How is data stored for analytics purposes?

Data for analytics is typically stored in structured repositories optimized for fast querying and large-scale processing. These systems prioritize efficiency, scalability, and the ability to handle complex analytical workloads. Common solutions include data warehouses (like Amazon Redshift or Google BigQuery), data lakes (built on platforms such as AWS S3 or Azure Data Lake), and hybrid approaches like lakehouses (e.g., Databricks Delta Lake). Data warehouses store structured data in tables with predefined schemas, using columnar storage formats (e.g., Parquet, ORC) to compress data and speed up aggregations. Data lakes accommodate unstructured or semi-structured data (JSON logs, CSV files) and often serve as raw data reservoirs before processing. Lakehouses combine aspects of both, enabling schema enforcement and ACID transactions while retaining the flexibility of a data lake.

The storage layer is designed around analytical query patterns. For example, time-series data might be partitioned by date to allow efficient filtering, while frequently joined tables are colocated to reduce network latency. Indexing strategies (e.g., bitmap indexes) and partitioning schemes help avoid full-table scans. Tools like Apache Iceberg or Apache Hudi add transactional consistency and versioning to file-based storage, enabling reliable updates and time travel queries. Data is often transformed into star or snowflake schemas, with fact tables (e.g., sales transactions) linked to dimension tables (products, customers) to simplify reporting. Precomputed aggregates (daily revenue totals) or materialized views further optimize performance for common queries.

Pipeline tooling and governance also shape storage decisions. ETL/ELT processes (using tools like Airflow or dbt) clean and structure raw data before loading it into analytical storage. Metadata management (via tools like AWS Glue Data Catalog) tracks data lineage, schema changes, and access controls. For example, a retail company might store raw clickstream logs in a data lake, transform them into a structured format with user session metrics, then load aggregated results into a warehouse for dashboarding. Security practices like encryption at rest, role-based access, and audit logs ensure compliance. The combination of storage format, schema design, and pipeline orchestration creates a foundation for scalable, repeatable analytics.