Cloud-Based Data Services

Your data warehouse is a fancy restaurant—expensive, perfectly plated, but tiny portions. Your data lake, A farmers market—cheap, abundant, but chaotic and half the produce is rotten. Enter the Lakehouse: It's a food hall. Best of both worlds. For years, data teams were stuck choosing between warehouse reliability ($$$ per TB) or lake affordability (good luck finding clean data). The lakehouse revolution ended that tradeoff. 🏗️ What really Changed? Open table formats—Delta Lake, Apache Iceberg, Apache Hudi — all of these brought warehouse features to cheap cloud storage (S3, GCS, ADLS). Now you get: → ACID transactions on $20/TB storage (not $300/TB) → Time travel & rollbacks (undo bad writes instantly) → Schema evolution (add columns without breaking pipelines) → Unified batch + streaming reads Think: Database reliability. Cloud storage prices. Does this really make an Impact? Yes it does! → Netflix migrated petabytes from separate warehouse/lake systems to lakehouse—cut costs 40%, unified analytics. → Uber uses Delta Lake for 100+ petabytes—powers real-time pricing, fraud detection, all on one architecture. Curious to know When to Use What ❓ Lakehouse (Delta/Iceberg): → 90% of modern use cases → Large-scale analytics → Mixed batch + streaming workloads → Cost-conscious teams Pure Warehouse (Snowflake/BigQuery): → Small data volumes (<10TB) → Business analysts who live in SQL → Zero engineering tolerance Pure Lake (Raw Parquet): → Archival storage only → Need messy data Here are the Cloud Platforms solutions for Data Lakehouse: Amazon Web Services (AWS):  • S3 stores data; Glue, EMR process Delta Lake/Iceberg.  • Athena queries; Lake Formation governs access and auditing. Microsoft Azure:  • ADLS Gen2 stores data; Databricks runs Delta Lake.  • Synapse queries; Purview manages governance and compliance. Google Cloud:  • GCS stores data; Dataproc processes with Iceberg/Delta.  • BigQuery and BigLake query; Dataplex manages governance. Ready to level up? Which format are you exploring—Delta Lake or Iceberg? Drop your pick below! 👇

Today we @Earthmover are releasing something that I think will change the landscape of how geospatial data are produced and consumed. Currently, data providers face some difficult tradeoffs when deciding how to disseminate large geospatial datasets.... Option 1️⃣ - 𝗖𝗹𝗼𝘂𝗱 𝗡𝗮𝘁𝗶𝘃𝗲  - Embrace multi-file cloud-native formats like Geoparquet / Apache Iceberg (for vector data) or Zarr / Icechunk (for raster data); and provide direct access to analysis-ready datasets in cloud object storage. This provides excellent performance and convenience for the user, but is generally an “all or nothing” approach. Either you have access to the full dataset or not. Option 2️⃣ - 𝗔𝗣𝗜 𝗚𝗮𝘁𝗲𝘄𝗮𝘆 - The provider hides their data behind an API gateway, which provides fine-grained access controls and detailed metrics. This approach is more versatile for the provider, supporting complex pricing and permissioning schemes, but operating an API gateway at scale can become a major engineering challenge for the provider. And it’s generally less friendly to the user, who has to learn to talk to a new API. It can also be a severe performance bottleneck; most APIs can’t deliver data anywhere close to the throughput of S3 itself, meaning that the customer has to re-ingest the data to their own database before it can be used for serious analytics and AI. Option 3️⃣ - 𝗕𝗲𝘀𝗽𝗼𝗸𝗲 𝗗𝗲𝗹𝗶𝘃𝗲𝗿𝘆 - Deliver a custom dataset for each customer, pushing data to the customer’s storage in their format of choice. This is the ultimate convenience for the user. And a major pain for the provider. We once met a B2B weather sales team who was delivering the same weather forecast data in 10 different ways for 10 different customers. Unsurprisingly, that business unit was failing due to poor unit economics. ✨ Earthmover's new 𝗙𝗶𝗹𝘁𝗲𝗿𝗲𝗱 𝗦𝘂𝗯𝘀𝗰𝗿𝗶𝗽𝘁𝗶𝗼𝗻𝘀, available with the Data Marketplace, eliminates these tradeoffs. It allows data providers to create secure, read-only views into multidimensional Icechunk data cubes, enabling more granular cloud-native data exchange between provider and consumer. Long form blog post (in comments) explains how we built it...

