Cloud-based Big Data Analytics

I’m thrilled to share my latest publication in the International Journal of Computer Engineering and Technology (IJCET): Building a Real-Time Analytics Pipeline with OpenSearch, EMR Spark, and AWS Managed Grafana. This paper dives into designing scalable, real-time analytics architectures leveraging AWS-managed services for high-throughput ingestion, low-latency processing, and interactive visualization. Key Takeaways: ✅ Streaming Data Processing with Apache Spark on EMR ✅ Optimized Indexing & Query Performance using OpenSearch ✅ Scalable & Interactive Dashboards powered by AWS Managed Grafana ✅ Cost Optimization & Operational Efficiency strategies ✅ Best Practices for Fault Tolerance & Performance As organizations increasingly adopt real-time analytics, this framework provides a cost-effective and reliable approach to modernizing data infrastructure. 💡 Curious to hear how your team is tackling real-time analytics challenges—let’s discuss! 📖 Read the full article: https://lnkd.in/g8PqY9fQ #DataEngineering #RealTimeAnalytics #CloudComputing #OpenSearch #AWS #BigData #Spark #Grafana #StreamingAnalytics

Modernizing Analytics: My Journey from SAS/Teradata to Python/GCP ☁️ In my experience, one of the most rewarding challenges for a Data Engineer is modernizing a legacy analytics ecosystem. The migration from SAS on a traditional data warehouse like Teradata to a flexible, cloud-native stack on GCP is a prime example of this strategic shift. It's a move from a proprietary, monolithic environment to a scalable, open-source-driven one. The Technical Transformation: From SAS to Python: The first critical step is re-platforming the analytics logic. This involves translating complex Base SAS and PROC SQL code into modern, maintainable Python scripts. We use libraries like Pandas for smaller datasets and, more importantly, PySpark or Apache Beam (for Google Cloud Dataflow) for distributed processing of massive data volumes. This refactoring is an opportunity to optimize logic and improve code quality. 🐍 From Teradata to GCP: This is a fundamental infrastructure shift. We migrate data from the on-premise Teradata database to a cloud data warehouse like Google BigQuery for high-performance analytics, or to a data lake in Google Cloud Storage (GCS) for raw data storage. Google Cloud Dataproc (for Spark) or Dataflow are then used to build the new data pipelines, replacing the old ETL processes. 🏛️ Orchestration & Governance: For managing the new pipelines, we replace legacy schedulers with a modern orchestration tool like Google Cloud Composer (managed Airflow). This provides robust workflow management, monitoring, and logging. Data governance, which was often siloed, is now managed centrally with tools like BigQuery's built-in controls and Cloud Data Catalog. 🔐 The Key Benefits: The outcome of this migration is transformative: Scalability & Cost: We move from a fixed-capacity, expensive Teradata box to the elastic, pay-as-you-go model of GCP, which scales compute and storage independently. Flexibility: We gain access to Python’s vast open-source ecosystem, empowering data scientists and analysts with a modern toolset. Efficiency: Modern, version-controlled code is easier to maintain, debug, and collaborate on. This migration is more than a technical upgrade; it's a strategic shift that unlocks new possibilities for data and analytics. Have you been part of a similar migration? I'd be interested to hear about your experience!

