Data Storage Solutions

Modern data platforms are built on one core foundation: storage. Every pipeline, dashboard, ML model, and AI system ultimately depends on how well your data is stored, accessed, and scaled. That’s why understanding today’s Big Data storage landscape matters. Here’s a practical snapshot of the Top Big Data Storage Solutions in 2026 and where each one fits best: - Amazon S3 for cloud data lakes and durable object storage at massive scale. - Google Cloud Storage for GCP-native analytics and machine learning workloads. - Azure Blob Storage for enterprise-grade unstructured data with compliance controls. - IBM Storage for hybrid environments needing block, file, and object storage. - Apache Hadoop (HDFS) for distributed storage powering large-scale batch processing. - MongoDB for flexible, document-based application data with horizontal scaling. - Apache Cassandra for globally distributed, always-on workloads. - Snowflake for cloud data warehousing with separated compute and storage. - Cloudian HyperStore for S3-compatible object storage in private or hybrid clouds. - Amazon Redshift for high-performance analytics using columnar storage and MPP. A simple way to think about it: - Use S3, GCS, Azure Blob, or HyperStore for raw data lakes. - Use Snowflake or Redshift for analytics and reporting. - Use HDFS for distributed processing frameworks. - Use MongoDB or Cassandra for operational big data. - Use IBM Storage for hybrid enterprise architectures. Strong data platforms rarely rely on just one system. They combine multiple storage layers based on workload, cost, latency, and governance needs. Save this if you’re working in data engineering. Share it with your platform team. Because choosing the right storage stack early makes everything downstream easier.

💡There’s an interesting trend I observed with organizations recently: they are choosing to save money and simplify their operations by using slower but cheaper storage systems. This is especially true when they handle large amounts of data and sub-second latency isn't critical. Let’s find out what’s motivating this. Data loses its value over time. Once data becomes older and rarely accessed, real-time performance becomes less crucial. While developers need to access historical data for analysis, ad hoc queries, and compliance requirements, they can accept some latency. Their priority now shifts to storing this older data most cost-effectively and efficiently. Compute-storage decoupling is something that we inherited from the Hadoop era, allowing storage systems to use tiered storage for improved cost-efficiency and scalability. ✳️ Object stores became the de facto tiered storage Amazon S3 was officially launched in 2006. Almost 20 years later and with trillions of objects stored, we now have reliable infinite storage. People started to call this cheap, infinitely scalable storage a Data Lake(or Lakehouse nowadays). For developers, it offers a simple path to disaster recovery. When you upload a file to S3, you immediately get eleven nines of durability—that's 99.999999999%. To put this in perspective: if you store 10,000 objects, you might lose just one in 10 million years. As object stores like S3 become more affordable, databases and OLAP systems have increasingly utilized deep object storage to enhance cost efficiency and durability. For example, PGAA, the EDB’s analytics extension for Postgres, allows you to query hot data and cold data with a single dedicated node, ensuring optimal performance by automatically offloading cold data to columnar tables in object storage, reducing the complexity of managing analytics over multiple data tiers. ✳️ Not only databases, but streaming data platforms are evolving too Redpanda and WarpStream show how modern streaming platforms can save money while maintaining good performance. They do this by using a mix of fast local storage (SSDs) for quick access and cloud storage for most of their data, avoiding costly cross-AZ data transfers. ✳️ Why not make the object stores Iceberg compatible? That will transform simple storage solutions into powerful data management systems like data lakehouses. This compatibility brings essential features like schema evolution, time travel capabilities, ACID transactions, and performance optimizations—all while maintaining the cost benefits of object storage. This gives organizations the flexibility to choose their own query engine and catalog, making data platforms more modular and composable.

