Data Lakehouse came to existence as data warehouses and data lakes were becoming less capable of meeting today’s sophisticated demands. As businesses are handling ever increasing volumes of data, the traditional data architectures were becoming incapable of handling today’s requirements. Data Lakehouse is a combination of data lakes with the performance and governance of data warehouse.
According to a report from IDC, global data creation is expected to reach 181 zettabytes by 2025, up from 64.2 zettabytes in 2020. This explosive growth in data is driven by the increasing reliance on digital tools, cloud computing, and emerging technologies like artificial intelligence (AI) and machine learning (ML) (Ref: https://www.red-gate.com/blog/database-development/whats-the-real-story-behind-the-explosive-growth-of-data)
Imagine data lakehouse – it is like having both a well-organized filing cabinet (a data warehouse) and a giant box where you throw everything else that doesn’t fit neatly (a data lake) but in the same place. It’s like managing the photos on your phone where some photos are neatly organised and are in folders for easy access but some others are just sitting there randomly, which can’t be deleted but is not required in urgent situations. The photos in folders are structured data, and random pictures are unstructured data. The role of a data lakehouse is to keep both these in one place, and it’s easy to find what you need, whether it’s organised or random.
It’s the same for businesses. Lakehouse can be used to store and access all their data like various reports or random updates, which don’t necessarily belong to a report. This gives businesses some kind of flexibility to store and analyse data and use it for machine learning without moving it between different systems.
According to a study from Databricks and Fivetran, 65% of surveyed organizations have already implemented a data lakehouse for their analytics needs, with another 42% of those who haven’t done it planning to adopt this data management model over the next 12-24 months.
This is happening because companies are in need of a simple seamless system that can handle all their data and give them instant access to data analysis and AI.
Powering Advanced Analytics with Unified Data
Today, for businesses to succeed, they completely depend on AI, ML and real-time analytics. Data lakehouse provides the right scenario for analytics by eliminating the silos that prevail in traditional data architecture. Instead of dividing the structured data into a warehouse and the unstructured data into a lake, it brings it all together in the lakehouse.
When there are diverse datasets like customer interaction logs, employee interaction logs, IoT sensor data and financial transactions etc., it becomes easier to handle with data lakehouse. With centralized data storage and access, it allows organizations to gain real-time insights from various domains like from predictive maintenance in manufacturing to personalized customer experiences in retail. They increase the ability to perform multifaceted analytics.
Accelerating Machine Learning and AI
The importance of data lakehouses lies in its ability of advanced AI and ML initiatives. Machine learning models require high volume, high-quality amounts of data in order to function efficiently. Instead of pulling data from multiple silos, it is easy for ML engineers to train, validate, and deploy their models with a single source of truth. Organizations using this architecture will see an increase in the accuracy of the models by about 40% compared to traditional data storage. This is because real-time data will be introduced faster into training models for two reasons – responsive and adaptive algorithms.
Data lakehouses offer a scalable and flexible solution as generative AI and reinforcement learning continue to advance, thus helping manage data complexity and volume. Beyond the structured data capabilities, they can store unstructured data streams in high volumes from every sector of AI applications – be it from natural language processing to computer vision.
The Future: What to Expect from Data Lakehouses
The data lakehouse trend does not seem to be slowing down anytime soon. The volume of global data will only increase, reaching 221 zettabytes by 2030 (ref: https://m.digitalisationworld.com/blogs/58065/data-centres-the-good-they-do).
This means that the role of lake houses will increase in supporting the execution of advanced analytics, AI, and ML.
Another future trend will be the integration of automation capabilities in data quality management and self-healing data pipelines within lakehouse platforms, reducing human intervention and allowing businesses to focus on delivering insights rather than managing the infrastructure.
Adding to this will be the multi-cloud and hybrid-cloud data lakehouse, where businesses will be able to perform in multi public and private clouds without compromising performance or governance. Data lakehouses are becoming the foundation for tomorrow’s enterprise: a family of data architectures that target the advanced analytics and machine learning capabilities to power innovation. The embracing of the lakehouse model by organisations means increased data-driven innovation, with data becoming strong and powerful asset that is capable of pushing competitive advantage.
CRG Solutions has been delivering expert guidance and leading solutions to help improve business management and performance. We are a group of business, financial and technology experts helping leaders transform organizations. We have one goal: to improve enterprise performance through digital transformation of the enterprise through Data and Predictive Analytics, Collaboration and Automation.
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