The data storage and analytics market has witnessed a transformative journey, evolving from the structured confines of data warehousing to the considerable, uncharted territories of data lakes. With the evolution of records storage and control, a unique concept emerged, bridging the distance between data lakes and data warehouses – the "data lakehouse."

As IoT infrastructure expands each day, so do the complexities associated with dealing with the volumes of data that require storage and classification. Let’s go through how the latest data storage approach changes the IoT industry analytics and applied along with other solutions.

The Main Concepts: Data Warehouse, Data Lake, Data Lakehouse 

Originally, data warehousing implied a structured data storage system for specific enterprise intelligence and reporting needs. At the same time, businesses realized the value of unstructured data – raw and messy pieces like visuals or videos, which often constitutes major part of the enterprise data. It holds the insights, such as those hidden within years of customer email interactions or extensive production line video records. Unfortunately, it doesn't align with the structured approach offered by data warehouses. As a result, data lakes, which offer a straightforward way to store data in its raw, unprocessed state, emerged. While unquestionably powerful as data storage tools, data lakes also present specific challenges, including concerns about data governance, privacy, technical complexities, and the absence of data indexing or structure. 

According to Gartner's perspective, there is a shift toward the convergence of data warehouses and data lakes, resulting in a unified solution known as the data lakehouse. It integrates the functionalities of both, with the primary goal to improve overall analytics agility while decreasing data redundancy, simplifying the data architecture, and providing a consistent semantic view for all analytical data. Much like data lakes, data lakehouses store both structured and unstructured data, eliminating the need for separate data warehouse and data lake infrastructures. In scenarios where both are employed, data in the warehouse typically fuels business intelligence (BI) analytics, while data in the lake serves data science purposes, which may encompass AI, for instance machine learning, and storage for future yet-to-be-defined use cases. 

Learn more about the difference between the three data architectures in IDC Perspective: Data Warehouses, Lakes, and Lakehouses.

 

Which Data Storage to Choose and Who Is It For? 

A significant segment of data lakehouse users comprises organizations seeking to progress in their analytics journey, transitioning from basic business intelligence (BI) to the realm of artificial intelligence (AI). For instance, within the context of a Smart City, during the BI phase, a city government may deploy IoT sensors to collect traffic and environmental data for fundamental reporting and analysis. As they advance, the integration of AI allows for dynamic traffic signal optimization, congestion prediction, and improved urban planning through the analysis of data sourced from various sensors and channels. 

Comparing Data Storages by Gartner Data & Analytics Summit 2023  

Simultaneously, the selection between a data lake, data warehouse, data lakehouse, or even a data hub implies the unique use case and requirements of each organization. In numerous instances, they require the deployment of two or more of these data solutions. For instance, consider a scenario in the healthcare sector. An organization might employ a data lake for managing vast volumes of unstructured patient data for research and data science applications. Also, they could utilize a data warehouse to generate reports on patient outcomes and hospital operations. Additionally, a data hub may be implemented to distribute controlled medical data products to various stakeholders. Finally, a data lakehouse could be used for advanced analytics, merging insights from the patient data lake and the structured data warehouse to enhance clinical decision support systems. 

Hubs, Lakes and Warehouses Work Together Gartner Data & Analytics Summit 2023  

Data Lakehouse and Its Benefits in IoT 

The data lakehouse incorporates metadata layers, acting as intermediaries between unstructured data and data for categorization. This improves the classification and indexing of raw data, transforming it into structured, organized data through processes like ACID (Atomicity, Consistency, Isolation, Durability) transactions. Additional features encompass a decoupled architecture, enabling real-time data streams directly accessible by analytical tools, enhancing data processing and simplifying insights extraction. The reasons for adopting the data lakehouse in the context of IoT applications are strong enough: 

• IoT Data Variety. IoT generates a wide array of data types, encompassing structured data from sensors and unstructured data from sources such as images, text, and voice. This is a specific task of data lakehouse to handle it. 

• Real-time Processing. IoT frequently entails real-time or near-real-time data streams. The data lakehouse's align seamlessly with the need for real-time IoT data processing to enable swift decision-making and insight extraction. 

• Data Science and AI. IoT data serves as a valuable resource for data science and AI applications, including predictive maintenance, anomaly detection, and optimization. The capacity to store and analyze unstructured IoT data within a data lakehouse enables organizations to use AI and machine learning for valuable insights. 

• Data Governance and Compliance. Within the realm of IoT, where security and regulatory considerations are paramount, the data lakehouse introduces automated data governance and compliance procedures. These procedures enable organizations to securely manage IoT data while adhering to privacy regulations.  

• Advanced Analytics & AI. IoT data sometimes contains insights that can only be extracted through advanced analytics, such as computer vision or natural language processing. A data lakehouse enables the application of these advanced analytics methods to IoT data. At the same time, data lakehouse supports transition of many organizations from BI-focused IoT applications to more AI-driven use cases.   Explore more about the data storage with focus on IoT and find more about marketing trends within the niche in ABI’s research IoT Data Storage Technologies  As organizations continue their analytics journey, the choice between data lake, data warehouse, data lakehouse, or data hub remains crucial, with the flexibility to deploy multiple solutions simultaneously, tailored to specific use cases, requirements, and potential results. However, data lackehouse is gradually charting a course towards a smarter and more informed future. 

Explore more about the data storage with focus on IoT and find more about marketing trends within the niche in ABI’s research IoT Data Storage Technologies.

As organizations continue their analytics journey, the choice between data lake, data warehouse, data lakehouse, or data hub remains crucial, with the flexibility to deploy multiple solutions simultaneously, tailored to specific use cases, requirements, and potential results. However, data lackehouse is gradually charting a course towards a smarter and more informed future.