BI / Analytics: Serverless SQL Warehouse for dashboards Real-Time ML Path: Fraud detection+alerts via SNS/dashboards Monitoring & Governance: Unity Catalog+CloudWatch+TF

Designing a Scalable ETL Pipeline: AWS Glue + Lambda In production, we built a fully serverless ETL architecture using: - AWS Glue for distributed transformations - AWS Lambda for orchestration & event triggers - Amazon S3 as the data lake On paper: Simple. Raw Data → Transform → Curated Layer In production: Not so simple. ━━━━━━━━━━━━━━━━━━━ 𝗧𝗛𝗘 𝗣𝗥𝗢𝗕𝗟𝗘𝗠𝗦 𝗪𝗘 𝗙𝗔𝗖𝗘𝗗 As data volume grew from 10GB to 500GB daily: 🔸 Glue job runtime → 2 hours to 6 hours 🔸 Small file problem → Thousands of tiny S3 files 🔸 Schema evolution → Pipeline breaks on new columns 🔸 Cloud costs → 3x increase in 6 months 🔸 Dependency hell → Manual job triggering & monitoring ━━━━━━━━━━━━━━━━━━━ 𝗪𝗛𝗔𝗧 𝗔𝗖𝗧𝗨𝗔𝗟𝗟𝗬 𝗪𝗢𝗥𝗞𝗘𝗗 𝟭. 𝗜𝗻𝗰𝗿𝗲𝗺𝗲𝗻𝘁𝗮𝗹 𝗟𝗼𝗮𝗱𝘀 ✓ Implemented Glue job bookmarks ✓ Process only new/changed data ✓ Runtime: 6 hours → 45 minutes (87% faster) 𝟮. 𝗦𝗺𝗮𝗿𝘁 𝗣𝗮𝗿𝘁𝗶𝘁𝗶𝗼𝗻𝗶𝗻𝗴 ✓ Partition pruning & pushdown predicates ✓ Reduced data scanned by 70% ✓ Query performance improved 5x 𝟯. 𝗙𝗼𝗿𝗺𝗮𝘁 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 ✓ Migrated CSV → Parquet ✓ Storage costs reduced by 60% ✓ Scan efficiency improved 10x 𝟰. 𝗘𝘃𝗲𝗻𝘁-𝗗𝗿𝗶𝘃𝗲𝗻 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 ✓ Lambda triggers Glue jobs via S3 events ✓ Eliminated manual orchestration ✓ Near-real-time processing (< 5 min delay) 𝟱. 𝗢𝗯𝘀𝗲𝗿𝘃𝗮𝗯𝗶𝗹𝗶𝘁𝘆 ✓ Structured logging with CloudWatch ✓ Automated retry mechanisms ✓ Slack alerts for failures ✓ Mean time to resolution: 2 hours → 15 minutes ━━━━━━━━━━━━━━━━━━━ 𝗧𝗛𝗘 𝗥𝗘𝗔𝗟 𝗟𝗘𝗔𝗥𝗡𝗜𝗡𝗚 Serverless ≠ Zero Engineering It reduces infrastructure management. It does NOT reduce engineering responsibility. Real scalability comes from: → Smart partitioning strategies → Optimized data formats (Parquet > CSV) → Comprehensive observability → Cost awareness from day one → Handling schema evolution gracefully ━━━━━━━━━━━━━━━━━━━ 𝗕𝗢𝗧𝗧𝗢𝗠 𝗟𝗜𝗡𝗘 Anyone can build a pipeline. Building one that survives 10x data growth while staying cost-effective and maintainable? That's the real skill. ━━━━━━━━━━━━━━━━━━━ 💬 What's been your biggest challenge with serverless ETL pipelines? Drop your war stories below! 👇 #DataEngineering #AWS #AWSGlue #Lambda #CloudArchitecture #ETL #BigData #Serverless #DataPipelines #CloudComputing #SoftwareEngineering