Been drowning in questions about Salesforce Data Cloud lately from my financial services clients. "What is it?" "Do we need it?" "Is it just another Salesforce upsell?" Finally had time to dive deep, and here's my unfiltered take: In simple terms: Data Cloud is like a universal translator for all your systems. Instead of forcing everything into your CRM (we all know how that goes 😬), it creates connections while letting data stay where it belongs. For financial firms with multiple business units - where client data lives across portfolio systems, CRM, and marketing platforms - this solves that maddening fragmentation problem. What jumped out at me from my research: This isn't just another database. It's specifically designed for "organizations with multiple orgs and/or business units" - which describes practically every financial services enterprise I work with. Implementation reality check: "It's 80% analysis and design and 20% implementation" - so don't rush the planning phase. Map out your data sources and quality issues before building anything. For firms exploring AI initiatives, this is addressing the foundation issue - can't get good AI outcomes with fragmented data. Anyone else exploring Data Cloud for financial services? How are you currently tackling the "unified client view" challenge? #SalesforceDataCloud #FinancialServices #DataIntegration

#Cloud-#Platform #Independent #Data #Architecture Building Cloud-Platform Independent Data Architecture for Big Data Analytics In today's rapidly evolving cloud landscape, organizations are often faced with vendor lock-in challenges, making it difficult to scale, optimize costs, or switch platforms without major disruptions. As someone who has worked extensively in data engineering and cloud migrations, I firmly believe that cloud-platform independent data architectures are the future of big data analytics. Here’s why: ✅ Portability & Flexibility – Designing an architecture that is not tightly coupled with a single cloud provider ensures seamless migration and multi-cloud capabilities. ✅ Cost Optimization – Avoiding dependency on proprietary services allows businesses to leverage the best pricing models across clouds. ✅ Scalability & Resilience – A well-architected platform-independent data strategy ensures high availability, performance, and disaster recovery across environments. ✅ Technology Agnosticism – Open-source and cloud-agnostic tools (such as Apache Spark, Presto, Trino, Airflow, and Kubernetes) enable organizations to build robust data pipelines without being restricted by vendor limitations. As organizations migrate massive data workloads (often in petabytes), ensuring interoperability, standardization, and modular architecture becomes critical. I've seen firsthand the challenges of moving data pipelines, storage solutions, and analytics workflows between clouds. A strategic, well-thought-out data architecture can make all the difference in ensuring a smooth transition and long-term sustainability. How are you tackling cloud vendor lock-in in your data architecture? Would love to hear your thoughts! #CloudComputing #DataArchitecture #BigData #DataEngineering #CloudMigration #MultiCloud #Analytics #GCP #AWS #Azure

If you're building data pipelines, processing large datasets, or architecting analytics solutions in the cloud, AWS offers one of the most complete data engineering ecosystems in the world. This visual lays out every major component you need to know - from ingestion to storage to analytics and security - all mapped to the exact AWS service that powers it. Here’s the full breakdown: 1. Data Ingestion & Orchestration Manages real-time and batch data movement using AWS Glue, Kinesis, Step Functions, MWAA (Managed Airflow), and AWS DMS to keep pipelines automated and reliable. 2. Data Processing & Analytics Enables scalable cleaning, transforming, and querying of data through Amazon EMR, Athena, AWS Lake Formation, and AWS Glue Jobs. 3. Compute & Containers Runs workloads of any size with flexible compute options like AWS Lambda, EC2, AWS Batch, ECS, and EKS. 4. Databases (Purpose-Built) Supports every data model using Amazon Aurora, Neptune, Timestream, and DocumentDB, each optimized for specific workloads. 5. Data Storage & Management Stores raw and processed data securely and at scale with Amazon S3, Redshift, RDS, and DynamoDB powering the core data foundation. 6. Data Transfer (Hybrid & Cloud) Moves data quickly across environments using AWS Snow Family for petabyte-scale transfers and AWS DataSync for fast cloud migrations. 7. Analytics & Machine Learning Delivers insights and ML capabilities through Amazon SageMaker, QuickSight, and OpenSearch for dashboards, models, and search analytics. 8. Governance, Security & Operations Keeps data systems compliant and observable using AWS IAM, CloudWatch, CloudTrail, DataZone, KMS, and Security Hub. AWS brings every piece of the data engineering lifecycle into one connected ecosystem - making it easier than ever to build pipelines, manage data, and scale analytics.

