Data lakehouse architecture: How to build your data foundations for AI

Most companies are working with systems that simply aren't built to handle the massive, mixed types of data needed for AI and advanced analytics. Traditional data architectures-whether data lake vs data warehouse-each offer strengths, but they're often unable to support the demands of modern data landscapes fully.

The data lakehouse changes that. It combines the best of both worlds-the scale and flexibility of a data lake with the data management and performance strengths of a warehouse. In this article, we'll explore what makes the data lakehouse architecture different and how it addresses the pain points of traditional and data storage systems for enterprise-specific needs.

Challenges with traditional data lake and data warehouse setups

Businesses today require architectures that support complex analytics, Machine Learning, and real-time insights. However, the distinct limitations of data lakes and data warehouses make it challenging to fulfill these requirements effectively. Let's break down the specific gaps these architectures present in modern environments.

Data lakes were initially designed to accommodate vast amounts of varied data, enabling storing both structured and unstructured formats in one repository. However, while data lakes provide flexibility, they present significant obstacles that limit their efficiency and reliability for enterprise-level data management and analytics.

Primary challenges with data lakes:

• Without stringent governance frameworks, data lakes often devolve into "data swamps," making it difficult to locate, manage, or interpret data accurately.
• Data lakes frequently lack built-in mechanisms for enforcing data quality, which can result in inconsistencies, redundancies, and inaccuracies.
• Integrating and harmonizing data from disparate sources within a lake is highly complex, requiring extensive data preprocessing.
• Business Intelligence tools and SQL-based analytics struggle with the unstructured, raw data in lakes.
• Data lakes are generally architected for batch processing, which makes them unsuitable for applications requiring real-time or low-latency data analytics.

While data warehouses excel in structuring data for analytics and reporting, they have considerable limitations when handling the scale and complexity of present-day enterprise data.

Key challenges with data warehouses:

• Scaling a data warehouse for larger datasets or diverse data types involves significant costs, often rendering warehouses impractical for massive datasets.
• Warehouses struggle to accommodate unstructured data like text, images, or sensor data, which limits their utility in modern analytics ecosystems.
• Adding new data sources requires substantial schema redesigns, slowing down data integration and adaptation.
• Warehouses predominantly rely on batch processing, resulting in data latency that hinders real-time analysis.

While both data lakes and data warehouses have strengths, their challenges become more evident when applied to data-driven initiatives, particularly in AI and Machine Learning (ML) projects. Let's examine the specific challenges these architectures face in AI-driven initiatives:

1. AI and ML models depend on high-quality, consistent data. However, data lakes often store data in its raw form without enforcing structure, leading to inconsistencies across data types and sources. Data warehouses, while structured, struggle to integrate diverse data formats.
2. Many AI and ML applications require real-time or near-real-time data access for ongoing model training, updates, and deployment. Traditional data lakes and warehouses often introduce latency due to batch processing.
3. For AI workflows, data quality is the top priority. Data lakes, however, often lack rigorous data validation mechanisms. While data warehouses have built-in validation processes, they are less effective with unstructured data.
4. AI models often require unstructured data like text, images, and streaming information from IoT devices. While data lakes can store unstructured data, they lack the indexing and management features for efficient retrieval and processing. Data warehouses, meanwhile, are optimized for structured, tabular data.

These limitations make it clear: traditional data lakes and warehouses weren't built to handle the demands of modern AI and data analytics services. That is where the data lakehouse comes in-a hybrid solution designed to bridge these gaps, offering the flexibility of a data lake with the structured reliability of a data warehouse.

Understanding data lakehouse

The data lakehouse represents a new direction in data architecture, combining the flexibility and scalability of data lakes with data warehouses' governance, data management, and performance benefits. Initially developed by Databricks, the lakehouse model addresses a core challenge that has long frustrated enterprises: managing structured and unstructured data in a unified environment that supports high-performance analytics, real-time processing, and rigorous data quality standards.

