Abhisek Sahu’s Post

𝗕𝗶𝗴 𝗗𝗮𝘁𝗮 𝗣𝗶𝗽𝗲𝗹𝗶𝗻𝗲 𝗖𝗵𝗲𝗮𝘁𝘀𝗵𝗲𝗲𝘁: 𝗔𝗪𝗦, 𝗔𝘇𝘂𝗿𝗲, 𝗮𝗻𝗱 𝗚𝗖𝗣: In today’s data-driven world, cloud-native big data pipelines are essential for extracting insights and maintaining a competitive edge. Here’s a concise breakdown of key components across AWS, Azure, and GCP: 𝟭. 𝗗𝗮𝘁𝗮 𝗜𝗻𝗴𝗲𝘀𝘁𝗶𝗼𝗻: AWS: Kinesis (real-time), AWS Data Pipeline (managed workflows) Azure: Event Hubs (real-time streaming), Data Factory (ETL) GCP: Pub/Sub (real-time), Dataflow (batch & stream processing) 𝟮. 𝗗𝗮𝘁𝗮 𝗟𝗮𝗸𝗲: AWS: S3 with Lake Formation for secure data lakes Azure: Azure Data Lake Storage (ADLS), integrates with HDInsight & Synapse GCP: Google Cloud Storage (GCS) with BigLake for unified data management 𝟯. 𝗖𝗼𝗺𝗽𝘂𝘁𝗲 & 𝗣𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴 AWS: EMR (managed Hadoop/Spark), Glue (serverless data integration) Azure: Databricks (Spark-based analytics), HDInsight (Hadoop) GCP: Dataproc (managed Spark/Hadoop), Dataflow (Apache Beam-based processing) 𝟰. 𝗗𝗮𝘁𝗮 𝗪𝗮𝗿𝗲𝗵𝗼𝘂𝘀𝗶𝗻𝗴 AWS: Redshift – scalable, high-performance data warehousing Azure: Synapse Analytics – combines SQL Data Warehouse & big data processing GCP: BigQuery – serverless, highly scalable, cost-effective analytics 𝟱. 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 & 𝗩𝗶𝘀𝘂𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻 AWS: QuickSight – scalable BI & reporting Azure: Power BI – deeply integrated with Microsoft ecosystem GCP: Looker – flexible data visualization & analytics Each cloud provider has unique strengths. Selecting the right combination of ingestion, storage, compute, and analytics tools is key to building scalable, cost-effective big data pipelines. Whether handling real-time streaming or deep data warehousing or batch processing, choosing wisely can optimize both efficiency and costs. Image Credits : ByteByteGo Alex Xu

𝗕𝗶𝗴 𝗗𝗮𝘁𝗮 𝗣𝗶𝗽𝗲𝗹𝗶𝗻𝗲 𝗖𝗵𝗲𝗮𝘁𝘀𝗵𝗲𝗲𝘁: 𝗔𝗪𝗦, 𝗔𝘇𝘂𝗿𝗲, 𝗮𝗻𝗱 𝗚𝗖𝗣: In today’s data-driven world, cloud-native big data pipelines are essential for extracting insights and maintaining a competitive edge. Here’s a concise breakdown of key components across AWS, Azure, and GCP: 𝟭. 𝗗𝗮𝘁𝗮 𝗜𝗻𝗴𝗲𝘀𝘁𝗶𝗼𝗻: AWS: Kinesis (real-time), AWS Data Pipeline (managed workflows) Azure: Event Hubs (real-time streaming), Data Factory (ETL) GCP: Pub/Sub (real-time), Dataflow (batch & stream processing) 𝟮. 𝗗𝗮𝘁𝗮 𝗟𝗮𝗸𝗲: AWS: S3 with Lake Formation for secure data lakes Azure: Azure Data Lake Storage (ADLS), integrates with HDInsight & Synapse GCP: Google Cloud Storage (GCS) with BigLake for unified data management 𝟯. 𝗖𝗼𝗺𝗽𝘂𝘁𝗲 & 𝗣𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴 AWS: EMR (managed Hadoop/Spark), Glue (serverless data integration) Azure: Databricks (Spark-based analytics), HDInsight (Hadoop) GCP: Dataproc (managed Spark/Hadoop), Dataflow (Apache Beam-based processing) 𝟰. 