Building a Business-Driven Data Analytics Practice

Over the coming weeks, I will explore best practices in building a business-driven data analytics practice.

For today, let’s start with what I’ll characterize as the “basics” - what is the difference between a “Data Warehouse”, a “Data Lake”, and a “Data Lakehouse”?

A “Data Warehouse” is a structured, high-performance system optimized for querying and analyzing structured data (like numbers, tables, and categories).

Key traits:

• Stores data in rows and columns (like a giant Excel sheet, but on steroids).
• Used mostly for business intelligence (BI) and reporting.
• Data is cleaned, transformed, and modeled before being loaded (ETL process).
• High performance, but expensive and less flexible.

Best for:

• Dashboards
• Financial reporting
• Operational metrics

Examples: Snowflake, Amazon Redshift, Google BigQuery

A “Data Lake” is a large, scalable storage repository that can hold raw data in its native format, whether it's structured, semi-structured, or unstructured (think logs, audio files, videos, JSON, etc.).

 Key traits:

• Stores everything, often without structure or schema.
• Cheap and easy to scale (especially on cloud storage like S3 or Azure Blob).
• Data is usually processed after it’s stored (ELT process).
• Slower performance for querying.
• Risk of becoming a "data swamp" if not managed well.

Best for:

• Raw data ingestion
• Machine learning prep
• Long-term data storage

Examples: Amazon S3, Azure Data Lake, Hadoop

A “Data Lakehouse” is a newer architecture that combines the low-cost, flexible storage of a data ‘lake’ with the reliability and performance of a data ware’house’.

Key traits:

• Supports both structured and unstructured data.
• Allows for atomicity, consistency, isolation, and durability (ACID) transactions, data governance, and fast SQL queries.
• Uses open formats like Delta Lake, Apache Iceberg, or Hudi.
• Lets analysts, engineers, and data scientists work in one place.

Best for:

• Unified analytics and AI workloads
• Teams that need both data science and Business Intelligence (BI)
• Reducing data duplication and pipeline complexity

 Examples: Databricks Lakehouse, Snowflake with Iceberg/Delta integration, Dremio

Final thoughts for today – As organizations handle increasing amounts of data, choosing the right architecture is key. The three main approaches - Data Warehouse, Data Lake, and Data Lakehouse - each serve different purposes in today’s business world.

Data Warehouse

A data warehouse is optimized for structured data and analytics. It provides fast, reliable performance for reporting, dashboards, and business intelligence. Data is cleaned and modeled before it’s loaded, making it highly organized but less flexible. It’s great for known, repeatable queries but comes with higher storage and compute costs. 

Data Lake

A data lake is built to store raw, unstructured, and semi-structured data at scale. It’s cost-effective and highly flexible, making it ideal for data science, big data processing, and machine learning. However, without strong governance, it can become disorganized and difficult to manage, earning the nickname “data swamp.”

Data Lakehouse

A data lakehouse combines the scalability and flexibility of a data lake with the data management and performance features of a data warehouse. It supports a wide variety of data types and workloads—from real-time analytics to AI—while maintaining governance, reliability, and lower cost. It’s an all-in-one modern architecture for teams that want to simplify their data stack without sacrificing power.