Cloud Storage for Big Data Analytics

“𝗦𝟯, 𝗔𝗗𝗟𝗦, 𝗚𝗖𝗦? 𝗝𝘂𝘀𝘁 𝘀𝘁𝗼𝗿𝗮𝗴𝗲, 𝗿𝗶𝗴𝗵𝘁?” Not quite. Here’s a better way to think about it 👇 𝗖𝗹𝗼𝘂𝗱 𝗦𝘁𝗼𝗿𝗮𝗴𝗲 — 𝗠𝗼𝗿𝗲 𝗧𝗵𝗮𝗻 𝗝𝘂𝘀𝘁 𝗮 𝗙𝗶𝗹𝗲 𝗗𝘂𝗺𝗽 Cloud storage is like a hotel for your data. It checks in from various sources — APIs, apps, pipelines. Some stay temporarily (like staging or temp files) Others are long-term guests (like audit logs or historical records) You control who can access it (IAM), what they can do (read/write), and how long it stays (retention policies) There’s even housekeeping involved — with lifecycle rules, versioning, deduplication, and cost optimization. ⚠️ 𝗪𝗵𝗮𝘁 𝗣𝗲𝗼𝗽𝗹𝗲 𝗧𝗵𝗶𝗻𝗸 𝗗𝗘𝘀 𝗗𝗼: "Just dump the data to S3 and move on." ✅ 𝗪𝗵𝗮𝘁 𝗔𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗛𝗮𝗽𝗽𝗲𝗻𝘀:   • Design folder structures for efficient querying and partitioning   • Choose the right storage class (Standard, Infrequent Access, Glacier)   • Use optimal file formats (Parquet, ORC) and compression (Snappy, Zstandard)   • Set access controls, encryption, and auditing (IAM roles, KMS, logging)   • Enable direct querying (Athena, Synapse, BigQuery on GCS)   • Integrate storage across cloud platforms (multi-cloud architectures)   • Automate lifecycle management to control cost and reduce clutter   • Leverage features like S3 Select, signed URLs, and Delta format for smart access 📌 Takeaway: Cloud storage isn’t where data ends up — it’s where the journey begins. How you design and manage it defines the performance, scalability, and reliability of everything downstream. #data #engineering #reeltorealdata #python #sql #cloud

Imagine you have 5 TB of data stored in Azure Data Lake Storage Gen2 — this data includes 500 million records and 100 columns, stored in a CSV format. Now, your business use case is simple: ✅ Fetch data for 1 specific city out of 100 cities ✅ Retrieve only 10 columns out of the 100 Assuming data is evenly distributed, that means: 📉 You only need 1% of the rows and 10% of the columns, 📦 Which is ~0.1% of the entire dataset, or roughly 5 GB. Now let’s run a query using Azure Synapse Analytics - Serverless SQL Pool. 🧨 Worst Case: If you're querying the raw CSV file without compression or partitioning, Synapse will scan the entire 5 TB. 💸 The cost is $5 per TB scanned, so you pay $25 for this query. That’s expensive for such a small slice of data! 🔧 Now, let’s optimize: ✅ Convert the data into Parquet format – a columnar storage file type 📉 This reduces your storage size to ~2 TB (or even less with Snappy compression) ✅ Partition the data by city, so that each city has its own folder Now when you run the query: You're only scanning 1 partition (1 city) → ~20 GB You only need 10 columns out of 100 → 10% of 20 GB = 2 GB 💰 Query cost? Just $0.01 💡 What did we apply? Column Pruning by using Parquet Row Pruning via Partitioning Compression to save storage and scan cost That’s 2500x cheaper than the original query! 👉 This is how knowing the internals of Azure’s big data services can drastically reduce cost and improve performance. #Azure #DataLake #AzureSynapse #BigData #DataEngineering #CloudOptimization #Parquet #Partitioning #CostSaving #ServerlessSQL