𝐃𝐢𝐝 𝐲𝐨𝐮 𝐤𝐧𝐨𝐰 𝐭𝐡𝐚𝐭 𝐠𝐥𝐨𝐛𝐚𝐥 𝐦𝐨𝐛𝐢𝐥𝐞 𝐝𝐚𝐭𝐚 𝐭𝐫𝐚𝐟𝐟𝐢𝐜 𝐢𝐬 𝐞𝐱𝐩𝐞𝐜𝐭𝐞𝐝 𝐭𝐨 𝐫𝐞𝐚𝐜𝐡 𝐚 𝐬𝐭𝐚𝐠𝐠𝐞𝐫𝐢𝐧𝐠 77.5 𝐞𝐱𝐚𝐛𝐲𝐭𝐞𝐬 𝐩𝐞𝐫 𝐦𝐨𝐧𝐭𝐡 𝐛𝐲 2027? This explosion of data presents both a challenge and a massive opportunity for telecommunication companies. But are they equipped to handle it? The telecommunications industry is undergoing a seismic shift. Why should you care? Because this transformation impacts how we connect, communicate, and experience the digital world. A recent study showed that poor network performance can lead to a 30% increase in customer churn. 👉 In today's hyper-connected world, customer expectations are higher than ever, and telcos need to leverage data to stay ahead of the curve. 👉 Traditional data management systems struggle to keep pace with the sheer volume, velocity, and variety of data generated by modern telecom networks. Sifting through massive datasets to gain actionable insights is like finding a needle in a haystack. 👉 This makes it difficult to optimize network performance, personalize customer experiences, and develop innovative new services. Telcos need a new approach to data management to unlock the true potential of their data. 𝐓𝐡𝐞 𝐬𝐨𝐥𝐮𝐭𝐢𝐨𝐧? 👉 Deutsche Telekom, one of the world's leading telecommunications providers, is leading the charge by designing the telco of tomorrow with BigQuery. 👉 By leveraging BigQuery's powerful data warehousing and analytics capabilities, Deutsche Telekom is able to ingest and analyze massive datasets in real time. This enables them to gain valuable insights into network performance, customer behavior, and market trends. 👉 They can now proactively identify and resolve network issues, personalize offers and services for individual customers, and develop new revenue streams. 𝐊𝐞𝐲 𝐓𝐚𝐤𝐞𝐚𝐰𝐚𝐲𝐬: 👉 Real-time Insights: BigQuery enables real-time analysis of massive datasets, allowing telcos to react quickly to changing network conditions & customer needs. 👉 Improved Customer Experience: By understanding customer behavior and preferences, telcos can personalize services and offers, leading to increased customer satisfaction and loyalty. 👉 Innovation & Growth: Access to rich data insights empowers telcos to develop innovative new services & explore new business models. 👉 Scalability & Flexibility: Cloud-based solutions like BigQuery offer the scalability and flexibility needed to handle the ever-growing data demands of the telecommunications industry. This journey highlights the transformative power of data in the telecommunications industry. By embracing cloud-based data solutions, telcos can unlock valuable insights, improve customer experiences & drive innovation. The future of telecom is data-driven, and companies that embrace this reality will be the leaders of tomorrow. Follow Omkar Sawant for more. #telecommunications #bigdata #cloud #digitaltransformation #datanalytics