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! 👇

Your data has a temperature, and you are wasting money if you don't know it. Hot, Warm, and Cold data. Storing data is not just about saving it and forgetting about it. You need to understand how often you will access the data and how long you should keep it. You can group data into three categories based on how often it's accessed: 𝗛𝗼𝘁 𝗗𝗮𝘁𝗮 • What It Is: Data that you need often and fast. • Where It's Stored: On fast storage like SSDs or even in memory. • Examples: Things like product recommendations or cached search results. • Cost: Storing hot data is expensive, but accessing it is cheap because it's always ready to go. 𝗪𝗮𝗿𝗺 𝗗𝗮𝘁𝗮 • What It Is: Data you access occasionally, like once a month. • Where It's Stored: On slower but still accessible storage, e.g., Amazon S3 Infrequently Accessed Tier, Google Nearline. • Examples: Older logs or data that are not as frequently needed. This could be data that you use for reporting or analytics. • Cost: It is cheaper to store than hot data, but accessing it costs a bit more. 𝗖𝗼𝗹𝗱 𝗗𝗮𝘁𝗮 • What It Is: Data are rarely accessed and primarily kept for long-term storage. • Where It's Stored: On the cheapest storage options, like HDDs or cloud archive services. • Examples: Old backups or records that you keep for compliance reasons. • Cost: It is very cheap to store but can be slow and expensive to access. Retention is a different animal, explains "how long you should keep data" and is based on 4 pillars: 𝗩𝗮𝗹𝘂𝗲 Is this data critical for you, or can it be recreated if needed? You should keep Important data for longer. 𝗧𝗶𝗺𝗲 For data you store in fast-access places like memory, set a time limit (TTL) for how long it stays there before moving it to cheaper storage. 𝗖𝗼𝗺𝗽𝗹𝗶𝗮𝗻𝗰𝗲 Some laws require you to keep data for a certain amount of time or delete it after a specific period. Make sure your data storage practices follow these rules. 𝗖𝗼𝘀𝘁 Storing data costs money. To save on storage costs, you can automate deleting or archiving data when it's no longer needed. Don't just store data—manage it. Save this for your next Storage Decision.

Storage is no longer just “where data lives”.. it’s how AI remembers, learns, and thinks faster than ever. In modern ML systems, storage architecture directly impacts speed, efficiency, and cost. As a cloud engineer, understanding how to map the right storage type to each AI workload is critical. We can categorize storage along two key dimensions: → Performance vs Capacity Optimized → File vs Object Protocol Here’s how storage supports each stage of the AI/ML lifecycle: Raw Data Ingest → Stores large volumes of raw, unstructured data such as images, logs, or text → Requires scalable, cost-effective storage that supports parallel ingestion Data Preparation → Uses high-performance file storage for cleaning, labeling, and transforming data → Requires frequent, low-latency reads/writes to enable fast iteration and processing Training → Uses high-performance file or in-memory storage to feed large datasets to accelerators → Demands high-speed, parallel data access to keep GPU clusters fully utilized Fine-Tuning → Uses high-performance file or in-memory storage for task-specific model updates → Requires low latency and high throughput to handle compute-intensive workloads Inference / Deployment → Relies on in-memory or local storage (CPU/GPU) to serve model predictions → Prioritizes ultra-low latency for responsive, real-time user interactions Archiving → Uses object storage for historical or infrequently accessed data → Optimized for long-term retention, cost-efficiency, and scalable capacity Now, each of these use cases may rely on different storage solutions—whether it's object storage (like S3, GCS, Azure Blob), high-performance file systems, or block storage. The key takeaway: Performance matters. Throughput, latency, and access patterns are critical—especially when feeding data to compute-hungry accelerators like GPUs and TPUs. The faster you serve data, the more efficient your pipeline. And with those accelerators costing a premium, idle time is wasted money. Read the full newsletter here for a deeper dive: https://lnkd.in/gnfpprku • • • If you found this insightful: 🔔 Follow me (Vishakha Sadhwani) for more AI infrastructure insights ♻️ Share so others can learn as well!

