Quick Summary:
Organizations have evolved from a scenario where they longed for any kind of help with their overwhelming data to a state where they are now spoiled for choice. With Data Warehouses, Data Lakes, and the emergence of Data Lakehouses, businesses find themselves in a good dilemma: choosing the right architecture for their needs.
Through this blog, we explore all the possible options, who it is best suited for, and the core similarities and differentiators of these data architectures. We also distill our expertise and share a verdict on what we believe to be the best choice for businesses with an eye on the future.
When we work with clients who want to improve data management strategies and become truly data-driven organizations, the most common dilemma they face is Data Warehouse vs Data Lakehouse vs Data Lakeface. In our experience of working with clients across the globe for 10+ years, we have found that there is no “one-size-fits-all” approach and a lot goes into selecting and implementing a robust, sustainable, and flexible data ecosystem.
A study by Gartner revealed that 57% of data and analytics leaders invested in modern data warehousing, 46% in data hubs, and 39% in favor of data lakes.
In this blog, we distill our experience and share impactful insights about data warehouse, data lakehouse, and data lake through our expertise in helping businesses establish scalable and future-focused data infrastructure. At the end of this blog, you will have a clear idea of choosing and implementing the right data warehouse, data lakehouse, and data lake based on your unique data management needs and analytical goals.
Data Warehouse vs Data Lakehouse vs Data Lake: A Quick Overview
Data Warehouse:
While the warehouse is a self-explanatory word, for data it is a term used for a repository either on-premise or cloud, that stores structured data from various sources, which can then be used for analytics, reporting, and business intelligence.
Data warehouses (1980s) act as centralized storage units for the exorbitant data that large-scale companies generate daily. Independent data reveals that approx. 54% of organizations globally use data warehouses, and it is expected to grow at a CAGR of 10.7%. Popular data warehouses used worldwide include Google BigQuery, Snowflake, Microsoft Azure Synapse Analytics, IBM Db2 Warehouse, etc.
Data Lake:
While data warehouse services helped organizations deal with data there was one problem, the data had to be structured. As technology grew organizations started generating data in the form of a lot of images, and videos, besides text and this is where Data Lakes (2010) came in.
Data Lake has become a go-to solution for organizations looking for a data storage solution that offers a lot of flexibility and is capable of dealing with a lot of raw and unstructured data, and is expected to touch $17.6 billion by 2026, with a CAGR of 29.9%. It is a solution ideal for the growing needs of your large-scale organization and it is expected that almost 60% of organizations will adopt data lakehouse solutions by 2026.
Data Lakehouse:
While a part of the world steered towards data warehouses and the remaining towards data lakes, an eminent question popped up. What if the best features of both could be combined to clear the bottlenecks? As with any new technological innovation, drive for efficiency, and getting the best of those available, data lakehouses came into the picture.
Data Lakehouse (2017) supersedes data warehouses in the department of storing raw, structured, and unstructured data—a major shift from the traditional rigidity of only structured data finding its way into data warehouses. Data Lakehouses with a CAGR of 22.9%, add a lot of flexibility in terms of data types and hence solve the rigidity that comes with data warehouses. Additionally, data lakehouse came up as a low-cost option that combined the exceptional querying and performance-optimized features of the data warehouse and the flexibility of data flakes.
Confused Between Data Lake, Data Warehouse, or Data Lakehouse?
Core Differences between Data Warehouse, Data Lakehouse, and Data Lake
- Data Warehouse excels at optimized query performance and enterprise-grade reliability and is known for its super-speed in delivering analytics. Solutions from Snowflake, Oracle, and IBM power data warehouses and they find the most use cases in financial analytics, sales performance, and business intelligence.
- Data Lake is adopted for its strength of providing flexibility and doing the difficult task of supporting raw data, making it perfect for diverse data storage needs including on-premise data lakes. However, the disadvantage of data lake includes potential management challenges, sometimes leading to a ‘data swamp.’ Giants like Amazon S3, Azure Data Lake Storage, and Google Cloud Storage power data lakes are best suited for storing raw data for ML models and AI initiatives.
