Relational Databases or RDBMS played a key role in making data management mainstream. They are good with highly structured, low-quantity data of the pre-internet era. The advent of the internet coincided with the ambitions of large organizations to incorporate a 360-degree view of their customer database. This led to a new type of storage destination known as Data Warehouses. Today, even this storage destination has a smaller subset, popularly known as Data Lakes. When it comes to the field of Database Storage, the Data Warehouse vs Data Lake question is a relatively tough choice.
Even today very few people understand the differences between these 2 types of storage. Although Data Warehouses are good at handling structured Big Data, companies quickly realized that the Data Warehouses might not cater to the rising demand for insights into unstructured data. The 21st century witnessed a deluge of data collection by organizations not only from internal sources but also from public repositories. They needed a technology that could complement the capabilities of Data Warehouses, an extension that can facilitate the immense unstructured aspect of Big Data. As a result, Data Lakes came into existence. You can also check our comprehensive guide on data lake architecture.
This article provides a comprehensive guide to help you answer the Data Warehouse vs Data Lake question. It also provides a brief introduction to both storage techniques. Given the overlapping nature of the features of these technologies, it might be tricky to pick the right choice because even today. Read along to find out how you can easily decide between a Data Warehouse and a Data Lake.
Table of Contents
Prerequisites
- Knowledge of Databases
- A Good Understanding of the Differences between Structured and Unstructured Data
What are Data Warehouses?
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A Data Warehouse is a repository that only stores the pre-processed data collected either from a Data Lake or multiple Databases. ETL (Extract, Transform, and Load) processes are used to organize data in multi-dimensional structures that can expedite Analytics workflows with Data Warehouses. Data stored in a Data Warehouse is ready to use by Business Intelligence professionals and Data Analysts for generating reports and building dashboards.
You would also love to read an article about Data Warehousing and Data Mining.
What is Data Lakes?
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A Data Lake is a repository that houses a colossal amount of information in its native form. It can store structured, semi-structured, and unstructured data without any fixed limits on the data size. Data Lakes are the foundation for any Data-Driven business that relies on Big Data for uncovering insights. However, data in a Data Lake gets stored at the leaf level in an untransformed or nearly untransformed state, thereby requiring further processing to support Analytics workflows.
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Factors that Drive the Data Warehouse vs Data Lake Decision
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Now that you have a basic idea of both storage techniques, let us attempt to answer the Data Warehouse vs Data Lake question. There is no one-size-fits-all answer here and the decision has to be taken based on the business requirements, budget, and parameters listed below. The following are the key factors that drive the Data Warehouse Data Lake comparison:
1) Data Warehouse vs Data Lake: Data Shape
Data Lakes contain information in natural and unprocessed formats. This data can either be structured or unstructured. Whereas, Data Warehouses contain structured and semi-structured data that have been cleaned and processed, ready for strategic analysis based on predefined business needs.
2) Data Warehouse vs Data Lake: Data Quality
Since data stored in Data Lakes is in its native form, the quality of the data is low. Data Lakes can consist of key-value file formats, sequence files (.seq) that consist of binary key-value pairs, audio, video, and documents. These raw and unprocessed data are mostly unfit for Data Analysis and Machine Learning models.
But by using ETL techniques, Data Warehouses achieve high-quality structured or semi-structured data through aggregation, generalization, and normalization. The data elements in the Data Warehouse comprise quantitative metrics and the attributes that describe them, making them suitable for generating insights immediately.
Some Data Observability vendors support data quality monitoring for both data warehouses and lakes.
3) Data Warehouse vs Data Lake: Schema
Data Lakes support various formats of data, but this aggravated data normally does not have any schema. A schema can be viewed as a structured organization of various tables and their fields, data types, constraints, and relationships. However, in Data Lakes, the schema is defined, developed, and tailored as per user needs at the time of reading. In other words, Data Lakes follow the Schema-on-Read approach. This approach offers shape and structure to the data only when required.
Unlike Data Lakes, Data Warehouses leverage the Schema-on-Write approach. In this, the schema is defined before storing data using upfront Data Modeling. Identifying all the columns and rows in advance is crucial to ensuring that structured data is placed exactly where it needs to be.
As a result, data stored in the Data Warehouse already possesses a predefined structure and schema. Since the data is organized with a proper schema, users can easily navigate and interpret the data in Warehouses. However, users must be aware of the schema definition used while preparing Data Visualization and reporting. Not following the schema can result in garnering inaccurate insights.
4) Data Warehouse vs Data Lake: Modelling
Data Lakes tend to have a flatter model. This means there may be various information about an entity but may not be related to one another. Therefore, any changes made in one would not reflect in the other. For example, consider a business profile record. The record may contain information about sales made, current and finished projects, market projections, business tools developed, etc. While each of this information may be important to the business owner, they may not be linked.
For Data Warehouses, several Data Modeling techniques like dimensional and relational are implemented for streamlining the data analysis processes. As a result, it is common for Data Warehouses to be constructed with several tables that can be joined together to provide insights.
5) Data Warehouse vs Data Lake: Users
Data Scientists generally use Data Lakes to extract unstructured data to identify patterns and useful information that can be used for enhancing Artificial Intelligence-based products and services. Meanwhile, Data Warehouses are primarily used by Business Analysts to create visualizations and reports. It is preferred by businesses looking for well-structured and purpose-built infrastructure for Data Analytics. Even non-experts can leverage data for making informed decisions with the help of drag-and-drop data analytics tools.