𝗕𝗶𝗴 𝗗𝗮𝘁𝗮 𝗣𝗶𝗽𝗲𝗹𝗶𝗻𝗲 𝗖𝗵𝗲𝗮𝘁𝘀𝗵𝗲𝗲𝘁 𝗳𝗼𝗿 𝗔𝗪𝗦, 𝗔𝘇𝘂𝗿𝗲 & 𝗚𝗼𝗼𝗴𝗹𝗲 𝗖𝗹𝗼𝘂𝗱 Everyone talks about data pipelines, but few can actually design one end to end across clouds. This post fixes that a concise cheatsheet for AWS, Azure, and GCP. It’s not theory. It’s the real building blocks of modern data infrastructure. 1/ 𝗔𝗪𝗦 → 𝗧𝗵𝗲 𝗢𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗼𝗿 ↳ Ingestion: 𝗞𝗶𝗻𝗲𝘀𝗶𝘀 (real-time), 𝗦𝟯 (batch) ↳ Processing: 𝗚𝗹𝘂𝗲 (ETL), 𝗘𝗠𝗥 (Spark/Hadoop), 𝗟𝗮𝗺𝗯𝗱𝗮 (serverless transforms) ↳ Storage: 𝗦𝟯 (raw/lake), 𝗥𝗲𝗱𝘀𝗵𝗶𝗳𝘁 (warehouse), 𝗔𝘁𝗵𝗲𝗻𝗮 (SQL on S3) ↳ Orchestration: 𝗦𝘁𝗲𝗽 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀 / 𝗔𝗶𝗿𝗳𝗹𝗼𝘄 ↳ Monitoring: 𝗖𝗹𝗼𝘂𝗱𝗪𝗮𝘁𝗰𝗵 / 𝗗𝗮𝘁𝗮𝗗𝗼𝗴 Example → Retail clickstream analytics, where events stream via Kinesis → processed by Glue/Spark → stored in Redshift for dashboards. 2/ 𝗔𝘇𝘂𝗿𝗲 → 𝗧𝗵𝗲 𝗘𝗻𝗱-𝘁𝗼-𝗘𝗻𝗱 𝗣𝗹𝗮𝘆𝗲𝗿 ↳ Ingestion: 𝗘𝘃𝗲𝗻𝘁 𝗛𝘂𝗯𝘀, 𝗗𝗮𝘁𝗮 𝗙𝗮𝗰𝘁𝗼𝗿𝘆 (ADF) ↳ Processing: 𝗗𝗮𝘁𝗮𝗯𝗿𝗶𝗰𝗸𝘀 (Spark), 𝗦𝘆𝗻𝗮𝗽𝘀𝗲 (SQL + Spark), 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀 (serverless ETL) ↳ Storage: 𝗕𝗹𝗼𝗯 𝗦𝘁𝗼𝗿𝗮𝗴𝗲, 𝗗𝗮𝘁𝗮 𝗟𝗮𝗸𝗲 𝗚𝗲𝗻𝟮, 𝗖𝗼𝘀𝗺𝗼𝘀 𝗗𝗕 ↳ Orchestration: 𝗗𝗮𝘁𝗮 𝗙𝗮𝗰𝘁𝗼𝗿𝘆 / 𝗟𝗼𝗴𝗶𝗰 𝗔𝗽𝗽𝘀 ↳ Monitoring: 𝗔𝗽𝗽 𝗜𝗻𝘀𝗶𝗴𝗵𝘁𝘀, 𝗠𝗼𝗻𝗶𝘁𝗼𝗿 Example → IoT analytics pipeline where data flows via Event Hubs → processed in Databricks → queried through Synapse dashboards. 3/ 𝗚𝗼𝗼𝗴𝗹𝗲 𝗖𝗹𝗼𝘂𝗱 → 𝗧𝗵𝗲 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗣𝗼𝘄𝗲𝗿𝗵𝗼𝘂𝘀𝗲 ↳ Ingestion: 𝗣𝘂𝗯/𝗦𝘂𝗯, 𝗗𝗮𝘁𝗮𝗳𝗹𝗼𝘄 ↳ Processing: 𝗗𝗮𝘁𝗮𝗳𝗹𝗼𝘄 (batch/stream), 𝗗𝗮𝘁𝗮𝗽𝗿𝗼𝗰 (Spark/Hadoop), 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀 / 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀 ↳ Storage: 𝗚𝗖𝗦 (lake), 𝗕𝗶𝗴𝗤𝘂𝗲𝗿𝘆 (warehouse), 𝗙𝗶𝗿𝗲𝘀𝘁𝗼𝗿𝗲 (NoSQL) ↳ Orchestration: 𝗖𝗹𝗼𝘂𝗱 𝗖𝗼𝗺𝗽𝗼𝘀𝗲𝗿 (Airflow), 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀 ↳ Monitoring: 𝗢𝗽𝘀 𝗦𝘂𝗶𝘁𝗲 / 𝗗𝗮𝘁𝗮𝗱𝗼𝗴 Example → Marketing pipeline pulling ad data from APIs into Pub/Sub → Dataflow transforms → BigQuery analysis with Looker. 4/ 𝗖𝗼𝗺𝗺𝗼𝗻 𝗣𝗮𝘁𝘁𝗲𝗿𝗻 𝗔𝗰𝗿𝗼𝘀𝘀 𝗖𝗹𝗼𝘂𝗱𝘀 Here’s how they all align conceptually: ┌────────────┐ Ingestion │ → (Kinesis / Event Hubs / PubSub) └─────┬──────┘ ↓ ┌────────────┐ Processing │ → (Glue / Databricks / Dataflow) └─────┬──────┘ ↓ ┌────────────┐ Storage │ → (S3 / ADLS / GCS) └─────┬──────┘ ↓ ┌────────────┐ Analytics │ → (Redshift / Synapse / BigQuery) └────────────┘ 𝗧𝗟;𝗗𝗥 AWS → modular + scalable Azure → integrated + enterprise-ready GCP → analytics-first + developer-friendly Big Data pipelines aren’t about tools. They’re about designing reliable data flow across clouds, without friction. 📸/ ByteByteGo

Every major cloud claims to be a complete data platform. What that really means is this👇 Each one covers the same core data lifecycle, just with different building blocks. The lifecycle never changes: → 𝐈𝐧𝐠𝐞𝐬𝐭𝐢𝐨𝐧– collect data from applications, events, and streams → 𝐃𝐚𝐭𝐚 𝐋𝐚𝐤𝐞 – store raw, unprocessed data → 𝐂𝐨𝐦𝐩𝐮𝐭𝐚𝐭𝐢𝐨𝐧 – clean, transform, and analyze → 𝐃𝐚𝐭𝐚 𝐖𝐚𝐫𝐞𝐡𝐨𝐮𝐬𝐞  – structure data for analytics → 𝐏𝐫𝐞𝐬𝐞𝐧𝐭𝐚𝐭𝐢𝐨𝐧 – turn data into insights people can use Here’s how the big three map to that flow: Amazon Web Services (AWS) → Ingestion: Kinesis → Data Lake: S3 → Processing: EMR → Warehouse: Redshift → Presentation: QuickSight Microsoft Azure → Ingestion: Event Hubs → Data Lake: Azure Data Lake Storage → Processing: Databricks → Warehouse: Cosmos DB → Presentation: Power BI GCP → Ingestion: Pub/Sub → Data Lake: Cloud Storage → Processing: Dataproc & Dataflow → Warehouse: BigQuery → Presentation: Data Studio Different tools. Same architecture pattern. This is why strong data engineers don’t start with services. They start with flows. Once you understand the lifecycle, switching clouds becomes an implementation detail, not a career reset. The real question isn’t which cloud is better? It’s where does your data break when it moves through this flow? That’s where engineering judgment actually matters. 💬 Which stage of this lifecycle do you see teams struggling with the most? Follow Disha Mukherjee for practical data engineering & AI system insights Subscribe to my newsletter for real-world data architecture breakdowns - https://lnkd.in/eFPw_cd5 ♻️ Repost if this helps you design better data pipelines. #AWS #Azure #GoogleCloud #CloudData #DataPipelines #ModernDataStack #DataEngineering #BigData #DataArchitecture #AnalyticsEngineering