- Data Lakehouse is a perfect combination and is the ultimate mix of both data warehouses and data lakes. The biggest advantage is that it offers a unified platform with a strong data pipeline framework, combining the scalability of lakes with the performance of warehouses. Emerging players in this field like Databricks are integrating lake and warehouse functionality-backed data lakehouses, which are perfectly apt for modern data architecture that needs real-time analytics and AI.
Data Warehouse vs Data Lake vs Data Lakehouse: Who Each Product is Best Suited For
| Data Warehouse | Data Lake | Data Lakehouse |
| Works best for traditional BI, dashboards, and where static reports needed | Mainly for use cases involving AI/ML model training and exploration-based data analysis | For dynamic analytics that combines raw and processed data |
| Best suited for industries that rely heavily on highly structured, transactional data | Apt for businesses that need to handle video, images, and logs in raw formats | Excellent for companies that need real-time analytics, large-scale data storage, coupled with predictive insights |
| Ex: Finance firms generating regulatory reports | Ex: Media companies storing unprocessed video streams | Ex: E-commerce companies that need to integrate clickstream data/product sales for customer behavior insights |
Similarities between Data Warehouse, Data Lakehouse, and Data Lake
- The way Data Lake stores and manages data is entirely different from data warehouses and data lakes. Data lakes score high in the data security area, and because of the data storage security in cloud computing, data security is excellent.
- Data Lakehouse on the other hand is all about addressing scalability in cloud environments. From cloud data solutions to on-premises options, enterprises trust Data Lakes for their capacity to restore and back up cloud data storage along with undettering reliability.
- Cloud Data Warehouse Guide has an unmatched performance in structured data analytics and this helps businesses to unlock the value of their data.
Positioning: Data Warehouse vs Data Lakehouse vs Data Lake
| Product | Best For | Strength |
| Data Warehouse | Organizations/Businesses that rely on traditional BI, dashboards, and reporting | Fast query performance, reliability, and structured data processing |
| Data Lake | Excellent for data scientists and teams working on AI/ML models and big data storage | Raw data storage for all formats, but lacks integrated query performance |
| Data Lakehouse | A perfect fit for enterprises that need real-time analytics, big data processing, and flexibility | Unified storage and analytics, and capability to handle both structured and unstructured data |
Head-to-Head Data Warehouse vs Data Lake vs Data Lakehouse Comparison
1. Structured Data Management
Winner: Data Warehouse
When you have a data system that is built on the power of data warehouses it means that you have with you the advantage of schema-on-write which ensures data integrity, speed, and reliability for analytics. This is especially true for departments like finance which relies heavily on a data warehouse for generating quarterly reports and real-time KPI dashboards. The fast query performance of Data Warehouses enables decision-makers to act on insights immediately and guarantees accuracy and performance.
2. Flexibility of Data Storage
Winner: Data Lake
As data lakes use schema-on-read, they enable organizations to store their raw data in any format—structured, semi-structured, or unstructured making it a perfect fit. Data Lake solutions are a must-have for organizations that need to ingest large datasets without upfront processing like video, images, and sensor data for ML models. It is an exceptional way for data scientists to work with raw, unprocessed data across various formats, giving them an edge through its unparalleled versatility.
3. Unified Analytics and Real-Time Processing
Winner: Data Lakehouse
Finally, when it comes to Data Lakehouse architecture, it combines schema-on-read flexibility with warehouse-style performance for querying structured and unstructured data using SQL, making it a sure-shot choice for businesses that want more by investing comparatively less. If your organization needs to integrate data from varied departments like marketing and customer data, the modern data architecture allows a flawless blending of structured transaction data with unstructured social media insights, reducing data duplication and improving time-to-insight. Data Lakehouses are perfect for businesses seeking a single platform for real-time data processing and long-term storage, due to their low cost, and high efficiency.