𝗗𝗮𝘁𝗮 𝗪𝗮𝗿𝗲𝗵𝗼𝘂𝘀𝗶𝗻𝗴 AWS: Redshift – scalable, high-performance data warehousing Azure: Synapse Analytics – combines SQL Data Warehouse & big data processing GCP: BigQuery – serverless, highly scalable, cost-effective analytics 𝟱. 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 & 𝗩𝗶𝘀𝘂𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻 AWS: QuickSight – scalable BI & reporting Azure: Power BI – deeply integrated with Microsoft ecosystem GCP: Looker – flexible data visualization & analytics Each cloud provider has unique strengths. Selecting the right combination of ingestion, storage, compute, and analytics tools is key to building scalable, cost-effective big data pipelines. Whether handling real-time streaming or deep data warehousing or batch processing, choosing wisely can optimize both efficiency and costs.

𝗕𝗶𝗴 𝗗𝗮𝘁𝗮 𝗣𝗶𝗽𝗲𝗹𝗶𝗻𝗲 𝗖𝗵𝗲𝗮𝘁𝘀𝗵𝗲𝗲𝘁: 𝗔𝗪𝗦, 𝗔𝘇𝘂𝗿𝗲, 𝗮𝗻𝗱 𝗚𝗖𝗣: In today’s data-driven world, cloud-native big data pipelines are essential for extracting insights and maintaining a competitive edge. Here’s a concise breakdown of key components across AWS, Azure, and GCP: 𝟭. 𝗗𝗮𝘁𝗮 𝗜𝗻𝗴𝗲𝘀𝘁𝗶𝗼𝗻: AWS: Kinesis (real-time), AWS Data Pipeline (managed workflows) Azure: Event Hubs (real-time streaming), Data Factory (ETL) GCP: Pub/Sub (real-time), Dataflow (batch & stream processing) 𝟮. 𝗗𝗮𝘁𝗮 𝗟𝗮𝗸𝗲: AWS: S3 with Lake Formation for secure data lakes Azure: Azure Data Lake Storage (ADLS), integrates with HDInsight & Synapse GCP: Google Cloud Storage (GCS) with BigLake for unified data management 𝟯. 𝗖𝗼𝗺𝗽𝘂𝘁𝗲 & 𝗣𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴 AWS: EMR (managed Hadoop/Spark), Glue (serverless data integration) Azure: Databricks (Spark-based analytics), HDInsight (Hadoop) GCP: Dataproc (managed Spark/Hadoop), Dataflow (Apache Beam-based processing) 𝟰. 𝗗𝗮𝘁𝗮 𝗪𝗮𝗿𝗲𝗵𝗼𝘂𝘀𝗶𝗻𝗴 AWS: Redshift – scalable, high-performance data warehousing Azure: Synapse Analytics – combines SQL Data Warehouse & big data processing GCP: BigQuery – serverless, highly scalable, cost-effective analytics 𝟱. 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 & 𝗩𝗶𝘀𝘂𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻 AWS: QuickSight – scalable BI & reporting Azure: Power BI – deeply integrated with Microsoft ecosystem GCP: Looker – flexible data visualization & analytics Each cloud provider has unique strengths. Selecting the right combination of ingestion, storage, compute, and analytics tools is key to building scalable, cost-effective big data pipelines. Whether handling real-time streaming or deep data warehousing or batch processing, choosing wisely can optimize both efficiency and costs. 🔈 For Regular Job & Data related updates, check out my Data Community to learn, share and grow together!! https://lnkd.in/g-ZtB4Yf Please Like, repost ✅, if you find them useful. #DataPipeline #data #ETL #dataengineering #datawarehouse