Built on the open-source Delta Lake project, Databricks' lakehouse platform integrates with popular cloud storage solutions. It provides a scalable, open data format designed for reliable, ACID-compliant transactions across large volumes of data. This approach eliminates the need to move data between lakes and warehouses for different types of processing. Databricks introduced advanced capabilities like schema enforcement, time travel, and batch and streaming data support with the lakehouse model.

Since Databricks rolled out the data lakehouse concept, other technology providers have adopted and adapted the model, offering their versions. While these implementations might vary slightly in terminology or specific features, the core principles remain consistent.

The data lakehouse was developed to solve the limitations of the warehouse and lake. Some core principles of a lakehouse include:

• Open storage formats such as Parquet and Optimized Row Columnar (ORC) are used for data storage. At the same time, APIs allow access to data in the storage layer via languages like SQL, Python, and Scala.
• Storing all structured, semi-structured, and unstructured data types in a single environment.
• Schema-on-read and schema-on-write capabilities to ingest data without strict upfront structuring in its raw format without imposing a rigid structure upfront.
• ACID (Atomicity, Consistency, Isolation, Durability) transactions control to cloud-based data storage for maintaining data integrity and reliability.
• Optimized for batch and streaming data processing through indexing, caching, and clustering, making them suitable for real-time analytics and insights.
• Separates computing resources from storage to allow for scalable workloads.
• Minimizes data duplication by allowing BI tools to access source data directly within the lakehouse.

Read more: Data lakehouse vs data warehouse: In-depth comparison

How data lakehouse can streamline your AI initiatives

AI and machine learning projects depend on consistent, high-quality data access, real-time processing, and a seamless flow of information across multiple data types. Let's examine how a data lakehouse can address these critical needs.

Unified data storage

In traditional setups, data scientists often need help accessing data from fragmented systems, leading to inefficiencies in training and deploying AI models. A data lakehouse unifies storage and access, enabling real-time interaction with diverse data formats. This single data environment supports both raw and structured data, making it possible to streamline complex AI workflows, reduce data movement, and minimize the need for data transformations.

Real-time processing capabilities

Real-time data processing is a core strength of the data lakehouse architecture. In the context of machine learning, access to real-time data translates into more responsive and adaptable models. For instance, a lakehouse enables machine learning models to incorporate live data streams, making it possible to detect trends or anomalies as they occur.

By supporting batch and streaming data, lakehouses enable organizations to train AI and ML models on the latest available data and update those models more frequently. Real-time processing also means that models can be tested and adjusted in real time.

Advanced data governance

When it comes to AI, data quality and reliability are everything. A data lakehouse helps ensure models are built on trustworthy data by offering advanced data governance tools- data lineage, versioning, and access controls. Data scientists can see where the data came from, verify it's up-to-date, and control who has access, which is vital for compliance and accuracy. The lakehouse architecture provides that level of control, making it possible to create more transparent and accountable AI models that meet both ethical and regulatory standards.

Now, let's explore how companies can apply or are already using data lakehouses to drive impactful AI initiatives across various industries:

• If your company needs real-time customer personalization, consider using a lakehouse to support AI-driven recommendation engines for customized content or product suggestions.
• Whether you're in manufacturing or transportation, a lakehouse can power predictive maintenance by using AI to forecast equipment failures, minimizing downtime and saving on repair costs.
• When fraud detection is critical, financial institutions can use lakehouses to monitor real-time transactions.
• If accelerating drug discovery is a goal, pharmaceutical companies can leverage lakehouses to store and analyze genetic, clinical, and patient data, enhancing AI-driven research for faster R&D cycles.
• When supply chain optimization is essential, lakehouses allow retailers and logistics firms to apply AI to demand forecasting, inventory management, and route optimization for greater efficiency.
• If customer support automation is on your roadmap, consider lakehouses to train AI-driven chatbots and virtual assistants using historical and real-time interaction data for improved responsiveness.
• Whether you're in finance or investment, lakehouses can support financial forecasting and portfolio management by enabling AI models to analyze market data and predict trends.