Summary of Benefits: Data Warehouse vs Data Lakehouse vs Data Lake
| Feature | Data Warehouse | Data Lake | Data Lakehouse |
| Structured Data Analytics | Superior | Adequate | High Performance |
| Flexibility of Data Formats | Limited | Superior | Superior |
| Real-Time Query Performance | High | Requires additional tools | Superior |
| Cost Efficiency | Higher cost | Lower initial cost, high management cost | Balanced for performance and flexibility |
Accelerate Your Business With the Power of A Data Architecture That’s a Perfect Fit for Your Enterprise
Quick Verdict: Data Lakehouse vs Data Warehouse vs Data Lake
So if all three have what it takes how to make that foolproof decision between Data Warehouse vs Data Lakehouse vs Data Lake?
The answer is simple yet strategic. If you’re running a business focused on traditional analytics and reporting, where data is in a repetitive form without complexities then choose a Data Warehouse. Data warehouses win a lot of votes for the way they deal with structured data and some very complex SQL queries. Data Warehouses empower your business by offering reliability for business intelligence services or BI applications, a much-needed asset for any business.
Choose Data Lake if you are dealing with overwhelming levels of unstructured data for AI/ML. The only thing that you might feel like considering is the lack of performance optimization that leads to slower analytics if there are no additional frameworks in place.
However, if you are a growing enterprise and seek a modern data warehouse that combines the best of both worlds, Data Lakehouse is the clear winner. Tech giants Databricks and Snowflake brought this hybrid model to solve the pain points associated with data lakes and to reap the benefits of data warehouses, for scalable, high-performance analytics.
Transform your Data into a Strategic Asset with our Expert Data Consulting Solutions
Why Do the Scales Tilt in Favour of Data Lakehouse?
A survey report from Dremio shows the rapid scale at which Data Lakehouses are being adopted. 70% of the professionals surveyed affirmed that they expect more than half of their analytics to be on the lakehouse within the immediate 3 years.
Image Credits: Dremio
Data Lakehouse has emerged as the preferred mode, as it eliminates the need for maintaining separate systems for raw data storage and analytics, thus reducing cost and complexity, and adding ease and cost-effectiveness for the organization. This shift is due to the potential that data lakehouse demonstrates in terms of the power of unified data management with ACID transactions and real-time data processing, upending the way businesses approach big data analytics.
“Lakehouses redeem the failures of some data lakes. That’s how we got here. People couldn’t get value from the lake”
– Adam Rosenthal, vice president and analyst at Gartner (Source)
For instance, an organization in the retail segment that needs sales forecasting or customer sentiment analysis can easily integrate structured point-of-sale data with unstructured social media feeds—WITHOUT moving data between systems!
Or a financial firm can store tons of historical trading data and use real-time queries for risk modeling and can gain hugely from both the scalable storage and rapid analysis that Data Lakehouses offer.
Data Lakehouse: The Future of Unified Data Architecture
Data Lakehouses are the best thing to happen for organizations that have a disparate enterprise data warehouse architecture diagram where data has to be duplicated in separate lakes and warehouses.
The high-performance analytical capabilities of data lakehouses that are optimized for SQL-like queries on massive datasets are a complete winner and the flexibility offered for different types of data in one system stands as a major advantage.
The cherry on the cake is the lower Total Cost of Ownership (TCO) because fewer systems mean fewer maintenance needs, negligible chances of data duplication, and an ecosystem with simplified data governance. No wonder that it is adopted by top companies across tech and finance, including Netflix and Goldman Sachs.
Wrapping Up
At X-Byte Analytics our teams of consultants have helped businesses get the right data storage and analytics solutions specific to the needs of evolving business needs. Our consultant and team of data analysts provide a custom data solution that is aligned with the ultimate business objective and drives growth.
If you are still on the fence about the right data architecture for your business and want more clarity on the one that will transform the way you handle data with a future-centric vision, we can help you.
Get in touch with our team for a call and transform into a scalable data-driven organization.