Using the wrong data storage can destroy your AI ambitions. Why? Not all solutions are created equal. Let's talk about 2 common options: • Data Warehouses • Data Lakes Confusing these two can lead to costly mistakes, so let's define them. A 𝗗𝗮𝘁𝗮 𝗪𝗮𝗿𝗲𝗵𝗼𝘂𝘀𝗲 stores structured, processed data that’s been cleaned and organized for specific business purposes. It’s optimized for fast queries and reliable reporting across departments. A 𝗗𝗮𝘁𝗮 𝗟𝗮𝗸𝗲 holds raw, unprocessed data in its original form. This includes everything (structured and unstructured data) which makes it highly flexible and ideal for machine learning and deep data exploration. Why does this distinction matter for your AI products? Because your data foundation directly impacts the quality and scope of your AI models. For example: • In 𝗵𝗲𝗮𝗹𝘁𝗵𝗰𝗮𝗿𝗲, unstructured data like clinical notes dominate, making data lakes a better fit to support AI that can handle complex, varied inputs. • In 𝗳𝗶𝗻𝗮𝗻𝗰𝗲 and many traditional businesses, highly structured data means data warehouses provide consistency, governance, and easier access for analytics teams. Choosing the wrong storage can limit your AI’s effectiveness - either by restricting access to rich data or complicating analysis. To unlock AI’s true potential, start by asking: • 𝗪𝗵𝗮𝘁 𝘁𝘆𝗽𝗲 𝗼𝗳 𝗱𝗮𝘁𝗮 𝗱𝗼𝗲𝘀 𝗺𝘆 𝘂𝘀𝗲 𝗰𝗮𝘀𝗲 𝗻𝗲𝗲𝗱? • 𝗛𝗼𝘄 𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲𝗱 𝗼𝗿 𝘂𝗻𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲𝗱 𝗶𝘀 𝘁𝗵𝗮𝘁 𝗱𝗮𝘁𝗮? • 𝗪𝗵𝗮𝘁 𝗶𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 𝘄𝗶𝗹𝗹 𝗯𝗲𝘀𝘁 𝘀𝘂𝗽𝗽𝗼𝗿𝘁 𝗿𝗮𝗽𝗶𝗱 𝗔𝗜 𝗶𝘁𝗲𝗿𝗮𝘁𝗶𝗼𝗻 𝗮𝗻𝗱 𝘀𝗰𝗮𝗹𝗮𝗯𝗶𝗹𝗶𝘁𝘆? Get these right, and you’re already a step ahead on your AI journey. ♻️ Share this with anyone building AI products or strategies. Follow me for more hands-on AI product leadership insights

I often hear the term object storage, and at first glance, it might seem like regular file storage—but it’s actually quite different. If you're working in the cloud, you're almost certainly storing data in some form of object storage. So, what exactly is object storage, and where is it used? Unlike traditional file storage, which organizes data in folders and hierarchies, object storage uses a flat structure. Each file (or object) is stored with a unique identifier and metadata, making it easier to retrieve and manage at scale. Why Do Data Engineers Use Object Storage? It’s scalable, cost-effective, and flexible, allowing us to store structured, semi-structured, and unstructured data without worrying about rigid file systems. Common use cases include: Data Lakes – Storing raw and processed data for analytics and machine learning Backups & Archiving – Keeping historical data and logs for long-term storage Big Data Processing – Working with tools like Apache Spark, Databricks, and Hadoop Streaming & IoT – Handling large-scale event-driven workloads Where Do You See Object Storage? If you're using the cloud, you're already working with object storage. Popular services include: AWS S3 (Simple Storage Service) Azure Blob Storage Google Cloud Storage (GCS) On-prem solutions like MinIO and Ceph for hybrid environments Key Takeaway Object storage isn’t just another way to store files—it’s the backbone of modern data engineering. It’s perfect for big data workloads, batch processing, and real-time analytics, but it’s not meant for transactional databases. If you're new to object storage, start experimenting with S3, Blob Storage, or GCS to see how it works in action. It’s one of those must-know concepts for any cloud-based data engineer.

Modern Storage Systems Every system you build, whether it's a mobile app, a database engine, or an AI pipeline, eventually hits the same bottleneck: storage. And the storage world today is far more diverse than “HDD vs SSD.” Here’s a breakdown of how today’s storage stack actually looks: Primary Storage (where speed matters most): This is memory that sits closest to the CPU. - L1/L2/L3 caches, SRAM, DRAM, and newer options like PMem/NVDIMM. Blazing fast but volatile. The moment power drops, everything is gone. Local Storage (your machine’s own hardware): HDDs, SSDs, USB drives, SD cards, optical media, even magnetic tape (still used for archival backups). Networked Storage (shared over the network): - SAN for block-level access.  - NAS for file-level access.  - Object storage and distributed file systems for large-scale clusters. This is what enterprises use for shared storage, centralized backups, and high availability setups. Cloud Storage (scalable + managed): - Block storage like EBS, Azure Disks, GCP PD for virtual machines.  - Object storage like S3, Azure Blob, and GCP Cloud Storage for massive unstructured data.  - File storage like EFS, Azure Files, and GCP Filestore for distributed applications. Cloud Databases (storage + compute + scalability baked in):  - Relational engines like RDS, Azure SQL, Cloud SQL.  - NoSQL systems like DynamoDB, Bigtable, Cosmos DB. Over to you: If you had to choose one storage technology for a brand-new system, where would you start, block, file, object, or a database service? -- Subscribe to our weekly newsletter to get a Free System Design PDF (368 pages): https://lnkd.in/grvckqjv #systemdesign #coding #interviewtips  .