GCP Data Components: Overview of GCP's Data Services

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. Image Credits : ByteByteGo Alex Xu 🔈 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. 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 𝗙𝗶𝗹𝗲 𝗙𝗼𝗿𝗺𝗮𝘁𝘀 𝗶𝗻 𝗔𝗪𝗦, 𝗗𝗮𝘁𝗮𝗯𝗿𝗶𝗰𝗸𝘀 & 𝗦𝗻𝗼𝘄𝗳𝗹𝗮𝗸𝗲 One of the most critical choices in 𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 is the right 𝗳𝗶𝗹𝗲 𝗳𝗼𝗿𝗺𝗮𝘁 — it impacts storage, performance, and cost. Here’s how the most popular formats play out in modern platforms   𝗥𝗼𝘄 𝘃𝘀 𝗖𝗼𝗹𝘂𝗺𝗻𝗮𝗿 𝗦𝘁𝗼𝗿𝗮𝗴𝗲  • 𝗥𝗼𝘄-𝗯𝗮𝘀𝗲𝗱 (CSV, JSON, Avro) → Good for ingestion, APIs, streaming.  • 𝗖𝗼𝗹𝘂𝗺𝗻𝗮𝗿 (Parquet, ORC) → Optimized for analytics, aggregations, and queries on subsets of columns. File Formats & Their Cloud Relevance ✅ 𝗖𝗦𝗩  • Widely used for ingestion in AWS S3, Databricks Autoloader, or Snowflake staging.  • Simple, but inefficient (no compression, no schema).  • Good for interoperability, not analytics. ✅ 𝗝𝗦𝗢𝗡  • Used for semi-structured data ingestion (API logs, clickstream, IoT).  • Snowflake’s VARIANT column + Databricks’ Auto Loader make JSON ingestion seamless.  • Higher storage cost due to repeatable keys. ✅ 𝗔𝘃𝗿𝗼 (𝗥𝗼𝘄-𝗯𝗮𝘀𝗲𝗱)  • Great for schema evolution in streaming pipelines.  • Common in Kafka + AWS Kinesis + Glue ETL pipelines.  • Often used as a source format before converting to Parquet/ORC in data lakes. ✅ 𝗣𝗮𝗿𝗾𝘂𝗲𝘁 (𝗖𝗼𝗹𝘂𝗺𝗻𝗮𝗿)  • Standard for AWS S3 Data Lake, Snowflake internal storage, and Databricks Delta Lake.  • High compression (up to 75%), predicate pushdown → reduces compute cost.  • Ideal for analytics and BI queries. ✅ 𝗢𝗥𝗖 (𝗖𝗼𝗹𝘂𝗺𝗻𝗮𝗿)  • Popular in Hadoop/Hive-based ecosystems, supported by AWS Athena & Glue.  • Strong compression and indexing → great for batch analytics.  • Less common than Parquet in Databricks/Snowflake, but still supported. Key Takeaway for Cloud Data Engineers  • 𝗜𝗻 𝗔𝗪𝗦: Land data as CSV/JSON → Process with Glue → Store in Parquet/ORC on S3.  • 𝗜𝗻 𝗗𝗮𝘁𝗮𝗯𝗿𝗶𝗰𝗸𝘀: Use Delta Lake (Parquet + Transaction log) for analytics & ML pipelines.  • 𝗜𝗻 𝗦𝗻𝗼𝘄𝗳𝗹𝗮𝗸𝗲: Ingest semi-structured JSON/Avro → Query seamlessly with VARIANT → Store analytics-ready data in compressed columnar format. #BigData #DataEngineering #AWS #Databricks #Snowflake #ETL #FileFormats #ApacheSpark

Modern Data Engineering Workflow – End to End Pipeline Data Engineering involves more than just moving data. It’s about designing scalable pipelines, enabling real-time insights, and powering business decisions. Data Sources – Ingest structured & unstructured data (APIs, CSV, Web, Relational, XML). Data Extraction – Load raw/unprocessed data into Data Lakes. Data Processing – Perform cleansing, validation, transformation, and aggregation (batch or real-time). Data Storage – Store processed data in Data Warehouses for fast analytics. Data Visualization – Enable insights through intuitive dashboards and advanced analytics. This is the foundation behind any modern cloud data platform whether on AWS, Azure, or GCP. hashtag #DataEngineering #ETL #DataPipelines #AWS #Azure #GCP #Databricks #Snowflake #BigData #DataWarehouse #DataLake #Analytics #CloudComputin #Python #SQL #Spark #DataTransformation #DataVisualization #DataOps #C2C #SeniorDataEngineer