What data lakehouse architecture includes

A well-designed data lakehouse architecture integrates diverse data management needs into a cohesive structure. At the core of this architecture is the Medallion architecture, which has a three-layered structure that progressively refines data. Let's break down into these layers of lakehouse data architecture:

Bronze layer

The Bronze Layer is where all incoming data first lands, capturing raw, unprocessed information directly from diverse sources such as IoT devices, transactional databases, and API feeds. This layer aims to retain the original, unaltered data format, ensuring nothing is lost or filtered out at this early stage. By keeping data in its purest form, the Bronze Layer enables comprehensive traceability and data lineage, allowing analysts and data scientists to trace back to the source whenever needed.

Storing raw data in this foundational layer supports flexible, exploratory analysis-precious for working with unstructured or semi-structured data that may require different processing approaches over time. This unprocessed data is an immutable, comprehensive record, a reliable source that can be reprocessed or referenced, providing a solid base for downstream transformations and analysis.

Silver layer

The Silver layer is where raw data is refined into a structured, reliable format. Data goes through critical cleaning and enrichment processes at this stage, such as removing duplicates, standardizing formats, and addressing missing values. The goal is to create a consistent and analysis-ready dataset by validating and consolidating disparate sources, ensuring the data is well-structured and more accessible.

In the data lakehouse architecture, the Silver layer is invaluable for AI and machine learning-it reduces the preparation time needed for model training and enables more efficient workflows for data scientists. This layer becomes a dependable foundation for business analytics, ad-hoc queries, and more complex data processing down the line.

Gold layer

The Gold layer represents the pinnacle of data refinement within a lakehouse architecture. At this stage, data is fully curated, aggregated, and aligned to meet specific business requirements, such as performance metrics, customer behavior insights, and strategic KPIs. Optimized for quick access by BI tools and reporting systems, the gold layer is structured in formats like star schemas that support rapid querying and visualization, reducing the need for additional transformations. When data reaches this layer, it is accurate and tailored to deliver actionable insights for strategic decisions.

Following the layered Medallion architecture, which ensures data is refined progressively from raw intake to actionable insights, an effective lakehouse data architecture also requires supporting components that maintain data quality, compatibility, and security. While the Bronze, Silver, and Gold layers handle data storage and transformation, additional elements are needed to optimize data usability, governance, and accessibility across the lakehouse.

WHITE PAPER

Unlock the power of enterprise data lakes for scalable analytics – access the guide now!

Full name*

Business Email*

I have read and accepted the Terms & Conditions and Privacy Policy*

DOWNLOAD

Success!

Additional components of data lakehouse architecture

These supporting features, like open data formats, metadata management, and transaction support, enable enhanced data compatibility, governance, and security. Let's look into these components and explore how they complement the Medallion layers of data lakehouse architecture.

Data processing and analytics tools

An open data lakehouse architecture format ensures that data stored in the lakehouse can be accessed, processed, and analyzed using various data processing and analytics tools. This compatibility allows teams to work with diverse data engines like Spark, Presto, Hive, and others without requiring complex conversions or additional transformations.

Data governance and security

With well-defined schemas and formats, organizations can enforce data quality standards more effectively and monitor data changes through the lineage, ensuring transparency and trust in the data. Security also benefits from a unified format; access control and encryption policies can be applied uniformly across datasets.

Metadata management

Metadata management in a lakehouse environment acts as the "map" of the data landscape, providing structure, context, and meaning to vast amounts of raw and processed data. By cataloging each dataset with comprehensive metadata, the lakehouse enables users to navigate data assets efficiently and clearly. Metadata covers data origin, schema, structure, usage history, and transformation lineage.

Metadata managing inside data lakehouse architecture allows data scientists and analysts to locate relevant datasets without the guesswork, promoting efficiency and reducing redundant work. With a metadata catalog in place, teams can easily see what data is available, how it's been processed, and its level of quality-all at a glance.