𝗕𝗶𝗴 𝗗𝗮𝘁𝗮 𝗣𝗶𝗽𝗲𝗹𝗶𝗻𝗲 𝗖𝗵𝗲𝗮𝘁𝘀𝗵𝗲𝗲𝘁: 𝗔𝗪𝗦, 𝗔𝘇𝘂𝗿𝗲, 𝗮𝗻𝗱 𝗚𝗖𝗣: In today’s data-driven world, cloud-native big data pipelines are essential for extracting insights and maintaining a competitive edge. Here’s a concise breakdown of key components across AWS, Azure, and GCP: 𝟭. 𝗗𝗮𝘁𝗮 𝗜𝗻𝗴𝗲𝘀𝘁𝗶𝗼𝗻: AWS: Kinesis (real-time), AWS Data Pipeline (managed workflows) Azure: Event Hubs (real-time streaming), Data Factory (ETL) GCP: Pub/Sub (real-time), Dataflow (batch & stream processing) 𝟮. 𝗗𝗮𝘁𝗮 𝗟𝗮𝗸𝗲: AWS: S3 with Lake Formation for secure data lakes Azure: Azure Data Lake Storage (ADLS), integrates with HDInsight & Synapse GCP: Google Cloud Storage (GCS) with BigLake for unified data management 𝟯. 𝗖𝗼𝗺𝗽𝘂𝘁𝗲 & 𝗣𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴 AWS: EMR (managed Hadoop/Spark), Glue (serverless data integration) Azure: Databricks (Spark-based analytics), HDInsight (Hadoop) GCP: Dataproc (managed Spark/Hadoop), Dataflow (Apache Beam-based processing) 𝟰. 𝗗𝗮𝘁𝗮 𝗪𝗮𝗿𝗲𝗵𝗼𝘂𝘀𝗶𝗻𝗴 AWS: Redshift – scalable, high-performance data warehousing Azure: Synapse Analytics – combines SQL Data Warehouse & big data processing GCP: BigQuery – serverless, highly scalable, cost-effective analytics 𝟱. 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 & 𝗩𝗶𝘀𝘂𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻 AWS: QuickSight – scalable BI & reporting Azure: Power BI – deeply integrated with Microsoft ecosystem GCP: Looker – flexible data visualization & analytics Each cloud provider has unique strengths. Selecting the right combination of ingestion, storage, compute, and analytics tools is key to building scalable, cost-effective big data pipelines. Whether handling real-time streaming or deep data warehousing or batch processing, choosing wisely can optimize both efficiency and costs. #DataPipeline #BigData #CloudComputing #AWS #Azure #GCP #DataEngineering #ETL #DataWarehouse #DataAnalytics #MachineLearning #CloudArchitecture #AnalyticsEngineering

🚀 𝐁𝐢𝐠 𝐃𝐚𝐭𝐚 𝐏𝐢𝐩𝐞𝐥𝐢𝐧𝐞𝐬 - 𝐀 𝐐𝐮𝐢𝐜𝐤 𝐂𝐨𝐦𝐩𝐚𝐫𝐢𝐬𝐨𝐧 (𝐀𝐖𝐒 | 𝐀𝐳𝐮𝐫𝐞 | 𝐆𝐂𝐏)⁣ ⁣ If you're working with large-scale data, choosing the right tools for your data pipeline is critical for performance, scalability, and cost-efficiency.⁣ ⁣ Here’s a streamlined cheat sheet comparing how the three major cloud platforms handle big data:⁣ ⁣ 🔹 𝟏. 𝐈𝐧𝐠𝐞𝐬𝐭𝐢𝐨𝐧 (𝐑𝐞𝐚𝐥-𝐓𝐢𝐦𝐞 & 𝐁𝐚𝐭𝐜𝐡)⁣ • AWS – Kinesis for real-time, Data Pipeline for ETL workflows⁣ • Azure – Event Hubs for streaming, Data Factory for orchestration⁣ • GCP – Pub/Sub for real-time messaging, Dataflow for batch/stream⁣ ⁣ 🔹 𝟐. 