🚀 Modern Data Platform on AWS – From Ingestion to Analytics This architecture showcases how a scalable and secure data platform can be built on AWS by combining cloud-native services with strong automation and governance. 🔹 Ingestion: Data flows from Salesforce and external databases using Amazon AppFlow and AWS Glue 🔹 Storage: Amazon S3 acts as the central data lake with fine-grained access control via AWS Lake Formation 🔹 Processing & Transformation: ELT pipelines orchestrated on Amazon EKS using tools like Argo, dbt, and Kubeflow 🔹 Analytics: Amazon Redshift with Spectrum enables seamless querying across warehouse and data lake 🔹 Security & Governance: Managed through AWS Firewall Manager and Lake Formation permissions 🔹 Automation: Infrastructure provisioned using AWS CDK and deployed via GitLab CI runners This kind of design enables scalability, cost efficiency, strong governance, and faster analytics delivery—while keeping operations fully automated and secure. 💡 A great example of how cloud-native services come together to support enterprise-grade data platforms. #AWS #DataEngineering #CloudArchitecture #DataPlatform #Analytics #ELT #BigData

The cloud landscape is vast, with AWS, Azure, Google Cloud, Oracle Cloud, and Alibaba Cloud offering a 𝘄𝗶𝗱𝗲 𝗿𝗮𝗻𝗴𝗲 𝗼𝗳 𝘀𝗲𝗿𝘃𝗶𝗰𝗲𝘀. However, navigating these services and understanding 𝘄𝗵𝗶𝗰𝗵 𝗽𝗹𝗮𝘁𝗳𝗼𝗿𝗺 𝗽𝗿𝗼𝘃𝗶𝗱𝗲𝘀 𝘁𝗵𝗲𝗺 can be overwhelming.  That’s why I’ve put together this 𝗖𝗹𝗼𝘂𝗱 𝗦𝗲𝗿𝘃𝗶𝗰𝗲𝘀 𝗖𝗵𝗲𝗮𝘁 𝗦𝗵𝗲𝗲𝘁—a side-by-side comparison of key cloud offerings across major providers.  𝗪𝗵𝘆 𝗧𝗵𝗶𝘀 𝗠𝗮𝘁𝘁𝗲𝗿𝘀  ✅ 𝗖𝗿𝗼𝘀𝘀-𝗖𝗹𝗼𝘂𝗱 𝗨𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴 – If you're working in 𝗺𝘂𝗹𝘁𝗶-𝗰𝗹𝗼𝘂𝗱 or considering a migration, this guide helps you quickly map services across providers.  ✅ 𝗙𝗮𝘀𝘁𝗲𝗿 𝗗𝗲𝗰𝗶𝘀𝗶𝗼𝗻-𝗠𝗮𝗸𝗶𝗻𝗴 – Choosing the right 𝗰𝗼𝗺𝗽𝘂𝘁𝗲, 𝘀𝘁𝗼𝗿𝗮𝗴𝗲, 𝗱𝗮𝘁𝗮𝗯𝗮𝘀𝗲, 𝗼𝗿 𝗔𝗜/𝗠𝗟 services just got easier.  ✅ 𝗕𝗿𝗶𝗱𝗴𝗶𝗻𝗴 𝘁𝗵𝗲 𝗚𝗮𝗽 – Whether you're a 𝗰𝗹𝗼𝘂𝗱 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁, 𝗗𝗲𝘃𝗢𝗽𝘀 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿, 𝗼𝗿 𝗔𝗜 𝗽𝗿𝗮𝗰𝘁𝗶𝘁𝗶𝗼𝗻𝗲𝗿, knowing equivalent services across platforms can save time and 𝗿𝗲𝗱𝘂𝗰𝗲 𝗰𝗼𝗺𝗽𝗹𝗲𝘅𝗶𝘁𝘆 in system design.  𝗞𝗲𝘆 𝗧𝗮𝗸𝗲𝗮𝘄𝗮𝘆𝘀:  🔹 AWS dominates with 𝗘𝗖𝟮, 𝗟𝗮𝗺𝗯𝗱𝗮, 𝗮𝗻𝗱 𝗦𝟯, but Azure and Google Cloud offer strong alternatives.  🔹 AI & ML services are becoming a core differentiator—Google’s 𝗩𝗲𝗿𝘁𝗲𝘅 𝗔𝗜, AWS 𝗦𝗮𝗴𝗲𝗠𝗮𝗸𝗲𝗿/𝗕𝗲𝗱𝗿𝗼𝗰𝗸, and Alibaba’s 𝗣𝗔𝗜 are top contenders.  🔹 𝗡𝗲𝘁𝘄𝗼𝗿𝗸𝗶𝗻𝗴 & 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆 services, from 𝗩𝗣𝗖𝘀 𝘁𝗼 𝗜𝗔𝗠, have cross-platform analogs but different 𝗹𝗲𝘃𝗲𝗹𝘀 𝗼𝗳 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻 𝗮𝗻𝗱 𝗶𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻.  🔹 Cloud databases, 𝗳𝗿𝗼𝗺 𝗗𝘆𝗻𝗮𝗺𝗼𝗗𝗕 𝘁𝗼 𝗕𝗶𝗴𝗤𝘂𝗲𝗿𝘆, are increasingly 𝘀𝗲𝗿𝘃𝗲𝗿𝗹𝗲𝘀𝘀 𝗮𝗻𝗱 𝗺𝗮𝗻𝗮𝗴𝗲𝗱, optimizing performance at scale.  Save this cheat sheet for reference and share it with your network!