Data versioning

Data versioning is a sophisticated feature that enables the lakehouse to maintain a history of changes, ensuring that any modifications or transformations can be traced and, if necessary, reversed. In practice, data versioning stores different versions of a dataset as it evolves.

Versioning provides a snapshot of data at each point, allowing data scientists to revisit specific versions for model retraining or validation. Teams can revert to a previous version if an issue arises in a particular data pipeline, minimizing downtime and preserving data integrity. This rollback capability is a game-changer in maintaining a reliable and consistent data pipeline.

Transaction support

Transaction support brings ACID properties into the data lakehouse, ensuring data operations are reliable, even in complex, high-demand environments. ACID compliance is critical in scenarios where multiple users or applications interact with data concurrently, providing stability and accuracy.

Transaction support guarantees that data remains stable and consistent, even when ingested or updated in real-time, enabling continuous and reliable model training without data anomalies. ACID compliance ensures that these concurrent processes do not conflict, preventing issues like partial updates or duplicate records, which can compromise analytics outcomes.

Now that we've explored the layered architecture that structures and refines data in a lakehouse, let's focus on the data storage format.

How data lakehouse storages differ

To build an effective data lakehouse, selecting a storage format that balances performance with functionality is essential. The right format supports efficient data operations and facilitates advanced data handling capabilities. Key considerations include:

• Performance: Columnar formats are generally the most efficient for query performance.
• Data Manipulation Language support: DML capabilities are essential, enabling operations like updates, deletes, and merges directly within the data lake.
• Compatibility: The storage format should handle streaming data writes and reads, supporting real-time data processing.
• Change data stream (CDC): A row-level change publishing mechanism allows downstream systems to receive only the modified data, optimizing update efficiency.
• Merge on Read (MoR) and Copy on Write (CoW): These options balance read and write performance based on application needs.

Among the available data storage formats, Delta Lake, Apache Iceberg, and Apache Hudi stand out for their advanced capabilities. Here's a closer look at each.

Delta Lake

Delta Lake is known for its robust support for ACID transactions and seamless integration with Apache Spark, making it a reliable choice for batch and streaming data applications. Its strengths lie in data reliability and consistency, which are essential for businesses that require high data accuracy and fault tolerance standards, such as financial institutions. By supporting schema enforcement and evolution, Delta Lake allows users to adjust data structures over time without jeopardizing data integrity.

Key benefits:

• Ensures data consistency and reliability, even with concurrent read and write operations.
• Allows easy management of evolving data schemas, accommodating changes over time.
• Handles real-time streaming and batch workloads seamlessly, which is critical for applications needing low-latency data access.

Drawbacks:

• Integrates poorly with other data processing engines like Flink or Presto.
• Requires maintaining multiple data versions to support time travel and versioning.
• Involves complex setup and maintenance with ACID transactions and schema enforcement, which can be resource-intensive.

When to choose Delta Lake: This data storage is well-suited for organizations that need reliable, consistent data transactions. It's especially advantageous for industries requiring high data integrity, like finance or healthcare, where consistent, real-time data access is essential.

Apache Iceberg

Apache Iceberg is notable for its ability to manage petabyte-scale tables and advanced metadata management capabilities. This format is engineered for high scalability, making it particularly effective in environments dealing with massive datasets. Iceberg's partitioning and indexing allow for efficient query execution on large-scale data, while its schema evolution feature enables users to modify table structures over time without costly reprocessing. Iceberg also supports multi-table transactions, enhancing its utility for complex data operations.

Key benefits:

• Works with multiple query engines, enhancing flexibility in diverse data ecosystems.
• Supports dynamic and hidden partitioning, which optimizes performance on large datasets and reduces the need for manual partition management.
• Allows modifications to data schema without requiring table rewrites, saving processing time and enhancing data usability.

