If you have a business that produces a lot of data, you may experience one of two major pitfalls. First, you may be too inundated with data that you don’t know where to look. Second, you may focus too much on one segment of data and miss the bigger picture.
In TierPoint’s 2025 Tech/IT Modernization report, 48% of respondents told us that data management is a key driver for their IT modernization initiatives. That’s because growth opportunities are hiding just under the surface of your data, and if you don’t manage them properly and succumb to data gravity, you can miss them.
What’s an ideal approach to uncovering these insights? Data lakehouses. We’ll cover what data lakehouses are, what makes them different from other modern architectures, and how businesses can implement them to tackle various challenges.
Understanding Traditional Data Architectures
Traditional data architectures typically consist of data lakes and data warehouses. Each comes with its own strengths and weaknesses, and is designed to address specific business needs. For example, data warehouses are ideal for querying and reporting because they serve as a centralized repository for structured data, whereas data lakes are best for storing raw, unprocessed data.
Data Warehouse Limitations
While data warehouses can store and analyze structured, pre-defined data for businesses, the rigidity of the schema definition required can make it difficult to accommodate new data sources or evolve the warehouse with changing business needs without significant restructuring. Data warehouses also struggle with handling unstructured data, such as images, social media posts, and sensor readings.
Data Lake Challenges
Data lakes can store vast amounts of data in their native format, so organizations don’t have to worry about structure. However, flexibility doesn’t come without challenges, including a potential lack of organization and data quality issues. It can also be harder to support complex queries in a data lake. Plus, the sheer quantity of data can pose a security risk without appropriate governance measures and access controls.
What is a Data Lakehouse?
A data lakehouse combines the elements of data lakes and data warehouses to support everything in an organization’s data lifecycle – structured, semi-structured, and unstructured data. Some examples of data lakehouses include Databricks Lakehouse Platform, Delta Lake and Snowflake.
A 2024 survey by Dremio found that 86% of respondents plan on unifying their data, and that 70% of respondents believe half of analytics will be in data lakehouses in the next three years.
What Are the Layers of Data Lakehouse Architecture?
Key features of data lakehouse architecture can include a data storage layer, a data ingestion layer, a data processing layer, a metadata layer, and a data consumption layer. However, there isn’t one true list of components. What is included in data lakehouse architecture will vary based on the organization.
Data Storage Layer
All raw data is kept at the data storage layer, and can include all data types. Sometimes, raw data will be saved with cloud object storage, while frequently accessed data will be handled by tiered storage. This is often decoupled from compute so that data can be read by preferred tools in ideal formats.
Data Ingestion Layer
At the data ingestion layer, data is gathered from internal and external sources and brought into the data lakehouse. Data may be collected from APIs, real-time data streams, databases, and CRMs, for example. Different migration services, such as Amazon Data Migration Service, will be used to import data into the lakehouse.
Data Processing Layer
At this layer, data is prepared for analysis through real-time pipelines and batch processing. This is where raw, diverse data will be transformed into high-quality, semi-structured, and structured datasets that will be well-suited for various analytical workloads.
Metadata Layer
The metadata layer is also often referred to as the metastore/data catalog layer. Schema definitions, data lineage, and access control policies are stored here to maintain data quality and improve data discovery. This is where organizations can get a unified view of their data assets.
Data Consumption Layer
The data consumption layer is where most end users will interact with the data lakehouse. This can include data scientists or other analysts leveraging Tableau, Power BI, and other tools to access stored data. SQL and BI tools can allow users to query and analyze all types of data.
Technologies Enabling Data Lakehouse Architecture
Major cloud providers like AWS and Azure offer the core components—such as object storage, compute engines, and metadata tools—to build data lakehouse architectures, either natively or in partnership with platforms like Databricks and Snowflake.
Open-source platforms like Delta Lake and Apache Druid provide essential data lakehouse functionalities, allowing seamless integration with a wide range of cloud storage solutions. Data management platforms also play a crucial role by offering lakehouse capabilities, which support data governance, real-time analytics, and integration across diverse systems, ensuring data consistency and accessibility.
Benefits of Data Lakehouse
Instead of feeling limited by a data lake or a data warehouse, data lakehouses provide the best of both worlds and enable businesses to make better decisions, scale more easily, simplify data management, and save money.
Improved Decision-Making
By providing a unified view of your data, lakehouses eliminate silos and centralize both your structured and unstructured data in one platform. When all data is available in the same place, businesses can conduct holistic analyses and make better data-driven decisions.
