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Differences Between a Data Warehouse, Data Lake, and a Database 🧑🎓
What's a Database, and How Does it Work? 🙋♂️
A database is used to store, search and report on structured data from a single source. They are the simplest to create and SQL can be used to query and report on the data. There are both open source and proprietary databases, making it widely accessible to install and start using on premise or on the cloud.
What's a Data Warehouse? 🙋♂️
A data warehouse is used to store large amounts of structured data from multiple sources in a centralized place. Organizations invest in building data warehouses because of its ability to deliver business insights from across the company, and quickly.
What's a Data Lake? 🙋♂️
A data lake stores structured, semi-structured and unstructured data, supporting the ability to store raw data from all sources without the need to process or transform it at that time.
How to Choose? 🙋♂️
1. Structured vs Unstructured vs Semi Structured Data
How many data sources, what format the data comes in, how predictable or consistent or known is the structure ahead of time are important considerations. Data lakes accept unstructured data while data warehouses only accept structured data from multiple sources. Databases perform best when there’s a single source of structured data and have limitations at scale.
2. Data Processing Requirements
Included in the data management strategy is the process of understanding what the data model is and when it needs to be defined. Data lakes offer the flexibility of storing raw data, including all the meta data and a schema can be applied when extracting the data to be analyzed. Databases and Data Warehouses require ETL processes where the raw data is transformed into a pre-determined structure, also known as schema-on-write.
3. Data Storage and Budget Constraints
Big data offers business value to organizations which is hopefully reflected in the budgets for its data management plan. As data continues to increase in volume and velocity, storage costs increase accordingly. Data lakes are the most efficient in costs as it is stored in its raw form where as data warehouses take up much more storage when processing and preparing the data to be stored for analysis. Databases can scale up and down depending on the need.
4. Consider Who is Using the Data
Whether or not the end user is a business analyst, a data scientist or a business operations will determine what makes sense for the organization. If the primary use case is business insights and reporting for the operations team, a data warehouse will meet their needs but at a higher cost to set up and store the data. The data scientist may prefer data lakes because he/she wants to deep dive into new artificial intelligence and machine learning algorithms and appreciate access to a mix of structured and unstructured data. A business analyst might be proficient in SQL and only need to create a trends report on one part of the business and thus, a relational database is best.
5. Technology & Data Ecosystem
Organizations differ on their stance on trusting open source software or proprietary software and the community behind it. Data lakes are popular because of the widespread adoption of Hadoop and the rise in unstructured data from various systems used across the company and real-time data streams. Another aspect of technology to consider is the accessibility and fidelity of updating the system when data sources and structures change. It is more costly to update the relational database and data warehouse whereas changes are simple with a data lake.