The Difference Between "Working" and "Resilient" Architecture Production systems rarely stay on the happy path for long. In my experience, the shift from a basic pipeline to a resilient one lies in how you handle failure and scale. I’ve put together a look at a serverless, event-driven ETL pattern I recently designed. It focuses on moving away from brittle, monolithic scripts toward a decoupled architecture using Amazon EventBridge and SQS to ensure reliability and observability. Resilience isn't an afterthought; it’s an architectural choice. Read the full breakdown on Medium: https://lnkd.in/eZSMwFFm

What Is a Lakebase? Databricks propose a new architecture for OLTP databases called a lakebase. A lakebase is defined by: Openness: Lakebases are built on open source standards, e.g. Postgres. Separation of storage and compute: Lakebases store their data in modern data lakes (object stores) in open formats, which enables scaling compute and storage separately, leading to lower TCO and eliminating lock-in. Serverless: Lakebases are lightweight, and can scale elastically instantly, up and down, all the way to zero. At zero, the cost of the lakebase is just the cost of storing the data on cheap data lakes. Modern development workflow: Branching a database should be as easy as branching a code repository, and it should be near instantaneous. Built for AI agents: Lakebases are designed to support a large number of AI agents operating at machine speed, and their branching and checkpointing capabilities allow AI agents to experiment and rewind. Lakehouse integration: Lakebases should make it easy to combine operational, analytical, and AI systems without complex ETL pipelines.

What ETL taught me about building scalable products? Building scalable products requires a mindset shift from "making it work" to "making it work under pressure." Principles of ETL pipelines provide a blueprint for focusing on resilience, automation & handling massive data volumes. Lessons from ETL about building scalable products: 1. The Power of Repeatability If a job fails halfway through, running it again should not create duplicate data or corrupt the database. Product Lesson: Design API endpoints and backend processes to be idempotent. If a user clicks "submit" twice due to lag, or if a background job retries, the system must handle it gracefully without creating duplicate entries. 2. Embrace Asynchronous Processing ETL processes rarely run in real-time user-facing flows; they process data in batches or streams to avoid bottlenecks. Product Lesson: Heavy operations (email sending, image processing, complex reporting) should be offloaded to background queues. It keeps the user interface responsive while the product scales. 3. Implement Defensive Coding & Data Validation Dirty data can crash a pipeline. ETL requires rigorous validation at the ingestion stage to ensure data quality. Product Lesson: Never trust user input. Validate data at the API edge. 4. Separate Storage from Compute Modern ETL (or ELT) often uses data lakes to store data, while using separate, scalable compute engines (like Databricks) to process it. Product Lesson: Keep your database for transactional data, but use object storage (S3) for large blobs, files, or archives. This allows you to scale storage cheaply and compute power independently based on demand. 5. Built for Failure ETL pipelines are designed with monitoring & alerting in mind because network, database or API failures are inevitable. Product Lesson: Implement robust logging, alerting, and automated retry mechanisms for recovering automatically. 6. Partitioning & Parallelism To handle petabytes of data, ETL doesn't run one giant script; it breaks data into smaller, parallelizable chunks based on keys(e.g. date, region). Product Lesson: Database tables should be partitioned and services should be distributed. If a product feature works on data, ensure it can be processed in parallel across multiple nodes rather than sequentially. 7. Version Control for Everything Old ETL jobs must be able to run alongside new ones, making schema evolution & data versioning critical. Product Lesson: Use database migration tools and version your APIs. 8. Data Lineage & Observability When a dashboard breaks, you need to know exactly which step in the ETL pipeline failed. Product Lesson: Implement "observability" in your product. Know the lineage of your data, how it moves from user action to data warehouse. If a user sees wrong data, you must be able to trace it back to the exact API call. In essence, ETL teaches that scalability is a result of structuring data and processes to handle volume, speed & failure from day one.