Because data lakehouses support more data formats, the configuration also allows businesses to leverage more powerful analytics tools. This can help organizations identify previously hidden patterns and predict trends with greater accuracy
Better Performance and Scalability
When data volume and processing needs change, data lakehouses can scale to meet new demands. This When data volume and processing needs change, data lakehouses can scale to meet new demands. This improves performance and cuts down on manual provisioning. Since real-time processing is easier with data lakehouses, businesses can gain access to valuable insights much faster, giving them a competitive edge.
Simplified Data Management and Governance
Instead of being relegated to one data type, data lakehouses enforce governance policies across all data types, improving the consistency of data quality and ensuring regulatory compliance. When all types of data are stored together, the central repository makes data management more straightforward, improving the user’s ability to discover and understand relevant datasets they need to review or analyze.
Cost-Effectiveness and Efficiency
Cloud object storage is typically a cost-efficient solution for storing large volumes of diverse data, especially when combined with tiering strategies (e.g., standard, infrequent access, archive) to optimize usage and costs. Data lakehouses also cut down on the need to manage multiple disparate systems, reducing operating costs and increasing efficiency.
Improved Data Democratization
The unifying nature of data lakehouses also improves end-user access to the data. When data lakehouses have strong governance structures and well-built metadata layers, users can leverage and analyze data without waiting for specialists to assist them, improving access across the organization. This can lead to more valuable, nuanced insights.
Challenges of Data Lakehouses
Of course, bringing disparate types of data to a centralized location also comes with challenges, including increased complexity, concerns over governance controls, security and access control challenges, internal skills gaps, and issues with cost management.
Data Lakehouse Implementation and Management Complexity
A data lakehouse consists of several diverse components that all need to work together to be effective. This includes storage, processing engines, governance frameworks, and metadata management tools. The tools need to be configured properly for ideal interoperability, and one issue in the process can lead to longer timelines and higher implementation costs.
Governance Challenges
Governance capabilities tend to be greater in modern data lakehouses compared to their more traditional counterparts. Still, ensuring that consistent governance policies are kept across the environment and enforcing data quality standards can prove challenging. Businesses also need to ensure data is consistent between the lake and warehouse environments.
Security and Access Control
While data lakehouses can improve democratization, too much access can be a bad thing. Organizations must ensure that they are giving access to users as needed to perform their duties, while maintaining security controls and limiting access where it isn’t needed.
Internal Skills Gaps
To use and effectively manage a data lakehouse internally, your team will need a diverse set of skills. This can include having data scientists, data analysts, data engineers, data visualization experts, and governance specialists on the team. This can be too expensive for a business to shoulder. It can also be hard to find sufficient talent to fit your needs.
Cost Management Issues
Data lakes can be cost-effective storage solutions, but the larger cost of a data lakehouse can be much higher when considering costs associated with processing, personnel, and specific software. To optimize these costs, organizations must closely monitor how resources are being used and managed.
Use Cases and Applications of Data Lakehouses
The versatility of data lakehouses makes them ideal for several use cases and analytical needs. Here are a few applications that may make this type of data management architecture attractive to your business.
Advanced Analytics and Business Intelligence
While traditional architectures can result in siloed data, data lakehouses can create a 360-degree view of user data. This can make recommendations and user profiles more relevant, and can also help businesses identify trends to develop new products and services. For a FinOps team, this might look like compiling structured costs from cloud providers, resource utilization from application logs, and invoices from structured billing data sources to better understand cloud costs.
Advanced analytics and business intelligence can also enable organizations to analyze both historical and real-time information, making it easier to pinpoint patterns that may indicate fraudulent activity.
Machine Learning (ML) and Artificial Intelligence (AI)
Machine learning and artificial intelligence can predict potential equipment failures for manufacturers, provide personalized recommendations to retail shoppers, and analyze call records to find customers at risk of churning.
Because data lakehouses aren’t limited in their ability to store and analyze data, machine learning, and artificial intelligence can use several different data sources for more nuanced data-driven decisions. The Dremio survey found that 81% of respondents are using data lakehouses to support AI applications and models.
With all of these sources, generative AI could be used in various settings, such as retail, to create personalized content for customers, suggest customized product bundles, or create descriptions optimized for different customer profiles.
Real-Time Data Processing and Streaming Analytics
Data lakehouses can ingest and process data streams from connective devices in real-time. This is important in situations where real-time decision-making is a must – for example, health sensors on patients or sensor data from smart grids. This could look like doctors receiving real-time information from a remote monitor a patient is wearing to keep an eye on a specific health condition, allowing for faster intervention and a clearer window into what’s happening with a patient and when.
Real-time data can also improve response time during major sales or business events, getting a handle on customer sentiment more efficiently.
What Industries Benefit the Most from Data Lakehouses?
Any businesses or industries that deal with a complex array of data can benefit from data lakehouse architecture.
