Key Differences and Use Cases: Data Warehouse vs. Data Lake
This blog explains the key differences between a data warehouse and a data lake while also providing their use cases.
Data Lakes and Data Warehouses: An Overview
In the contemporary landscape, organizations are bestowed with expanding reservoirs of data. Yet, the challenge lies in navigating through, processing, filtering, and analyzing the raw data to extract tangible advantages. Data Warehouses and data lakes are storage solutions that have different use cases. They are typically used by data engineers, analysts and data scientists. These data storage solutions can be termed as repositories storing vast amounts of data. In a data engineering ecosystem, these typically fall under the category of “Data Sinks”.
Data Warehouses:
These are like storehouses of processed data that suit various business application requirements. Here, the data stays structured and centralized so as to support decision making and analytics. It is the integration of data in data warehouses that makes it easier to facilitate efficient querying and reporting for
BI(business intelligence).
The data in data warehouses have a predefined schema and can be queried using SQL(Structured Query Language). The typical database management systems used are MySQL, PostgreSQL or others.
Data Mart
A data mart is a subset of data warehouse intended for specific business operations and functions and contain prestructured/presummarized forms of data. They may be used by sales, marketing and finance teams to get insights for making decisions.
Data Lakes:
These are repositories that store both structured and unstructured data. They are called “lakes” since they store data from multiple sources irrespective of the format or size. All sorts of real-time analytics, machine learning and decision making can be done using data lakes. This is due to the fact that data lakes consist of a combination of relational and non-relational data which do not have a purpose of their own.
In this case of data lakes, data storage can be more versatile, allowing for various file formats like JSON, Parquet, Avro, etc., and it may be distributed across different storage systems, including cloud-based solutions like Amazon S3 or Azure Data Lake Storage.
Data Lakehouse:
A data lakehouse is a hybrid data storage solution formed by combining a data lake and a data warehouse. It has the flexibility and affordability of data lakes along with the organized search speed and data management functionalities of a data warehouse.
Data lakehouses bring together different data sources and make it easier for everyone in the organization to access and use data(for different engineering requirements). They use affordable cloud storage, like data lakes, for storing data that can be easily scaled up or down. Similar to data lakes, they can handle large volumes of various data types in their raw form. In addition, they have features like structured schemas, data governance, and support for transactions, making them more like data warehouses.
Key Differences between a Data Warehouse and Data Lake
Schema
Data warehouses follow a schema-on-write approach. This means that when data is loaded into the warehouse, a schema must be defined. This schema defines the structure and relationships of the data, optimizing it for efficient querying and analysis. The schema is enforced during the data loading process. Data lakes follow a schema-on-read approach. This means that raw data is ingested without a predefined structure. The structure is applied when the data is read for analysis. This flexibility allows for the storage of diverse data types, including structured, semi-structured, and unstructured data.
Data Processing
The architecture of a data lake enables the efficient management of extensive data volumes produced by organizations, eliminating the need for pre-structuring. This adaptability aligns with the utilization of ELT (Extract, Load, Transform) operations, allowing organizations to extract raw data, load it into the data lake, and perform transformations as needed, ensuring a flexible and scalable approach to data processing. On the other hand, a data warehouse is designed to optimize the efficiency of rapid SQL queries. The implementation of ETL (Extract, Transform, Load) operations further enhances its capabilities, ensuring robust and accelerated data analysis.
Performance
Data lakes are generally more cost-effective for storing large volumes of raw data which includes unstructured and semi-structured data. The query performance of data lakes vary depending on data organization and indexing. With the schema-on-read approach, data lakes are well-suited for big data processing and exploratory analysis. Data warehouses typically involve higher costs especially for large-scale storage and processing. However, the costs may be minimized when query performance and structured data are critical. Their predefined schemas and structures enable quick and efficient execution of SQL queries making them ideal for complex analytical queries and reporting. Data warehouses excel in structured data queries, leveraging predefined schemas for optimized local storage queries. Data lakes provide versatility but may require additional processing for local storage queries, making them suitable for scenarios prioritizing schema flexibility and diverse data types.
Quality of Data
Data warehouses implement robust access controls, encryption, and audit trails to safeguard sensitive information. This high level of security makes them suitable for storing confidential business data and customer information. The data warehouse's structured nature and adherence to a predefined schema contribute to its role as a central and reliable repository of data, often referred to as the "single source of truth" within an organization. Data lakes include certain basic access controls. While it is highly concerning storing sensitive data, data lakes allow for trying out and coming up with new ideas using less important information.
Users & Use Cases
Data warehouses are used by business analysts, data scientists, and decision-makers. These users often engage in complex queries, reporting, and data visualization, leveraging the structured nature and predefined schemas of data warehouses. For example, analysts working in the finance sector want data that they can quickly refer to, perform some calculations/operations, and prepare reports. The same goes for supply-chain analysts who work with inventory data, logistics data, risk and compliance data, etc. Using data warehouses,
their work becomes much easier with quicker operations(selection, sorting, filtering etc.) and increased data accuracy compared to using Excel spreadsheets. On the other hand, data lakes are utilized by data engineers, data scientists, and researchers, who prioritize the ease and versatility of ingesting, storing, and analyzing diverse data types. Typical use cases for data lakes include machine learning, predictive analytics, data discovery, and operational analytics. For example - a company may want to increase ad revenue using user
behavior analytics(for clicking) in order to develop the best possible strategy for advertising. An employee could combine data coming from various channels as well as ad assessors and put that into a data lake for research.