With the fast development of modern data storage solutions, two popular options have emerged: data warehouses and data lakes. While both store and manage large volumes of data, they differ in terms of structure, design, and use cases. In this article, we explore differences between the two storage solutions; as well as the scenarios in which one may be better than the other.
What is a data warehouse and why you should use it?
A data warehouse is a large, centralized database designed to store, manage, and analyze large amounts of structured historical data from various sources. The data warehouse is built for fast querying, reporting, and data analysis.
The data in a warehouse is organized in a predefined schema, which outlines the structure and relationships between different elements. Some may confuse a data warehouse and database, as the two storage types have similar functions. The key thing to understand is that a database is ideal for capturing real-time data; while a data warehouse is typically used to store both current and historical data from many sources.
Below are the top 3 reasons why companies use data warehouses:
- Single source of truth. By consolidating data from multiple sources into a single, unified structure, data warehouses help to improve data quality, accuracy, and consistency across an organization.
- Better analytics. Data warehouses enable companies to store, access, and analyze large volumes of historical data. Warehouses support the online analytical processing (OLAP) system. OLAP is a technology that can query numerous records and perform complex analytical calculations. In other words, a data warehouse is optimized for advanced analytics.
- Increased efficiency and performance. Data warehouses streamline the process of data extraction, transformation, and loading (ETL). This helps to save time, reduce errors, and improve productivity. Furthermore, a data warehouse is designed to handle large volumes of data and are optimized for query performance.
Data Lake and its key advantages
A data lake is a large, scalable storage repository that stores raw, unprocessed data in its native format, regardless of whether it’s structured, semi-structured, or unstructured. A data lake is designed to handle massive amounts of data and support various types of analytics, such as machine learning, big data processing, and real-time analytics.
The ability to store and manage any type of data provides flexibility. This can help companies to adapt to changing circumstances and their data requirements. For example, when using a data warehouse, you need to transform data before storing. With data lakes, you can store raw data and define a schema later when data is read for analysis. This saves time and resources.
Another advantage of a data lake is cost-effectiveness. Typically, data lakes are low-cost, scalable storage solutions that make them more affordable than traditional data warehouses.
When to use a data lake instead of a data warehouse
Based on a company’s needs and the quickly changing data landscape, you might need to start using a data lake. Here are 3 reasons why a data lake might be a better choice than a warehouse:
- Diverse data types. If you work with various data types (structured, semi-structured, and unstructured), a data lake can store and manage them all in their native formats. Think of social media analytics in which companies need to collect and process various data types. These can include user profiles, social media posts, and various media content.
- Quick data ingestion. Data lakes can ingest data quickly from multiple sources, making them ideal for organizations that need to analyze real-time or near-real-time data. One example could be a company that collects and analyzes data from multiple sensors, such as traffic cameras or air quality detectors. By putting all this data into a data lake, you can see and respond to changing conditions more efficiently.
- Advanced analytics and machine learning. Data lakes are designed for advanced analytics and machine learning software. Data scientists love data lakes, because they allow them to access raw data and perform exploratory analysis without the constraints of a predefined schema. This access to raw, unprocessed data can help to find new patterns, trends, and help to train machine learning models.
Data lake is no magic pond
As good as it sounds, a data lake is not a solution suitable for all data storage requirements. There are situations when using a data lake is not the best option; and instead, a data warehouse should be considered.
First, if your organization primarily deals with structured data and has well-defined reporting and analytics requirements, a data warehouse is a better option. This is because data warehouses are specifically designed to handle structured data and offer a predefined schema. This results in a better querying and analysis process.
One example of this could be a retail store that needs to analyze sales data, inventory levels, and customer information. The store might find a data warehouse more suitable, because their datasets are structured, and reporting requirements are well-defined.
Second, companies with strict data governance and security requirements find a data warehouse more suitable, as it has better control of access and usage. A structured environment makes it easier to implement data governance policies, track data lineage, and ensure data consistency.
Financial companies and government institutions that must comply with strict regulations, such as GDPR or HIPAA, would prefer a data warehouse. The structured nature of a data warehouse simplifies the process of implementing data governance policies. It also ensures compliance with regulatory requirements, and protecting sensitive information.
When choosing between a data lake and data warehouse depends on your company’s specific needs, the type of data you handle, and analytics requirements. Data warehouses are best for managing structured data, performing querying and analysis of historical data. They are a suitable choice for organizations with well-defined analytics needs and stringent data governance and security requirements.
Data lakes, on the other hand, offer flexibility and scalability. They are built for various data types, support advanced analytics and machine learning applications. Data lakes are ideal for companies that work with real-time or near-real-time data or those that require the adaptability to handle evolving data landscapes.