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

Cloud-based data platform architecture overview Here is the step-by-step explanation: Step 1: Data Sources Data comes from various origins: Databases (e.g., MySQL, PostgreSQL) APIs (e.g., REST APIs, web services) Files (e.g., CSV, JSON, Excel) These are the raw inputs fed into the next stage. Step 2: ETL & Data Integration Using Informatica®, an ETL (Extract, Transform, Load) tool, to: Extract data from sources Transform it (clean, structure, enrich) Load it into a staging area or data lake Step 3: Data Processing & Machine Learning Using Databricks® (a unified analytics platform): Process large-scale data Run machine learning models Prepare data for analytics Step 4: ML Models & Orchestration Using Dataiku (a data science platform): Build and manage ML models Orchestrate workflows between processing and storage Step 5: Load into Data Warehouse Using Snowflake® (a cloud data warehouse): Store processed, structured data Enable fast querying and analytics Step 6: BI & Reporting End-users create: Dashboards (interactive visualizations) Reports (static or scheduled outputs) Tools like Tableau, Power BI, or Looker could be used here (not explicitly named in the image). Overall Flow: Data Sources → Informatica → Databricks → Dataiku → Snowflake → BI & Reporting This is a modern cloud-based data pipeline integrating ETL, big data processing, machine learning, and cloud warehousing for analytics. Databricks ETL Snowflake

A look into a modern multi-cloud data architecture built for scale, reliability, and faster insights 🚀 From sourcing data in Azure Blob Storage with Delta Lake, seamlessly transferring it to Google Cloud Storage, and building external tables in BigQuery, to transforming data across Bronze, Silver, and Gold layers using dbt and Dataform—this setup ensures reliability, performance, and analytics-ready data. Orchestrated with Apache Airflow, the architecture enables efficient ingestion, transformation, and governance across multi-cloud environments, empowering teams to deliver trusted insights faster. #DataEngineering #BigQuery #GoogleCloud #Azure #DeltaLake #dbt #Dataform #ApacheAirflow #MedallionArchitecture #AnalyticsEngineering #CloudData #ModernDataStack

🚀 Modern Data Platforms: Choosing the Right Stack for Your Data Strategy The data ecosystem is evolving rapidly, and organizations today often evaluate three major platforms when building modern analytics architectures: Microsoft Fabric, Snowflake, and Databricks. Each platform brings a different philosophy to data engineering, analytics, and AI. 🔹 Microsoft Fabric focuses on a unified analytics ecosystem, deeply integrated with the Microsoft stack. With OneLake and Delta Lake, it simplifies data access across services like Power BI, Synapse, and Data Factory, making it ideal for organizations heavily invested in the Microsoft ecosystem. ❄️ Snowflake is a powerful cloud-native data warehouse designed for scalable data processing. Its architecture separates compute and storage, enabling organizations to run high-performance analytics workloads across AWS, Azure, and GCP with flexible pay-per-use pricing. 🧱 Databricks pioneered the Lakehouse architecture, combining the reliability of data warehouses with the flexibility of data lakes. Built around Delta Lake, it excels in AI, machine learning, and advanced data engineering workloads. 💡 Quick perspective: • If your ecosystem is Microsoft-centric → Fabric can accelerate unified analytics. • If your priority is scalable SQL analytics → Snowflake is a strong choice. • If AI/ML and data science are core → Databricks shines with its Lakehouse model. The reality? Many modern enterprises are adopting multi-platform strategies, choosing the right tool depending on analytics, BI, or AI requirements. 📊 The real advantage today isn’t just moving data - it’s building intelligent data platforms that support analytics, AI, and real-time insights. Which platform are you currently working with the most? #DataEngineering #ModernDataStack #CloudData #AI #Analytics #Lakehouse