🔍 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

I recently created a poster highlighting the latest innovations and shifts shaping Database Management Systems (DBMS) in 2025 Here are a few key takeaways: 💡 1️⃣ AI-Powered Query Optimizers Machine learning is now part of the query optimization process , making databases smarter, faster, and more cost-efficient in the cloud. 🌐 2️⃣ Data Mesh & Decentralized Governance We’re moving beyond centralized control. Modern enterprises are adopting domain oriented data ownership to improve scalability and accountability. 🧩 3️⃣ Hybrid & Multi-Model Databases DBMS platforms are blending relational, document, and graph paradigms for greater flexibility and richer insights. ⏳ 4️⃣ Time-Travel Databases Querying past states of data is now built-in , enabling rollback, auditing, and historical analytics with ease. ☁️ 5️⃣ Serverless & Edge Databases Database systems are going serverless, scaling automatically and operating closer to users for lightning-fast access and global performance. 📊 6️⃣ Real-Time Analytics & Streaming Integration Modern DBMS now play nicely with Kafka, Pulsar, and other streaming tools to deliver instant data insights for faster business decisions. 🛡️ 7️⃣ Privacy & Data Compliance Built-In From GDPR to CCPA, compliance is no longer optional its supported with encryption, masking, and audit trails. I extend my gratitude to Santhosh NC for the motivation to keep the ball rolling. #DBMS #DatabaseManagement #AI #DataEngineering #BigData #DataMesh #TechTrends2025 #PostgreSQL #MySQL #MongoDB #DataScience #Serverless

Modern Data Engineering Workflow – End to End Pipeline Data Engineering involves more than just moving data. It’s about designing scalable pipelines, enabling real-time insights, and powering business decisions. Data Sources – Ingest structured & unstructured data (APIs, CSV, Web, Relational, XML). Data Extraction – Load raw/unprocessed data into Data Lakes. Data Processing – Perform cleansing, validation, transformation, and aggregation (batch or real-time). Data Storage – Store processed data in Data Warehouses for fast analytics. Data Visualization – Enable insights through intuitive dashboards and advanced analytics. This is the foundation behind any modern cloud data platform whether on AWS, Azure, or GCP. #DataEngineering #ETL #DataPipelines #AWS #Azure #GCP #Databricks #Snowflake #BigData #DataWarehouse #DataLake #Analytics #CloudComputing #Python #SQL #Spark #DataTransformation #DataVisualization #DataOps #C2C #SeniorDataEngineer

#Create a #scalable, #automated #data #pipeline for #healthcare analytics. Data Sources: CSV files hosted on Google Drive. Architecture Components: o   Ingestion (AWS Glue ETL job):  Ingestion using an AWS Glue ETL job refers to the process of extracting, transforming, and loading (ETL) data into a target destination, typically within a data lake or data warehouse environment, utilizing AWS Glue's serverless capabilities. Supports various data formats and sources, and allows for custom transformation logic using PySpark or Scala. o   Storage (Amazon S3): The data lake foundation. - s3://gdrivetos3/data_raw/ : Initial landing zone. - s3:// gdrivetos3 /data_processed/: Stores processed, columnar data (csv). - s3:// gdrivetos3 /data_clean/: Stores cleaned, columnar data (Parquet). o  Aws Crawler: An AWS Glue Crawler is an AWS Glue component that automatically discovers data in various data repositories, infers its schema, and populates the AWS Glue Data Catalog, creating a centralized, queryable metadata repository.  o   Cataloging (AWS Glue Data Catalog): Holds the schema and metadata of the data in S3, making it queryable by Athena and ETL jobs. o   Processing (AWS Glue ETL): Serverless Spark environment to perform data quality checks, joins, and transformations. This is where you handle missing values, map data types, and calculate derived columns. o   Data Warehouse (Redshift): A fully managed, petabyte-scale cloud data warehouse by Amazon Web Services that powers data analytics at scale. It offers features like Redshift Serverless for automatic scaling and near real-time analytics via Zero-ETL integrations.  Security: IAM roles for least privilege access, S3 bucket policies, and encryption of data at rest and in transit. Amazon Web Services (AWS)