𝐃𝐚𝐭𝐚 𝐋𝐚𝐤𝐞 (𝐒𝐭𝐨𝐫𝐚𝐠𝐞 𝐚𝐭 𝐒𝐜𝐚𝐥𝐞)⁣ • AWS – S3 + Lake Formation for security & governance⁣ • Azure – Azure Data Lake Storage (ADLS) with Synapse/HDInsight support⁣ • GCP – GCS + BigLake for unified data lakehouse approach⁣ ⁣ 🔹 𝟑. 𝐏𝐫𝐨𝐜𝐞𝐬𝐬𝐢𝐧𝐠 & 𝐂𝐨𝐦𝐩𝐮𝐭𝐞⁣ • AWS – EMR (Hadoop/Spark), Glue (serverless ETL)⁣ • Azure – Databricks (Spark), HDInsight⁣ • GCP – Dataproc (Spark/Hadoop), Dataflow (Apache Beam)⁣ ⁣ 🔹 𝟒. 𝐃𝐚𝐭𝐚 𝐖𝐚𝐫𝐞𝐡𝐨𝐮𝐬𝐢𝐧𝐠⁣ • AWS – Redshift: high-speed, scalable warehouse⁣ • Azure – Synapse Analytics: integrates SQL & big data⁣ • GCP – BigQuery: serverless, pay-per-query analytics⁣ ⁣ 🔹 𝟓. 𝐁𝐈 & 𝐕𝐢𝐬𝐮𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧⁣ • AWS – QuickSight⁣ • Azure – Power BI⁣ • GCP – Looker⁣ ⁣ 🔍 Choosing the right stack depends on your use case - whether you're building real-time streaming applications or batch-based data platforms. Each cloud brings unique strengths, and understanding these differences can help you design better pipelines.⁣ ⁣ ✅ Found this helpful? Like it.⁣ 🔁 Repost to help others.⁣ 👋 Follow Akash AB for more Data Engineering insights!⁣ Image Credits : ByteByteGo #dataengineering #bigdata #cloudcomputing #ETL #dataarchitecture #AWS #Azure #GCP #DataPipeline

🔄 Reposting with PM Insights as an ICT Project Manager with 12+ years of experience now transitioning into data engineering, Akash AB's post on #dataengineering #bigdata #cloudcomputing resonates deeply with the project management challenges I've observed. From a PM perspective, here's what makes data engineering projects succeed in 2025: 🎯 Strategic Alignment: Data engineering isn't just about pipelines—it's about delivering business value. Every ETL job should map to a clear business outcome, just like our finance transformation projects at Barclays and Visa. ⚡ Real-Time Demands: 70% of organizations are shifting from batch to real-time processing. This means PMs need to plan for infrastructure that scales instantly, not just quarterly upgrades .🔄 Iterative Delivery: Unlike traditional waterfall projects, modern data platforms require continuous integration. Think sprints, not milestones—especially when working with Azure Data Factory and Databricks. 📊 Stakeholder Management: Data engineers speak in pipelines, business users speak in insights. As PMs, we're the translators ensuring technical excellence meets business requirements. 