What Is a Data Lakehouse? Definition, Capabilities and Comparisons

Data organization and management has taken numerous forms as technology becomes more comprehensive and intelligent. Siloes are combining, artificial intelligence (AI) is categorizing and cloud storage is housing. A data lakehouse is another solution in the suite of options for corporate information oversight. Learn what it is while distinguishing it from concepts that sound similar but carry unique qualities.

What Is a Data Lakehouse?

A data lakehouse is an architecture format for information, which means companies implementing them can use existing tech stacks for construction. They are digital platforms to make data parsing, maneuvering and accessibility easier for industry professionals. It is a sector-agnostic data management solution, so every corporation can benefit from this structure if they invest in the platforms that support data lakehouses.

Companies need solutions in the age of big data. Navigating seemingly infinite data input leads to potential discrepancies, especially when integrating with AI and machine learning solutions.

Lakehouses seek to eliminate barriers against obtaining critical information while preserving integrity by preventing duplication. Many lakehouse solutions are also cloud-native, crafting some of the most collaborative environments in history for information sharing.

A lakehouse is a marriage of data lakes and warehouses. What does this mean?

How Does it Differ From Similar Concepts?

There are several other terms that sound a lot like data lakehouse but are different. These are the big-picture differences between notable computing terms:

• Data lake: Stores unstructured and structured data in original forms before processing
• Data warehouse: Store structured data and easily produces insight reports
• Data hub: Facilitates data exchanges

So, if a data lakehouse is a combination of a standard lake and a warehouse, what does this imply? First, lakehouses better support semi-structured data. It also holds raw and structured data in the most optimal fashion. Lakehouses contain high storage capacities to house both with ease. 

They also permit AI and machine learning integrations for enhanced data management. Because data scientists prime the data and environment for decision-making goals, it leverages unstructured assets in a more meaningful way.

Why Is a Data Lakehouse Essential for Businesses?

A data lakehouse is essential for businesses for several reasons, combining the best features of data lakes and data warehouses. Here are some key benefits.

Unified Data Storage

Instead of having a separate lake and warehouse, companies can combine assets into a single location. This minimizes disparate data silos and promotes collaboration across teams, regardless of their location.

Cost Efficiency

Consolidating data locales equates to lower storage costs. Additionally, workers spend tons of time extracting, transforming and loading data, and this streamlines using a data lakehouse.

Enhanced Data Accessibility

If teams want real-time processing abilities and accessible interactivity, lakehouses are the ideal architecture. There is less friction between workers and data because fewer experts need to get involved to make the data parsable. 

Scalability

If companies want to grow, their data solutions have to grow with them. Having limitations from a data lake or warehouse hinders scalability. Lakehouses have more flexible and expandable storage so corporations spend less time worrying about investing in digital transformation overhauls or new software.

Advanced Analytics and Machine Learning

Lakehouses better support integrations of the future, like AI and machine learning. Advanced analytics will provide companies a competitive edge, so embedding them where possible where large amounts of data are present is crucial for staying ahead of the curve.

Improved Data Governance

Removing siloed mentalities across a business already boosts the chances of more consistent data integrity. Security can be under one roof instead of across services and hardware, and data is centralized for comprehensive visualization at all times.

What Are the Challenges Associated with Data Lakehouse Management?

Managing a data lakehouse can present several challenges despite its many advantages. Here are three common, along with potential solutions for each.

Data Quality

While it is easier to manage data in a single lakehouse instead of numerous resources in a tech stack, data quality still requires intense maintenance. There are still opportunities for data to be incomplete, inaccurate or incompatible with each other, leading to unreliability and poor decision making.

Solve this by having dedicated staff for data cleaning and validation with robust quality control measures, such as audits and scheduled reviews from third-party experts.

Complexity of Management

Because of the hybrid nature of a lakehouse, there are various data types floating around. If a company is unfamiliar with homogenizing this information, then it could become difficult to manage metadata and obtain high levels of productivity.

Numerous data orchestration tools exist to help with management. The user-friendly interfaces help professionals upskill to a lakehouse’s needs and unique architecture. It tracks changes to help collaboration efforts and provide tips and resources for maintaining best practices.

Security and Compliance

Security is a challenge with any data housing, especially when it comes to balancing all the requirements from legal and regulatory bodies. If meeting the criteria from each framework remains a struggle, companies can request third-party assistance and incorporate internal security measures to keep data under lock and key. For example, role-based access reduces the chances data gets corrupted. Consultations from penetration testers can find oversights in the lakehouses defenses.

How Are Companies Using Data Lakehouses?

What industries are using data lakehouses right now?

Retail analytics is a massive sector, especially as e-commerce booms. There are sales transactions, inventory logs and customer feedback to track, among employee metrics and website records. The number of data collection services could cause compatibility issues with funneling into a data lake, but there are fewer concerns with a lakehouse.

Health care also uses them. It is one of the most guilty sectors for having too many siloes, so engaging with lakehouses is the best way to begin consolidating and defending patients’ sensitive data. Electronic health records are also increasingly valuable for research, and using AI with these lakehouses could find novel pharmaceutical ideas or suggest innovations for medical devices based on nuanced patient usage data.

Finally, fintech can gain a lot of value from lakehouses. Much like retail, it has data coming from countless places, from Wall Street stock markets to corporate banks. To enhance security, lakehouses can scrub data to ensure financial safety and detect fraud. 

Swimming in Data

Data architecture is critical to hone as soon as a corporation undergoes digital transformation. They provide safe, straightforward solutions to complex data management by bringing everything in a more localized space. Yet, it remains accessible through cloud technologies so next-generation collaboration can occur. The future will see experts combining lakehouses with the IoT and AI for advanced analytics.