How do you store big data?

Storing big data requires systems designed to handle large volumes, high velocity, and diverse formats while remaining scalable and fault-tolerant. The most common approach involves distributed storage architectures that split data across multiple nodes. For example, Hadoop Distributed File System (HDFS) breaks files into blocks and distributes them across a cluster, with replication ensuring data durability. Cloud-based solutions like Amazon S3 or Google Cloud Storage offer similar distributed object storage with built-in redundancy, scaling seamlessly as data grows. These systems avoid bottlenecks by spreading data and processing across hardware, allowing horizontal scaling.

Data formats and storage layers are optimized for specific use cases. Columnar formats like Parquet or ORC improve query performance for analytics by storing data in columns rather than rows, reducing I/O during aggregation. NoSQL databases like Cassandra or HBase handle high write throughput and flexible schemas for semi-structured data. For unstructured data (e.g., logs, images), object storage or distributed file systems are typical choices. Compression (Snappy, Zstandard) and partitioning (by date, region) are often applied to reduce storage costs and speed up access. For instance, partitioning log files by date allows queries to skip irrelevant data, improving efficiency.

Storage strategies also depend on data lifecycle. Hot data (frequently accessed) might reside in fast SSDs or in-memory systems like Redis. Warm data could use cheaper HDDs or tiered cloud storage, while cold data is archived to low-cost solutions like AWS Glacier. Tools like Apache Iceberg or Delta Lake add metadata layers on top of raw storage, enabling features like ACID transactions and time travel queries. For example, Iceberg tracks file-level metadata to optimize query planning in data lakes. Security measures like encryption (at rest and in transit) and access controls (IAM roles, Kerberos) are critical throughout. Managing metadata (schema, lineage) with tools like Apache Atlas ensures data remains discoverable and governable as systems scale.