🛡️ Risk Mitigation: With GDPR and data privacy regulations, every data engineering decision has compliance implications. Risk assessment frameworks are non-negotiable. Key PM Skills for Data Engineering Projects: Understanding data quality metrics (not just project KPIs)Managing cross-functional teams (engineers + analysts + business users)Balancing technical debt vs. feature Delivery resource optimization in cloud environments. The intersection of project management and data engineering is where transformation happens. It's not enough to build great pipelines—they need to be delivered on time, within budget, and aligned with business strategy. What's your experience managing data-heavy projects? How do you balance technical innovation with project constraints? #ProjectManagement #DataEngineering #FinanceTransformation #CloudMigration #AzureDataFactory #Databricks #TechTransition #Australia

🔍 What does ETL really look like in modern cloud data engineering? In today’s data-driven world, ETL isn’t just about moving data — it’s about making it analytics-ready, scalable, and cost-efficient. Here’s a quick breakdown of the ETL flow using Azure tools: 🔸 Extract: Ingest raw data from sources like Blob Storage, APIs, or databases 🔸 Transform: Use Azure Data Factory’s Mapping Data Flow or Databricks to clean, enrich, and reshape data 🔸 Load: Push the final dataset into Synapse Analytics or Data Lake for reporting and ML 🧠 Optimization tip: Use coalesce() in PySpark or Data Flow sink settings to reduce file fragmentation and improve query performance. 📊 Visual below: A simplified view of how ETL works in Azure #AzureDataEngineering #ETL #DataFactory #Databricks #SynapseAnalytics #CloudData #OpenToWork #LearningTogether #DataEngineer

Here is the list of GCP (Google Cloud Platform) data components 👇 🔹 𝐁𝐢𝐠𝐐𝐮𝐞𝐫𝐲 – Serverless, fully managed data warehouse for analytics at scale. 🔹 𝐂𝐥𝐨𝐮𝐝 𝐒𝐭𝐨𝐫𝐚𝐠𝐞– Durable, scalable object storage for structured & unstructured data. 🔹 𝐂𝐥𝐨𝐮𝐝 𝐒𝐐𝐋– Managed relational database service for MySQL, PostgreSQL, and SQL Server. 🔹 𝐁𝐢𝐠𝐭𝐚𝐛𝐥𝐞– NoSQL wide-column database for large-scale, low-latency workloads. 🔹 𝐃𝐚𝐭𝐚𝐩𝐫𝐨𝐜 – Managed Spark & Hadoop for batch and streaming data processing. 🔹 𝐃𝐚𝐭𝐚𝐟𝐥𝐨𝐰– Serverless service for ETL and real-time data streaming pipelines. 🔹 𝐏𝐮𝐛/𝐒𝐮𝐛– Messaging service for event-driven systems and real-time ingestion. 🔹 𝐋𝐨𝐨𝐤𝐞𝐫 (𝐟𝐨𝐫𝐦𝐞𝐫𝐥𝐲 𝐃𝐚𝐭𝐚 𝐒𝐭𝐮𝐝𝐢𝐨)– Business intelligence and visualization platform. 🔹 𝐃𝐚𝐭𝐚𝐩𝐥𝐞𝐱– Unified data governance, catalog, and security across GCP data lakes/warehouses. 🔹 𝐅𝐢𝐫𝐞𝐬𝐭𝐨𝐫𝐞– Serverless NoSQL document database for app data and analytics. 🔹 𝐃𝐚𝐭𝐚 𝐅𝐮𝐬𝐢𝐨𝐧– Managed ETL/ELT tool for building and orchestrating pipelines. 🔹 𝐀𝐈 𝐏𝐥𝐚𝐭𝐟𝐨𝐫𝐦 / 𝐕𝐞𝐫𝐭𝐞𝐱 𝐀𝐈– End-to-end ML/AI platform integrated with data pipelines. #DSA #DE #LearnGCPwithDSA #AI

