Data warehouse vs data lake in HLD - Design Approaches Compared

Design: Data Storage and Analytics Platform

Design the architecture comparing data warehouse and data lake approaches for storing and analyzing enterprise data. Exclude detailed ETL pipeline design and specific vendor implementations.

Functional Requirements

FR1: Store large volumes of structured and unstructured data

FR2: Support fast querying and reporting for business intelligence

FR3: Allow flexible data ingestion from multiple sources

FR4: Enable data scientists to explore raw data for advanced analytics

FR5: Ensure data quality, security, and governance

FR6: Support historical data retention and versioning

Non-Functional Requirements

NFR1: Handle petabytes of data with scalable storage

NFR2: Query latency under 5 seconds for typical reports

NFR3: Availability of 99.9% uptime

NFR4: Cost-effective storage and compute resource usage

Think Before You Design

Questions to Ask

❓ Question 1

❓ Question 2

❓ Question 3

❓ Question 4

❓ Question 5

Key Components

Data ingestion pipelines

Storage layers (structured vs raw data)

Metadata and catalog services

Query engines and analytics tools

Data governance and security modules

Design Patterns

Lambda architecture for combining batch and real-time data

Schema-on-write vs schema-on-read

Data partitioning and indexing

Data lifecycle management

Reference Architecture

                +---------------------+                 
                |   Data Sources      |                 
                +----------+----------+                 
                           |                            
          +----------------+----------------+           
          |                                 |           
+---------v---------+             +---------v---------+ 
|   Data Lake       |             | Data Warehouse    | 
| (Raw, unstructured|             | (Structured,      | 
|  data storage)    |             |  cleaned data)    | 
+---------+---------+             +---------+---------+ 
          |                                 |           
          |                                 |           
+---------v---------+             +---------v---------+ 
| Metadata Catalog  |             | Metadata Catalog  | 
+---------+---------+             +---------+---------+ 
          |                                 |           
          +----------------+----------------+           
                           |                            
                +----------v----------+                 
                | Analytics & BI Tools |                 
                +---------------------+                 

Components

Data Sources

Various (databases, logs, IoT devices, APIs)

Provide raw data in different formats and structures

Data Lake Storage

Object storage (e.g., Amazon S3, Azure Blob Storage)

Store raw, unstructured, semi-structured, and structured data without predefined schema

Data Warehouse Storage

Columnar relational database (e.g., Snowflake, Redshift, BigQuery)

Store cleaned, structured data optimized for fast querying and reporting

Metadata Catalog

Data catalog tools (e.g., AWS Glue Data Catalog, Apache Hive Metastore)

Manage metadata, schemas, and data discovery for both lake and warehouse

Analytics & BI Tools

BI platforms (e.g., Tableau, Power BI), SQL query engines

Enable users to run queries, generate reports, and perform data analysis

Request Flow

1. 1. Data is ingested from various sources into the data lake in raw format.

2. 2. Data scientists explore raw data in the data lake for advanced analytics and machine learning.

3. 3. ETL/ELT processes clean and transform relevant data from the data lake into the data warehouse.

4. 4. Business users query the data warehouse for fast, reliable reports and dashboards.

5. 5. Metadata catalogs maintain schema and data lineage information for both storage layers.

6. 6. Governance and security policies are enforced across both data lake and warehouse.

Database Schema

Entities: DataSource (id, type, format), RawDataFile (id, source_id, path, ingestion_time), CleanedDataTable (id, schema, last_updated), MetadataCatalog (id, entity_type, schema, lineage), User (id, role, permissions). Relationships: DataSource 1:N RawDataFile, RawDataFile N:1 MetadataCatalog, CleanedDataTable N:1 MetadataCatalog, User N:N Permissions.

Scaling Discussion

Bottlenecks

Storage limits and costs as data volume grows

Query performance degradation with large datasets

Metadata management complexity with many data assets

Data ingestion throughput bottlenecks

Ensuring data consistency between lake and warehouse

Solutions

Use scalable object storage with lifecycle policies to archive old data

Implement partitioning, indexing, and caching in warehouse queries

Adopt automated metadata management and cataloging tools

Parallelize and batch data ingestion pipelines

Use incremental data processing and strong data validation in ETL

Interview Tips

Time: Spend 10 minutes understanding requirements and clarifying use cases, 20 minutes designing the architecture and explaining components, 10 minutes discussing scaling and trade-offs, 5 minutes summarizing.

Difference between schema-on-read (data lake) and schema-on-write (data warehouse)

Trade-offs between flexibility and query performance

Importance of metadata and governance in hybrid architectures

How data flows from raw ingestion to cleaned analytics-ready data

Scaling strategies for storage, compute, and metadata