Database vs Data Warehouse vs Data Lake | What is the Difference?

Summary

This video explores the distinctions between databases, data warehouses, and data lakes. Databases are ideal for transactional data storage, offering real-time access and a flexible schema. Data warehouses, on the other hand, are designed for analytical processing, housing summarized historical data via ETL processes. Data lakes serve as repositories for all types of data, both structured and unstructured, offering flexibility for future analytics but requiring additional processing for use. The video highlights that each serves unique purposes and can coexist within an organization.

Takeaways

• 🗄️ A database is typically a relational database used for capturing and storing data via an OLTP (Online Transactional Process).
• 📊 A data warehouse is a type of database designed for analytical processing or OLAP (Online Analytical Processing) to analyze large amounts of data.
• 🔄 Data warehouses receive data from operational databases through an ETL (Extract, Transform, Load) process, which extracts, transforms, and loads the data for analysis.
• 📈 Data in a data warehouse is usually summarized and historical, not necessarily current, and is optimized for fast querying and reporting.
• 📑 Databases store highly detailed data in table format with columns and rows, allowing for flexible schema changes.
• 🚫 Data warehouses have a more rigid schema and require careful planning for data structure, unlike databases.
• 📉 Databases are slower for querying large amounts of data and can slow down transaction processing, whereas data warehouses are designed to be fast for querying without affecting transactions.
• 💧 A data lake is designed to store any type of data, structured or unstructured, in its raw form.
• 🤖 Data lakes are particularly useful for machine learning and AI applications where raw data is used to create models.
• 🛠️ While data in a data lake is not immediately usable for analytics, it can be cleaned and structured for use in databases or data warehouses if needed.
• 🏢 Companies may use all three - databases, data warehouses, and data lakes - to serve different data storage and processing needs.

Q & A

What is the primary function of a database?

A database is primarily used for recording transactions or capturing and storing data via an OLTP (Online Transactional Process), which is ideal for real-time data management.

How is data stored in a database?

Data in a database is stored in tables with columns and rows, and it is highly detailed, allowing users to see every single aspect of the data.

What is the difference between a database and a data warehouse?

A database is used for transactional processing and stores detailed, real-time data, while a data warehouse is used for analytical processing (OLAP) and typically contains summarized historical data.

How does data get into a data warehouse?

Data is transferred into a data warehouse from databases through an ETL (Extract, Transform, Load) process, which extracts the data, transforms it, and loads it into the data warehouse.

What is the purpose of the ETL process in a data warehouse?

The ETL process is used to prepare data for analysis by extracting it from the source, transforming it into a summarized form, and loading it into the data warehouse.

Why is a data warehouse's schema more rigid than a database's?

A data warehouse's schema is more rigid because it requires careful planning ahead for how data will be structured and analyzed, unlike a database which allows for more flexibility and schema changes on the fly.

What is the main difference between the data in a database and a data warehouse?

Data in a database is detailed and current, while data in a data warehouse is summarized and may not always be current, depending on the frequency of the ETL process.

What is a data lake and what types of data can it store?

A data lake is a system designed to capture any type of data, including structured, semi-structured, and unstructured data such as videos, images, documents, and graphs.

Who benefits most from using a data lake?

People working with machine learning and AI benefit the most from using a data lake, as they can utilize the raw, unstructured data for creating models.

Why might a company use all three - databases, data warehouses, and data lakes?

A company might use all three systems to serve different needs: databases for transactional data, data warehouses for analytical reporting, and data lakes for storing large volumes of diverse data types.

How does the performance differ between querying a database and querying a data warehouse?

Databases can be slower for querying large amounts of data and may slow down transaction processing, whereas data warehouses are designed to query large amounts of data quickly without affecting other processes.