Niches like Healthcare, Marketing, and Education deal with data where the volume of structured data is very low. Here Data Lake will be a perfect fit. However, this does not mean that these organizations only used Data Lakes. Data Warehouses are still widely leveraged in these organizations for analysis with structured data.
6) Data Warehouse vs Data Lake: Pricing
Data Lakes lie on the low-cost end of the pricing spectrum because it is not performance-centric. The idea behind deploying a Data Lake is to store a colossal amount of data whose purpose is yet to be determined. Data in Data Lakes can occupy the space for years before it is leveraged for Machine Learning or Data Warehousing. As a result, Data Lakes are optimized to reduce the cost of storing raw information.
On the other hand, Data Warehouses are designed to support multiple Analytics needs within organizations. They have to support intensive performance for facilitating quality data for analytics and insights generation. This makes Data Warehouses costlier than Data Lakes. However, the Return On Investment from Data Warehouses can be immense if organizations devise a perfect plan. Several companies deploy a manual workforce for ETL practices, increasing the cost of maintaining Data Warehouses. But, with no-code ETL solutions, organizations can streamline the entire process of Data Warehousing to reduce operational costs.
7) Data Warehouse vs Data Lake: Security
The Database admins build models that grant Data Warehouse permission to only authorized personnel. These security models also protect Databases, Schemas from any rogue changes to avoid breakdowns in the data flow. Security and administration of Data Warehouses are key for organizations to comply with several Data Privacy rules from across the world. The same cannot be said about a Data Lake.
Many users, applications, or even third parties require access to Data Lakes, thereby compromising security. On the brighter side, as the importance of compliance requirements for every kind of data is increasing, better security measures might be enforced on Data Lake infrastructures.
8) Data Warehouse vs Data Lake: Accessibility
The accessibility of Data Repositories is measured by how well they can be used as a whole, not by the data within. The lack of schema makes Data Lake more accessible and flexible for obtaining data for complex analysis. However, only experts can pull Raw Data and pre-process it. Nevertheless, it supports a wide range of Data Analysis that goes beyond the usual scope of organizations’ operational needs. Unlike Data Lakes, the Data Architecture in Data Warehouses is highly structured, making it more complicated and requiring more cost to make any changes.
9) Data Warehouse vs Data Lake: Purpose (undetermined vs in-use)
Raw data is processed with a specific purpose in mind because it takes time and effort for it. So, companies always try to preprocess data that is needed for the purpose. This data is then stored in the Data Warehouse. This means that storage space is not wasted on data that may never be used.
The purpose of data chunks stored in the Data Lake is not fixed because the raw data loaded from multiple sources is used by companies in the future but not necessarily with a solid intent always. Data Lake works as a storage pool, and if in the future company needs any piece of data, it can load it from Data Lake.
10) Data lake vs data warehouse: which is right for me? (Use Cases)
Organizations need both Data Lakes for storing huge volumes of historical, structured, and unstructured data for Machine Learning and Analysis. Whereas, Data Warehouse helps them generate insights from the well-structured data and run analytics on it.
Healthcare: data lakes store unstructured information
Healthcare data is mostly unstructured as it contains physicians’ notes, clinical data, etc. Though, it needs analysis but using Data Warehouse for huge volumes of highly unstructured is not an ideal fit for the industry,
Data Lakes on the other hand are designed to handle unstructured data and are widely used in the Healthcare industry.
Education: data lakes offer flexible solutions
The education sector is highly influenced by Big Data and Analytics in the last few years. The majority of the educational data includes, including students’ grades, attendance, interests, etc. has reformed the educational sector in knowing more about the key factors to increase student performance, and help predict the issues before it occurs. Educational data is mostly unstructured and Data Lakes are the best option to handle such data and streamline the billing, fundraising, and more activities.
Finance: data warehouses appeal to the masses
Finance is one of the biggest industries that make use of data better than anyone. Finance data is mostly structured and that is why Data Warehouses are the best choice to analyze Finance data and generate valuable insights from it.
Data Warehouse is a boom for financial services as it provides high-accuracy predictions, trends, etc.
Transportation: data lakes help make predictions
The transportation industry usually needs insights, especially for supply chain management. The Data Lakes becomes the better choice for the transportation industry as it helps companies make a prediction from the flexible data it has. It has many cost-cutting benefits realized by analyzing data from forms within the transport pipeline.
Key Difference between Data Warehouse and Data Lake
| Parameter | Data Warehouse | Data Lake |
| Data Structure | Processed Data | Raw Data |
| Users | Business Professionals | Data Scientists |
| Accessibility | Complicated and costly to make changes | Highly accessible and quick to update |
| Data Retention | The Data warehouse retains only useful data | Data lakes retain all data |
| Data Types | Non-traditional data sources such as web server logs, sensor data, social network activity, text, and images are largely ignored. | Data Lakes Support All Data Types |
Conclusion
This article gave a comprehensive analysis of the 2 popular Database Storages in the market today: Data Warehouses and Data Lakes. It also provided a brief overview of both techniques. It also gave the parameters to judge each of the storage techniques. Overall, the Data Warehouse vs Data Lake choice solely depends on the goal of the company and the resources it has. Here’s a summary of the differences:
Today, both Data Lakes and Data Warehouses are a major part of Data Storage and Processing Needs. With the emergence of companies that want to benefit from Data Storage facilities, it is better to incorporate both Data Lakes and Data Warehouses for accelerating Data Analytics. While a Data Lake would be ideal for storing information continuously, a Data Warehouse can simplify access to quality data for both experts and non-experts. However, if an organization is leveraging only structured data, a Data Warehouse would be the most beneficial option. You can also read our other article about Data Warehouse vs Database.
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