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Data Lake vs. Data Warehouse vs. Database: Data Stores Compared
Successful organizations derive business value from their data. One of the first steps towards a successful big data strategy is choosing the underlying technology for storing, searching, analyzing, and reporting data. Here, we’ll cover common questions—what is a database, a data lake, or a data warehouse? What are the differences between them, and which should you choose?
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What Is a Database?
A database is an organized collection of data that is used to store, search, and report on structured data from a single source. Databases can be broadly categorized into two types: relational and non-relational.
Relational databases (RDBMS) use schemas and are ideal for structured data. They rely on tables to store data and use SQL (Structured Query Language) to manage and query data. Some examples of relational databases include MySQL, PostgreSQL, and Oracle.
Non-relational databases (NoSQL) are designed to handle unstructured or semi-structured data. They do not require a fixed schema and can store data in various formats, such as documents, key-value pairs, graphs, or wide columns. Examples include MongoDB, Cassandra, and Redis.
Databases are simple to create and can be either open source or proprietary, making them widely accessible for installation and use on-premise and in the cloud. They are typically used for structured data, which allows for efficient data storage, retrieval, and analysis.
However, relational databases require schemas and are not suitable for unstructured or semi-structured data. Because of this rigid schema, they are not suited for centralizing data from multiple sources where the raw data varies in format and structure. However, they are popular for data analysis and monolithic applications.
What Is a Data Warehouse?
A data warehouse is a centralized repository that is used to store large amounts of structured data from multiple sources. It enables organizations to consolidate data for advanced analytics and reporting, supports business intelligence, and helps to generate insights that drive decision-making. Creating a data warehouse requires some heavy lifting in the planning and design stage of examining data structures. While the setup can be costly and complex, mostly because of proprietary software and storage, the return on investment is justified by improved data analysis and informed decision-making.
Some popular data warehouses include Amazon Redshift, Google BigQuery, and Snowflake. These platforms provide robust solutions for storing and analyzing large datasets and offer scalability and advanced data management features.
What Is a Data Lake?
Unlike databases and data warehouses, a data lake stores structured, semi-structured, and unstructured data. It supports the ability to store raw data from all sources without the need to process or transform it at the time of ingestion.
In a data lake, data is stored until it is needed. This makes it easy for data scientists and analysts to create new data models to process and transform data. They can analyze data as needed without being constrained by predefined schemas.
Data lakes allow organizations to store diverse data types, including logs, videos, images, social media content, and more, which can be valuable for advanced analytics, machine learning, and big data processing.
Storing data in data lakes is much cheaper than in a data warehouse. Data lakes can store vast amounts of data at a lower cost, which is great for organizations looking to manage large datasets efficiently.
Data lakes are very popular in the modern stack because of their flexibility and costs, but they are not replacements for data warehouses or relational databases. They do not provide the same level of performance, reporting capabilities, or ease of use for business users as data warehouses. Data lakes require robust governance and management practices to ensure data quality, security, and accessibility.
Data Lake vs. Data Warehouse vs. Database: Key Differences
Databases, data lakes, and data warehouses are used by organizations to aggregate, store, and manage data. However, each serves a different purpose and has different types of users and use cases.
Below, we compare databases, data lakes, and data warehouses across a few key attributes:
Data Structure
- Databases: Primarily store structured data with predefined schemas. They are ideal for transactional data and applications requiring frequent read/write operations.
- Data Warehouses: Also store structured data but from multiple sources. They use predefined schemas and are useful for read-heavy operations, analytics, and reporting.
- Data Lakes: Can store structured, semi-structured, and unstructured data. They do not require predefined schemas and can store raw data in various formats.
Purpose and Use Cases
- Databases: Used for day-to-day operations to manage transactional data and applications needing immediate read/write access, like customer databases and inventory systems.
- Data Warehouses: Built for analytical processing, i.e., aggregating data from various sources to generate business insights. Ideal for historical data analysis, business intelligence, and reporting.
- Data Lakes: Serve as a central repository for all types of data, which enables data scientists and analysts to run machine learning models and big data analytics. Suitable for AI/ML applications and exploratory data analysis.
Data Processing
- Databases: Handle real-time data processing with immediate read/write capabilities and ensure transactional consistency and integrity.
- Data Warehouses: Use batch processing to integrate data from multiple sources, transforming it to support complex queries and analytics. ETL (Extract, Transform, Load) processes are common.
- Data Lakes: Allow batch and stream processing.They support ETL and ELT (Extract, Load, Transform) processes, providing flexibility in how and when data is transformed.
Cost and Scalability
- Databases: Generally cost-effective for small to medium-sized applications with moderate data volumes. Scalability can be limited.
- Data Warehouses: Often require significant investment in hardware and software, but provide high scalability for large volumes of structured data. They are optimized for performance and query speed.
- Data Lakes: More cost-effective for storing vast amounts of raw data, including semi-structured and unstructured data. They offer high scalability and support growth in data volume and variety.
Flexibility
- Databases: There is limited flexibility due to rigid schema requirements. It is challenging to adapt to new types of data without significant reengineering.
- Data Warehouses: These are more flexible than databases but rely on predefined schemas. They require careful planning and design to accommodate changes in data sources and structures.
- Data Lakes: Highly flexible, allowing organizations to store data in raw format. They support various data formats and structures, which make it easier to adapt to new data sources and analytical needs.
Databases are mostly useful for structured, transactional data. Data warehouses excel at structured data for analytics and reporting, and data lakes provide the flexibility to handle various data types for advanced analytics and big data applications.
How to Choose? 5 Things to Consider
The key differences in selecting how to house all the data in an organization comes down to these considerations:
1. Structured vs Unstructured vs Semi Structured Data
Consider the types and formats of data your organization deals with. Databases perform best with structured data from a single source and have limitations at scale. Data warehouses can handle structured data from multiple sources. Data lakes, however, can store unstructured, semi-structured, and structured data.
2. Data Processing Requirements
Think about when and how the data needs to be processed in your data management strategy. Data lakes store raw data with all of the metadata, 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 structured format, also known as “schema-on-write.”
3. Data Storage and Budget Constraints
Consider the cost of storage as data volume and velocity increase. Data lakes are cost efficient as they store raw data. Data warehouses, which process and store data for analysis, can incur higher storage costs. Databases can scale up and down depending on the need to balance cost and performance.
4. Consider Who is Using the Data
Identify the primary users of the data. If the primary use case is business insights and reporting for the operations team, a data warehouse will meet the needs of business analysts, but at a higher cost to set up and store the data. The data scientist may prefer data lakes, because of the flexibility to add new artificial intelligence and machine learning algorithms when accessing a mix of structured and unstructured data. A business analyst might be proficient in SQL and need simple trend reports, so a relational database might be sufficient.
5. Technology & Data Ecosystem
Organizations differ on their stance on trusting open source software or proprietary software, and the community behind them. Data lakes are popular because of the widespread adoption of Hadoop and the rise in unstructured data from various systems used across the company (often powered by 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.
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