- Financial services: Everything from customer transactions, sensitive personal data, and social media sentiment can be gathered by banks, investment firms, and insurance companies. Data lakehouses can help businesses in this industry detect fraud, mitigate risks, and personalize products for users.
- Retail and eCommerce: When customers make purchases, browse websites, and sign up for loyalty programs, the data can be aggregated into a lakehouse for a more unified view of individual behavior.
- Manufacturing: Manufacturers are leveraging Internet of Things (IoT) devices more in the production pipeline for real-time reporting on performance and operation. Data lakehouses can help with predictive maintenance, as well as optimization of certain production processes.
- Healthcare: Healthcare organizations collect sensitive health data, including patient intake forms, historical records, and imaging results. Data lakehouses can form connections between patient data and other sources of information, streamlining and personalizing treatment plans and other patient experiences.
- Government: Government agencies can be altered to emerging threats, optimize their resource allocation, and aggregate smart city sensor data in a data lakehouse.
Evaluating if a Data Lakehouse Architecture is Right for Your Business
For some businesses, traditional data architecture will be enough. However, if you’re struggling with data volume, variety, or management, or you’re not getting enough out of analytics, you may want to make the switch to a data lakehouse.
Data Volume and Variety
If your organization amasses a large volume of data, either structured or unstructured, handling the scale with a data lakehouse can be worth the investment. You’ll also want to think about the variety of your data. If you have some structured databases, some sensor data, and information you want to collect from social media feeds, data lakehouses can help you manage and store a variety of formats, giving you a unified platform for your data.
Analytics Requirement
Traditional architectures can accomplish simple reporting, but if you’re looking for more advanced analysis using AI or machine learning or looking to combine data from different formats into one reporting platform, data lakehouses can help you form deeper analyses and reach more nuanced insights.
Current Data Management Challenges
Think about your current data management struggles. If data silos, limited storage options for unstructured data, or data governance issues exist due to your present data architecture, lakehouses can help.
How to Design and Implement a Data Lakehouse Architecture
The more careful you are in planning your data lakehouse architecture, the more success you’ll have in implementation. Here are the steps businesses should follow when designing their ideal data lakehouse setup.
Outline Your Business Needs and Goals
Your business needs and goals will shape what your data lakehouse looks like and what services you choose to support it. Start by analyzing the different types of data you need to store and access, and their level of structure. What current data sources are you storing, and what might you want to add once you incorporate a data lakehouse?
A data lakehouse should work for your business and the specific problems you want to solve. By identifying your use cases early, you can start formulating your data ingestion strategy, governance policies, and list of potential tools.
Knowing what success will look like for you can also help you track how the data lakehouse has impacted your business. Do you want to speed up your decision-making, improve your ability to conduct data-driven marketing campaigns, or optimize your resource allocation? Establish the metrics you will use to track success early.
Research and Select Your Cloud Platform and Services
Decide whether you want to work with a major cloud provider, such as AWS or Azure, or a third-party provider for data lakehouse services. Your ultimate decision will come down to a combination of features, integration possibilities, pricing, scalability, and tools that each platform carries and supports. Your cloud platform should work with whatever services you choose, whether they are open-source or commercial.
Define Your Data Ingestion Strategy
How will data be extracted from databases, social media platforms, applications, IoT devices, and any other sources you may want to pull into your data lakehouse? What should be streamed in real-time and what can be batch-processed?
Once you know how you want data to come in, you’ll also want to establish a process for transforming, cleaning, and validating data before it goes into the data lakehouse. This can improve consistency and data quality.
Outline Your Data Architecture and Governance Principles
Even though data lakehouses can store structured, unstructured, and semi-structured data, you will want to outline guidelines for data schema and structure based on how you want to use them. To maintain data usage and regulatory compliance, create policies for access control, data security, and data retention.
Establish Your Security and Access Controls
Protect your repository of structured and unstructured data through access controls, intrusion detection systems, and encryption, keeping bad actors out via multiple tactics. Not all users will need access to all data held in the data lakehouse. Assign read, write, and modify permissions based on user roles and responsibilities held at the business to bolster security.
Develop a Plan for Monitoring, Optimization, and Management
Address new issues quickly by implementing monitoring that checks for data quality and system performance. Explore cost-optimization strategies based on data storage usage and designate a team to conduct ongoing data lakehouse management. This can include security updates, performance optimization, and user support.
Empower Your Data Management Strategy with the Right Cloud Environment
Data lakehouses offer a powerful solution for organizations struggling with data volume, variety, or management limitations in the cloud. But determining the best-fit cloud environment to support data lakehouse architecture requires careful planning, expertise, and the right cloud partner.
At TierPoint, our team of cloud experts can help guide you in the right direction – contact us today to learn more. In the meantime, download our whitepaper to explore different cloud options available for data management.