🚀 Google Cloud Dataproc – Simplifying Big Data Processing ☁️ Dataproc is a managed Spark and Hadoop service that lets you leverage open-source data tools for batch processing, querying, streaming, and machine learning — all without the headache of manual cluster management. With Dataproc, you can: ✅ Create clusters quickly and manage them easily. ✅ Save costs by turning off clusters when not needed. ✅ Focus on your jobs and data, not infrastructure! 🌟 Key Advantages of Dataproc 💰 Low Cost — Priced at only 1 cent per vCPU per hour, Dataproc offers second-by-second billing and supports preemptible instances to reduce costs even further. ⚡ Super Fast — Clusters start, scale, and shut down in under 90 seconds (vs. 5–30 mins on-premises). Spend more time analysing data, less time waiting! 🔗 Integrated — Seamless connectivity with BigQuery, Cloud Storage, Cloud Bigtable, Cloud Logging, and Cloud Monitoring. ETL terabytes of data directly into BigQuery for insights. 🛠️ Managed & Reliable — No admins or special software needed. Easily control clusters through the Google Cloud Console, SDK, or REST API. When you’re done, just shut it down—no wasted spend! 💡 Simple & Familiar — Continue using your favourite open-source tools like Spark, Hadoop, Pig, and Hive with frequent updates and zero redevelopment effort. Dataproc gives you a complete, integrated data platform to accelerate analytics, reduce costs, and simplify big data workflows. #GoogleCloud #Dataproc #BigData #DataEngineering #Spark #Hadoop #CloudComputing #MachineLearning #BigQuery #DataAnalytics #ETL #GCP #CloudStorage #DataScience

𝗗𝗮𝘁𝗮 𝗟𝗮𝗸𝗲 𝘃𝘀 𝗗𝗮𝘁𝗮 𝗪𝗮𝗿𝗲𝗵𝗼𝘂𝘀𝗲 𝘃𝘀 𝗟𝗮𝗸𝗲𝗵𝗼𝘂𝘀𝗲 – 𝗪𝗵𝗮𝘁’𝘀 𝘁𝗵𝗲 𝗗𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝗰𝗲? 🤔 One of the most common questions I hear is: “𝘚𝘩𝘰𝘶𝘭𝘥 𝘸𝘦 𝘶𝘴𝘦 𝘢 𝘋𝘢𝘵𝘢 𝘓𝘢𝘬𝘦 𝘰𝘳 𝘢 𝘋𝘢𝘵𝘢 𝘞𝘢𝘳𝘦𝘩𝘰𝘶𝘴𝘦?” The truth is – both have their strengths. And now, there’s a third player: 𝗟𝗮𝗸𝗲𝗵𝗼𝘂𝘀𝗲. Here’s a quick breakdown: 🔹 Data Warehouse  • Stores structured data (tables, rows, columns)  • Optimized for BI, reporting & dashboards  • Examples: Snowflake, Google BigQuery, Amazon Redshift 🔹 Data Lake  • Stores raw, unstructured or semi-structured data (JSON, images, logs, CSV)  • Cheap, scalable, great for data science & ML  • Examples: AWS S3, Azure Data Lake Storage, GCP Cloud Storage 🔹 Data Lakehouse  • Combines the best of both worlds: low-cost storage + ACID transactions + BI-friendly querying  • Removes the “data silos” between data lakes and warehouses  • Examples: Databricks Lakehouse, Delta Lake, Apache Iceberg 👉 Which one should you choose?  • If you need fast analytics on structured data → Warehouse  • If you need flexibility for all data types + ML → Lake  • If you need both, without duplication → Lakehouse My takeaway: 𝗟𝗮𝗸𝗲𝗵𝗼𝘂𝘀𝗲 𝗶𝘀 𝗯𝗲𝗰𝗼𝗺𝗶𝗻𝗴 𝘁𝗵𝗲 𝗳𝘂𝘁𝘂𝗿𝗲 𝗼𝗳 𝗺𝗼𝗱𝗲𝗿𝗻 𝗱𝗮𝘁𝗮 𝗽𝗹𝗮𝘁𝗳𝗼𝗿𝗺𝘀, but the right choice always depends on your use case. 💡 Curious — what’s your organization using today: a warehouse, a lake, or experimenting with lakehouse? #DataEngineering #DataWarehouse #DataLake #Lakehouse #BigData