The strongest data platforms don’t just store data — they differentiate through architecture. At a glance, Snowflake, Google BigQuery, Amazon Redshift, and Databricks may look similar, but under the hood they solve performance, scale, and concurrency in fundamentally different ways. Understanding these differences is what helps you align the platform to your workload — not the other way around 🔹 Snowflake • True separation of storage and compute via independent virtual warehouses • Each workload runs in isolation → minimal contention even at high concurrency • Near-instant scaling with pay-per-use compute model • Strong support for semi-structured data (JSON, Parquet, etc.) Best for: High-concurrency BI workloads, multi-team environments, and organizations that want simplicity without managing infrastructure 🔹 BigQuery • Fully serverless architecture with distributed execution trees • Decouples compute entirely — no clusters, no tuning, auto resource allocation • Columnar storage + execution engine optimized for large scans • Pricing model (on-demand vs flat-rate) directly tied to query patterns Best for: Large-scale analytics, ad hoc exploration, event data processing, and teams deep in the Google Cloud ecosystem 🔹 Redshift • Traditional MPP system with leader node + distributed compute nodes • Data distribution (keys, sorting) plays a critical role in performance • Offers predictable performance for structured, repeatable workloads • RA3 nodes + Spectrum extend capabilities to data lake querying Best for: Enterprise data warehousing, stable reporting pipelines, and AWS-first organizations optimizing for cost and control 🔹 Databricks • Lakehouse architecture combining flexibility of data lakes with warehouse performance • Powered by Spark, Photon engine, and Delta Lake for ACID transactions • Unified platform for batch, streaming, and ML workloads • Strong governance layer with Unity Catalog Best for: Data engineering pipelines, real-time processing, AI/ML workflows, and teams building unified data + AI platforms 🔍 What this means in practice The decision is not about features — it’s about fit: • Concurrency vs throughput • Structured vs semi/unstructured data • SQL analytics vs ML pipelines • Cost predictability vs flexibility There is no universal winner here. The most effective data leaders don’t start by picking a tool — they start by understanding the architecture their workloads demand. Because in modern data stacks, 👉 Architecture is strategy. Curious — which platform are you using today: Snowflake, BigQuery, Redshift, or Databricks? CC: Sumit Gupta

🚀 Azure Data Lake: What, Why, and How I recently reviewed a comprehensive presentation on Azure Data Lake architecture that clearly explains what a data lake is, why organizations adopt it, and how to design it effectively on Azure. Some key takeaways: - A data lake enables schema-on-read, allowing teams to ingest structured, semi-structured, and unstructured data quickly while deferring modeling until business value is understood. - Azure Data Lake Storage Gen2 combines object storage and a hierarchical file system, improving analytics performance, access control, and cost efficiency. - Multi-modal access allows tools such as Databricks, HDInsight, Spark, Power BI, and Data Factory to work on the same data without duplication. - Designing a data lake requires careful planning around data organization, security boundaries, governance, lifecycle management, and cost trade-offs. - Azure data lakes often operate as part of a multi-platform architecture that supports batch processing, streaming, advanced analytics, and machine learning use cases. A strong reminder that while data lakes help teams move fast, thoughtful design and governance are critical to avoid turning them into data swamps and to ensure long-term scalability. Highly recommended for anyone working with Azure, cloud architecture, big data, or analytics. #Azure #DataLake #ADLSGen2 #CloudArchitecture #BigData #AzureAnalytics #DataEngineering #IaC