Drawbacks:

• Requires detailed configuration for dynamic partitioning and multi-table transactions.
• Lacks built-in support for real-time streaming data.
• Demands additional effort to integrate with non-Spark engines, such as Flink or Presto.

When to choose Apache Iceberg: It is ideal for enterprises with massive, complex datasets requiring high scalability and flexibility, such as media or telecom companies. Its compatibility with multiple engines makes it a strong choice for organizations seeking to avoid vendor lock-in or those that run analytics across diverse data engines.

Apache Hudi

Apache Hudi is designed for real-time data processing and excels at incremental updates. Its support for ACID transactions and efficient handling of upserts and deletes makes it ideal for applications that demand near real-time analytics, such as e-commerce and streaming data environments. Hudi's MoR and CoW modes allow users to select read/write optimizations based on workload, offering a balanced approach to data ingestion and query performance.

Key benefits:

• Efficiently processes new data incrementally, reducing data refresh times and optimizing storage costs.
• Provides ACID transaction capabilities similar to Delta Lake, supporting reliable data writes.
• Is optimized for real-time ingestion, enabling low-latency access to the most current data.

Drawbacks:

• Requires more configuration and management, especially for users unfamiliar with real-time processing intricacies.
• Primarily optimized for Spark and Hive, which may limit its usability in environments with diverse processing engines.

When to choose Apache Hudi: This data storage is highly effective for use cases involving continuous data ingestion and frequent updates, such as e-commerce platforms that track user activity or IoT systems generating a constant flow of sensor data.

Key considerations for choosing a data lakehouse storage

When enterprises evaluate storage formats for a data lakehouse, aligning the format's capabilities with immediate data needs and long-term business objectives is essential. Here are key considerations from an N-iX perspective to guide the decision-making process:

Scalability

We prioritize scalability in storage format selection to ensure our clients can manage and analyze ever-growing datasets without performance bottlenecks. For businesses with fast-growing or petabyte-scale data requirements, we consider formats like Apache Iceberg, known for handling large datasets with efficient metadata management and dynamic partitioning. Our team performs scalability assessments to predict growth trends and design a data lakehouse that expands seamlessly.

Operational compatibility

Understanding that each client has unique, existing data processing tools and workflows, we carefully evaluate the operational compatibility of each storage format. For instance, Delta Lake's deep integration with Spark ecosystems is ideal for clients already invested in Spark-based infrastructures. For clients needing cross-platform flexibility, we often recommend Apache Iceberg due to its broad compatibility with multiple query engines.

Data freshness and real-time requirements

Real-time data access is important for many businesses, especially in ecommerce and IoT sectors. In such cases, we evaluate storage formats like Apache Hudi, which are designed for real-time data ingestion and incremental processing. Our team collaborates closely with clients to assess their real-time data needs and implement solutions that maintain freshness and accuracy, providing continuous access to the latest information without compromising reliability.

Governance and compliance

Maintaining data governance and compliance is critical for clients in regulated industries, like finance or healthcare. We consider formats like Delta Lake and Iceberg, both of which support ACID transactions. We also evaluate the need for features like schema evolution and detailed metadata tracking, which are crucial for auditability and compliance.

Conclusion

Having the proper data infrastructure isn't just a technical choice-it's a strategic one.

For businesses looking to deploy AI, advanced analytics, and scalable insights, this hybrid architecture provides a robust foundation for real-time insights, seamless data access, and scalable analytics, all while keeping data quality and governance intact. However, as powerful as the lakehouse model is, implementing it effectively requires the right expertise and a strategic, well-planned approach.

That's where partnering with N-iX can make a real difference. With our extensive experience in data lakehouse implementation, governance, and advanced analytics, we're equipped to guide you through every stage of the journey-from selecting the suitable storage format to ensuring real-time data accessibility and robust governance through data lakehouse architecture.

So, if you're ready to take advantage of the full power of your data, let's work together to make it happen. The journey to a high-performing lakehouse can be straightforward and impactful with the right partner at your side.