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

How Modern Data Platforms Benefit Community Banks and Credit Unions? Modern data platforms, powered by technologies like generative AI and machine learning (ML), are transforming community banks and credit unions by improving efficiency, customer service, and more. Here’s how: ✔ Cloud-Based Infrastructure :- Enables fast digital services like mobile banking and online loans, cuts overhead costs, and speeds up product launches. ✔Generative AI :- Automates processes, predicts trends, and supports better decision-making, enhancing customer interactions and operations. ✔Conversational AI AI-driven chatbots and virtual assistants provide 24/7 support, answering questions and improving customer engagement. ✔Streamlined Operations Modern core banking systems integrated with these platforms reduce operational costs and improve efficiency. ✔Easy Data Access Centralized data makes it quicker for employees to access the latest info, improving service and problem-solving. ✔Customer Insights & Personalization AI and data analytics offer deeper customer insights, helping small banks personalize services and strengthen relationships. ✔Improved Security Features like encryption and access control protect against data breaches and ensure compliance. Discuss with me how these solutions can help your community bank or credit union stay ahead.

🌟 From Hadoop & Big Data to Data Engineering on GCP 🌟 As Data Engineers, we play a vital role in enabling data-driven decision-making. Here’s a quick overview of what we typically do: ✅ Manage data ingestion from diverse sources. ✅ Build batch pipelines. ✅ Develop streaming pipelines. ✅ Create ML and LLM pipelines. Now, what technologies or services do we use to achieve this on GCP? Let’s break it down: What are the technologies or services we use on Google Cloud Platform (GCP)? • For ingestion: GCP offers Cloud Data Fusion and Cloud Composer for ETL workflows. For real-time ingestion, Pub/Sub is a popular choice. Many organizations also use third-party tools like Informatica, Talend, or Fivetran. For API-based ingestion, Cloud Functions provides a serverless solution. • For batch processing: Cloud Dataflow, based on Apache Beam, is a key service for scalable batch data processing. GCP also supports Dataproc, which simplifies Spark and Hadoop-based workflows on the cloud. • For stream processing: GCP excels in stream processing with Pub/Sub and Dataflow. Pub/Sub handles real-time messaging, while Dataflow processes the streaming data with its unified batch and stream processing capabilities. • For machine learning: Vertex AI is the flagship platform for developing and deploying machine learning models on GCP. For exploratory data analysis and BI workflows, BigQuery ML provides integrated machine learning capabilities directly within BigQuery. • For data warehousing: BigQuery is GCP’s serverless data warehouse, offering high-performance analytics at scale. Its deep integration with other GCP services and SQL interface makes it a favorite among data engineers. • For visualization: GCP integrates seamlessly with Looker and third-party tools like Tableau and Power BI. Looker, in particular, provides advanced data exploration and visualization capabilities. • For orchestration: GCP relies on Cloud Composer (built on Apache Airflow) for orchestration, providing a powerful tool to manage data pipelines and workflows effectively. In short: In today’s Data Engineering world, the key skills on GCP are SQL, Python, BigQuery, Dataflow, Dataproc, Pub/Sub, Vertex AI, Cloud Composer, Cloud Functions, and Looker. Start with SQL, Python, BigQuery, and Dataflow and build on additional services as required by the role. 💡 “As Data Engineers, our role extends beyond tools—it’s about designing scalable and efficient pipelines that unlock the true potential of data. Staying updated with GCP’s innovations is essential for success in this dynamic field.” 👉 Follow Durga Gadiraju (me) on LinkedIn for more insights on Data Engineering, Cloud Technologies, and the evolving world of Big Data on GCP! #GCP #DataEngineering #SQL #Python #BigData #Cloud