Bloomberg reported that Tabular – a company that offered Iceberg table format management – was acquired for $2 billion with just over $1m in revenue. I actually do want to break down Iceberg, why it was built, and how its useful. Apache Iceberg is a table format for large analytic datasets that utilizes several key data structures to optimize performance with a design for bottomless cloud storage. You'll see that it's good for read performance at the cost of fast write performance. Snapshot Tree: ✅ Tracks table history and metadata ✅ Enables time travel queries and rollbacks ✅ Optimized for fast metadata retrieval Manifest Lists: ✅ Index of all data files in a snapshot ✅ Partitioned for efficient pruning during queries ✅ Optimized for read performance Manifests: ✅ Contain metadata for data files ✅ Include partition data and column-level statistics ✅ Enable fine-grained filtering and partition pruning Data Files: ✅ Store actual table data ✅ Typically in columnar formats (e.g., Parquet) ✅ Optimized for analytical workloads Performance characteristics: Read-optimized: ✅ Efficient metadata handling reduces I/O ✅ Partition pruning and statistics enable fast data skipping ✅ Supports scan planning for distributed query execution Write considerations: ✅ Uses copy-on-write strategy for updates and deletes meaning it will copy the underlying tree structures rather than traversing back to a point on disk and re-writing it. ✅ Optimized for bottomless cloud storage architectures ✅ Enables efficient versioning and time travel without excessive storage costs Storage efficiency: ✅ Copy-on-write approach allows for immutable data files ✅ Leverages cloud storage's ability to handle many small files efficiently ✅ Reduces storage costs through file-level deduplication across versions Iceberg's architecture is primarily optimized for read-heavy analytical workloads on cloud storage platforms, offering strong consistency guarantees, efficient query performance at scale, and cost-effective storage utilization through its copy-on-write mechanism. Now why did Databricks spend so much on it? Iceberg gives you a sane format to store massive amounts of data on commodity cloud storage buckets. Now that many companies have amassed petabytes of data in S3, they're probably not moving it (insane egress costs). So the next stage in our evolution as an industry is making it easy to query it.

Catastrophic risk modeling means living in a world of gigabytes, terabytes, and sometimes petabytes per analytics run. I talked with Karthick Shanmugam from Verisk, a market leader in risk modeling for insurance and reinsurance, about how they’re handling that scale on AWS. Their architecture uses: Amazon S3 + Apache Iceberg as the scalable, open data storage layer  Amazon Redshift as the analytical processing engine – https://lnkd.in/eW5Y_Qnc Amazon QuickSight for visualization – https://lnkd.in/eukavW7T Amazon EC2 and the broader AWS ecosystem around it They’re analyzing massive risk datasets and seeing performance improvements on the order of 10-15x (depending on the use case) when using Redshift to aggregate and visualize data for customers. His team is moving from tightly coupled storage + compute to separating storage (S3 + Iceberg) and compute (Redshift), so storage can evolve independently while customers choose the right compute for their needs. If you’re in a similar high-scale analytics space, Karthik’s recommendation is to use an open table format on S3 and pair it with a strong analytical engine like Amazon Redshift to get both flexibility and speed.

🚀 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

Google BigQuery: Enabling Scalable and Efficient Data Analytics Google BigQuery continues to play a critical role in modern data architectures by providing a fully managed, serverless data warehouse designed for high-performance analytics at scale. Key Capabilities: • Serverless Architecture – Eliminates infrastructure management and allows teams to focus on data and insights • High-Performance SQL Engine – Supports complex analytical queries over large-scale datasets efficiently • Separation of Storage and Compute – Enables flexible scaling and cost optimization • Native Integration with GCP Services – Seamlessly works with services like Dataflow, Pub/Sub, and Cloud Storage • Built-in Machine Learning (BigQuery ML) – Allows model creation and deployment directly using SQL Practical Implementation Areas: • Enterprise data warehouse modernization • Batch and near real-time data processing • Advanced analytics and reporting • Data-driven decision support systems Best Practices: • Use partitioning and clustering to improve performance and control costs • Optimize SQL queries to minimize data scans • Implement orchestration tools such as Airflow or Cloud Composer • Establish strong data quality and governance frameworks • Monitor workloads and manage resource allocation effectively Conclusion: Google BigQuery provides a robust foundation for organizations looking to modernize their data platforms and enable scalable, high-performance analytics. #BigQuery #GoogleCloud #DataEngineering #DataAnalytics #CloudComputing #SQL #DataWarehouse