Data Lake vs Data Warehouse: What is Right for your Business?
17 Feb 2022
Successful organizations are the ones that derive maximum value from their data. Though storage of this information is a big deal. And one of the important steps in this process is to first opt for a viable big data strategy. Data warehouses and data lakes represent two of the leading solutions for enterprise data management.
They are both widely used for storing big data, but they are not interchangeable terms. The distinction is important because they serve different purposes and require different sets of eyes to be properly optimized. This Q&A article aims to provide insights around the differences between data lake and data warehouse and how they complement each other.
Que: What is a Data Lake?
Ans: A storage repository that can store a large amount of structured, semi-structured & unstructured data. This information is in a raw format from all sources without the need to process or transform it at that time. It is a preferred choice of larger enterprises. Though simply put, it can be like a large container similar to real lakes. Imagine how lakes have multiple tributaries coming in; similarly, with a data lake, it has structured & unstructured data as well as logs flowing through in real-time.
Que: What is a Data Warehouse and What are its Types?
Ans: As a single repository for structured data from diverse sources, it stores data that has already been cleaned and categorized in complex tables. Popular with mid-and large-size businesses for sharing information across the team- or department-siloed databases. They help organizations become more efficient. And, in many cases, they guide management decisions— often popularly referred to as “data-driven” decisions.
There are primarily three types and each has its role to play in supporting the needs of businesses.
- Enterprise Data Warehouse: Acting as a centralized warehouse, it helps with making effective decisions & provides a standardized framework for data organization.
- Operational Data Store: It is utilized when other types of data warehouses are unable to meet the needs of a business’s compliance requirements. Further, it makes data available in real-time & helps with corporate data management.
- Data Mart: It is tailored to the needs of various departments like accounting, sales, marketing, or finance.
Que: What is the Difference between Data Lake and Data Warehouse?
Ans: Now that you’ve gotten a basic understanding of both, let’s look at the key differentiators.
| Data Lake | Data Warehouse | |
| Data Processing | ELT: First load then transform & shape | ETL: First create schema then transform data to enable it for loading |
| Agility | Flexible and useful for MVP & PoC | Requires more analysis & modeling |
| Efficiency in Working with | Non-relational data | Relational data |
| Analytics | Suited for one-off or ad-hoc reports and for ML model training | Easy to create recurring reports & suited for subscriptions |
| Users | Best for data scientists but has limited scope for multiple queries | Can handle multiple queries & users and easy to use for non-advanced users |
| Data Volume & Performance | Efficient for processing large volumes of data column-wise | Not as efficient as a data lake to process large volumes, but more efficient for operations on rows |
| Security | Based on file permissions or folder access & can connect to active directory | Has complex features like column-level security, row-level security, dynamic data masking & fine-grained control |
| Support for 3rd party tools | Deploying on-premises has limitations, but cloud deployment can help harness tools for analytics, IoT, machine learning & artificial intelligence | Robust ecosystem of well-tested tools like BI, monitoring & tuning |
| Data Retention | Can store plenty of archival & historical data | Most helpful to store data that is utilized for reporting |
Data lake uses the ELT (Extract Load Transform) procedure – the data is processed after it is loaded into a data lake. The data warehouse uses the ETL (Extract Transform Load) procedure – the data is transformed and then loaded into the data storage.
Que: What are the Industry Use Cases of Data Warehouse vs Data Lake?
Ans:
| Data Lake | Data Warehouse |
| Healthcare: With the large amounts of unstructured data that healthcare institutions deal with, there is a need for real-time insights. A data lake can provide access to both structured and unstructured data that can benefit companies to a great extent. | Hospitality Industry: Can build crucial insights that help with the development of advertisements & promotional campaigns based on customer preferences. They are also used for day-to-day needs customer management & advanced BI reporting to understand the overall company’s growth. |
| Transportation: Since data lakes can make forecasts and predictions, transportation companies can harness the potential of crucial insights to improve operations & reduce overall costs. | Banking and Finance: A data warehouse is best suited for sectors like BFSI to help run periodic reports about the performance of funds & the growth of assets. |
| Oil & Gas: Historical data is vital for exploration, and thus, can be used to optimized for directional drilling, minimize unexpected downtime, lower operating expenses, improve safety, and stay compliant with regulatory requirements. | Investment & Insurance: Helps analyze customer and market trends and other data patterns. With forex and stock markets are two major sub-sectors where data warehouses act as a single point difference that leads to massive losses across the board with focus on real-time data streaming. |
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Que: Which One to Choose for Business - Data Lake and Data Warehouse?
Ans: Data lakes and data warehouses can function seamlessly together to serve as a cost-effective solution for businesses. Though everything boils down to the type of data you are dealing with and its sources. There is certainly a rise in the need to derive exploratory insights from data lakes & use them across the organization with the help of a data warehouse. For instance, if a business wants to improve cross-selling and needs to understand the criteria that match a specific set of customers, they can run multiple queries in the data lake to truly understand what those criteria may be. Once the business gets the required information, they can send those queries for the next level of insights through a data warehouse.
In summary – Why Choose?
| Data Lake | Data Warehouse |
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How Rishabh Can Help Build a Data Warehouse or Data Lake?
We enable global enterprises to overcome the data lake vs data warehouse debate. And, depending on customer’s needs, we help harness the power of big data that is instrumental in future growth. So, whether you’re deciding between either of the two, go through the above categories to assess what best fits your use case.
Rishabh’s Experience – Our Success Story
A US-based hospitality giant that we closely worked with wanted to integrate data from various business apps like RMS, Cognito Forms, Opera & more onto a single platform. By carefully analyzing their business, we discovered that there was:
- No proper system to monitor the property’s KPIs
- Siloed data that affected data redundancy & reliability
- Lack of custom BI reports and understanding of business’s changing requirements
Upon careful analysis of the business, we helped develop a cloud-based data warehouse solution that offered comprehensive data management & visualization. The solution helped with:
- 50% increase in workflow efficiency
- 99% accuracy of business understanding available on-the-go
- Better decision making due to visualization & reporting
Our software development teams help build data management solutions for businesses across fintech, retail, telecom, media, healthcare & other industries.
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