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

🚀 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.

Here's the complete Google Cloud Data Analytics Stack - from raw data to business insights: (Save this — every analyst working in GCP needs to understand this) Most analysts know how to query BigQuery. Few understand the full stack it sits inside. That gap is what separates analysts who use the cloud from ones who understand it. Here is the full breakdown 👇 𝟭. 𝗗𝗮𝘁𝗮 𝗖𝗼𝗹𝗹𝗲𝗰𝘁𝗶𝗼𝗻 & 𝗜𝗻𝗴𝗲𝘀𝘁𝗶𝗼𝗻 -- Cloud Pub/Sub → Collects real-time events, logs, and streaming data -- Cloud Data Fusion → Builds no-code and low-code ingestion pipelines -- Transfer Services → Moves data from SaaS tools and other clouds 𝟮. 𝗗𝗮𝘁𝗮 𝗦𝘁𝗼𝗿𝗮𝗴𝗲 & 𝗪𝗮𝗿𝗲𝗵𝗼𝘂𝘀𝗶𝗻𝗴 -- Cloud Storage → Stores raw, processed, and curated data -- BigQuery → Analyzes large datasets using a serverless warehouse -- BigLake → Queries data across lakes, warehouses, and clouds 𝟯. 𝗗𝗮𝘁𝗮 𝗣𝗿𝗲𝗽𝗮𝗿𝗮𝘁𝗶𝗼𝗻 & 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻 -- Dataflow → Processes batch and streaming data -- BigQuery SQL → Transforms data directly inside the warehouse -- Dataform → Builds SQL-based data models and pipelines 𝟰. 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀, 𝗕𝗜 & 𝗥𝗲𝗽𝗼𝗿𝘁𝗶𝗻𝗴 -- Looker → Creates governed dashboards and self-service analytics -- Looker Studio → Builds simple reports from connected sources -- BigQuery Built-in Analytics → Runs ML and advanced SQL without moving data 𝟱. 𝗔𝗜-𝗣𝗼𝘄𝗲𝗿𝗲𝗱 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 -- Vertex AI → Builds and deploys ML models -- Vertex AI Search → Creates conversational experiences over data -- Pre-built AI Services → Extracts insights from documents, images, and text 𝟲. 𝗚𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 & 𝗗𝗮𝘁𝗮 𝗦𝗵𝗮𝗿𝗶𝗻𝗴 -- Dataplex → Manages metadata, lineage, and discovery -- BigQuery Governance → Controls row and column-level access -- Analytics Hub → Shares trusted datasets across teams You do not need to master all of these. But understanding the full stack makes you a better analyst and far more valuable on any cloud team. Which layer are you working with most? ♻️ Repost to help someone learning the GCP stack 💭 Tag a data analyst who needs this 📩 Get my full data analytics career guide: https://lnkd.in/gjUqmQ5H