IoT technologies are continuing to transform how organizations monitor, automate, and control their business operations. What’s more, IoT is currently poised for further growth: The number of connected IoT devices is projected to exceed 29 billion by 2030 and global IoT spending is expected to surpass $1.1 trillion by 2024. So, it’s crucial for organizations to develop innovative new business models and capabilities to take advantage of these growth trends.
However, as an organization’s IoT infrastructure continues to expand, so do the challenges of managing ever-growing volumes of data that must be stored and categorized. IoT architects need to lead the charge in overcoming these data challenges.
To that end, a data lakehouse could solve the issues of data storage and quality control.
The evolution of data management
In today’s modern business world, ever-increasing amounts of data are being captured, stored, and analyzed at a volume that can seem almost infinite. The benefits of all this data are that it allows organizations to determine critical business decisions that can improve their product offerings, reach new customers, and expand their industry footprint. However, as the volume of data has grown, so has the amount of unstructured data that cannot be easily classified.
For a while, data warehouses served the main purpose of collecting and storing data. This worked well enough when the data was structured, meaning it was highly organized and formatted for easy query searching in a database. But with the growth of big data and IoT, a lot of the data that’s now being collected is in an unstructured or semi-structured format, such as log data, audio, and video streams, and sensor measurements. This type of data now makes up 80 to 90 percent of the information gathered by an organization and can be of immense value in deriving business insights.
However, due to the structured and ordered way in which data warehouses operate, they are ill-suited to the task of storing large amounts of unstructured and semi-structured data at scale. For this reason, data lake architecture has emerged, allowing for the easy storage of data in its raw format. But while the data lake is undoubtedly a powerful data storage tool, it also has some issues of its own. For instance, gathering large volumes of raw data can get very messy, leading to data governance and privacy issues, technical complexity, and an inability to perform indexing or bring any structure to this raw data.
This led IT teams to begin using a combination of data warehouses and data lakes, which solved many of the individual problems of these platforms. Unfortunately, these integration efforts also created new problems, such as slow speeds, complex data governance, and inefficient quality control measures. These issues would persist until the development of a new type of data platform, the data lakehouse.
What is a data lakehouse?
The data lakehouse is a relatively recent development in data platforms. Designed to address the limitations of both data warehouses and data lake systems, a data lakehouse is a hybrid architecture that combines the storage flexibility of the data lake with the data management and governance efficiency of a data warehouse system. In addition, a data lakehouse also solves the integration issues of using a data warehouse and data lake system in conjunction.
To achieve all this, the data lakehouse has a few technological advancements. For instance, the architecture is based on metadata layers, which can best be thought of as middlemen between unstructured data and the data that will be used to categorize that data. This allows for the classification and indexing of raw data into tidy structured data through ordering processes such as ACID transactions. Other features of a data lakehouse include its decoupled architecture, allowing for real-time data streams that can be directly accessed by analytical tools for more efficient data processing and insights extraction.
The benefits of a data lakehouse architecture
The following capabilities of a data lakehouse can help IoT architects reduce the complexities of handling large volumes of unstructured data, while also improving processing speed and maximizing ROI from IoT investments:
The core of a data lakehouse’s architecture is its decoupled nature. This means that the storage and compute models are separated, allowing users to easily scale their compute resources up or down as needed without affecting their storage needs. The result is a more flexible and optimized system that can store diverse data formats, just like a data lake, while also providing the data management capabilities of a data warehouse.
Improved data management
With a data lakehouse, users can automate the data integration process, which can be especially useful when dealing with unstructured IoT data For example, when developers are using a traditional data lake platform, it can be challenging to integrate new data assets when a post-processed dataset is no longer linked to the original data source. Similarly, a data warehouse platform can maintain a link between the original data source and a post-processed data asset, but only when the data is in a structured format.
By using a data lakehouse, IoT architects can overcome these limitations by deploying additional metadata layers and file caching that allows for the capturing of additional data asset information. This represents a huge leap in capability, as it essentially allows a user to index raw unstructured data, something that’s not possible with a data lake.
Additionally, data lakehouses allow users to perform data workflows that are typically only available through a data warehouse system, such as data versioning, auditing, indexing, and query optimization.
Strengthen security and access controls
As an IoT architect, you are responsible for designing a data platform that sufficiently complies with the security and governance policies of your organization. A data lakehouse allows you to achieve this by using advanced policy-based access control methodologies, such as Attribute Based Access Controls (ABAC). Such methods essentially segregate data assets between data consumers and producers, allowing for a high level of system security.
The idea is to restrict access to sensitive information while still providing information access on a need-to-know basis through the Principle of Least Privilege. The ABAC model prevents unauthorized access and privacy leakage by tracking how specific metadata attributes of a data workload apply to permissions clearance for an individual user. Best of all, the access control model is flexible enough to meet the evolving security needs of a growing business.
The data lakehouse is likely to become increasingly popular as more organizations recognize the need to overcome the limitations of both the data lake and data warehouse systems. Those organizations that are in the process of upgrading to or expanding on their IoT infrastructure are certain to see an uptick in the volume of raw unstructured data that must be stored and processed, something that’s just not possible with older data management systems.
In short, a data lakehouse can allow an organization to keep up with its data needs, and IoT architects will be at the forefront of ensuring that any transition is a smooth one.