What is a Data Lakehouse? Architecture, Features, and Benefits

Data-driven organizations are searching for different storage solutions to manage the latency, volume, and resilience of big data and analytics. Initially, businesses used existing data lakes and warehouses in their tech stack to make the most out of data assets. However, both tools have different use cases, and complementing these tools requires a lot of resources and expertise. To leverage the features of both data warehouse and lake data lakehouse comes into play. It is an architecture that merges the best aspects of a data warehouse and lake in one storage system.

This article will give a high-level overview of what is a data lakehouse and discuss its architecture, features and more. 

Data Lakehouse Architecture 

Generally, a data lakehouse architecture has five layers: Ingestion, Storage, Metadata, API, and Consumption. Let’s look at each layer of data lakehouse in detail: 

• Ingestion Layer
• Storage Layer 
• Metadata Layer 
• Application Programming Interface (API) Layer
• Consumption Layer

Ingestion Layer

As the name suggests, this layer is dedicated to data integration into the lakehouse. It gathers data from many sources and transforms it into a standard format that can be managed in lakehouse. The data sources in this layer can be internal or external, including relational and non-relational databases, social media, etc. 

Storage Layer 

In this layer, you can store and manage data in the lakehouse. A data lakehouse stores different types of structured, semi-structured, and unstructured data in file formats such as Optimized Row Columnar (ORC) or Parquet.  

Metadata Layer 

The metadata of lakehouse is a unified catalog that delivers metadata for each object in the storage system to help organize information about the data. This helps you to gain more insights and characteristics of stored data in the lakehouse. You can use this layer for management features like file caching, ACID transactions, and indexing for faster queries. 

Application Programming Interface (API) Layer

In this layer, APIs serve as a programmatical interface that allows different components of the data lakehouse, including analytical tools and services, to interact with each other. Therefore, as a developer or a consumer, you can use a range of libraries and languages to conduct advanced analytics and increase task processing using APIs. 

Consumption Layer

This final layer of data lakehouse architecture accommodates client tools and applications. Therefore, they have access to data and metadata stored in the lakehouse. In this layer, users from across the organization can use data lakehouse to carry out analytical or other tasks such as business intelligence, machine learning, and data visualization.

Features of Data Lakehouse 

A data lakehouse distinguishes itself from the rich set of features it has to offer. Some of the key features of this storage solution include: 

Unified Data Architecture

Organizations have always struggled with fragmented data mixed up with many storage systems. A data lakehouse eliminates the need for disparate storage solutions by unifying unstructured, raw data with processed, structured data, creating a centralized repository for all data types. With this unity, data access and management become much easier. 

Analytics Flexibility 

With data lakes and data warehouses, there are many limitations when it comes to analytical tasks. For instance, a data warehouse has schema rigidity and supports limited data types, while a data lake lacks performance. The data lakehouse offers an adaptable setting to overcome these challenges and allows you to perform many analytical approaches. This includes traditional SQL-based queries, advanced machine learning algorithms, and business intelligence tasks. 

Schema Evolution

Unlike data warehouses that work with rigid pre-defined schemas, a data lakehouse allows you to modify schema in real-time. This implies that as the data evolves, the underlying schema is flexible enough to change with the data. Therefore, you can streamline the integration of new data sources without interfering with existing workflows. 

ACID Transactions

Data lakes lack transactional support, but a data lakehouse incorporates ACID (Atomicity, Consistency, Isolation, Durability) transactions. Atomicity ensures that every process contained in a transaction is handled as a single individual unit. Consistency checks if the data is valid before and after the transaction. Isolation prevents multiple transactions from interfering with each other. And Durability makes sure that modifications in transactions are saved permanently. 

Data Warehouse Vs Data Lake Vs Data Lakehouse

Let’s briefly explore the difference between three storage solutions of data management: 

Data Warehouse 

A data warehouse is a centralized storage system that combines data from various sources in a structured format. It holds highly unified and structured data to support specific analytical and business intelligence needs. In a data warehouse, the data is transformed into a standard format that could fit the defined schema. 

Pros 

• Little or no data prep is required, making it easier for developers and analysts to access data. 

• The data warehouse has the ability to provide fast and efficient query performance on structured data. This makes it well-suited for complex business intelligence and analytical tasks.

Cons

• It is not ideal for storing unstructured data. 

• The process of designing and implementing a data warehouse requires careful planning of ETL (Extract, Transform, Load) processes, which can be resource-intensive. 

Data Lake

A data lake is known for storing raw, unstructured data in different formats to directly support machine learning and data science. Data lake focuses on rapid data ingestion without concern about its type or size. This solves the need to have all data types in one place, enabling cross-source analysis. 

Pros

• Data lakes can efficiently store vast amounts of semi-structured and unstructured data.

• Compared to data warehouse storage, operational costs are much lower. 

Cons

• Data lakes require a high level of administration and stewardship to avoid becoming dumping grounds or data swamps of undocumentated data sources. 

• With so much raw data stored in a data lake, security and access control issues can arise. 

Data Lakehouse

A data lakehouse is a more flexible storage option than a data warehouse or data lake. Since it combines the best of both technologies, you can leverage the data quality of the data warehouse and the flexibility of the data lake. A growing ecosystem of data lakehouse providers, including Databricks and BigLake by Google, offers a unified storage engine for efficient storage. 

Pros

• It provides the benefits of multiple storage solutions in a single repository, requiring less time and budget. 
• Unlike data lakes, which require careful transformation for analytics. Data lakehouse provides direct access to broader datasets for analytical and BI tools. 
• Less data redundancy while offering low-cost storage. 

Cons

• Data lakehouse can be more complex to set up and manage than other storage systems. 
• Organizations may face a learning curve while adopting this architecture. 

Data Lakehouse Case Study of Mactores Cognition

Mactores Cognition is a leading modern organization that provides data solutions to multinational businesses. 

One of Mactore’s clients was a large biotech company struggling to manage an on-premise traditional Oracle-based data platform. As the new data sources were added, the company was not able to scale new use cases for the machine learning team, which resulted in fragmented information assets and poor data quality. 

After doing an in-depth eight-week analysis, Mactores decided to transform their existing data pipeline and incorporate an advanced data lakehouse solution. 

The result of that incorporation was 10x agility in their DataOps process and 15x improvement for ML applications. This is because the biotech company’s IT teams were spending less time fixing data quality issues and more time building business-specific ML use cases. 

The improvement in results in the biotech company was largely associated with the data lakehouse approach of unifying data management features and advanced analytics. It also allowed the company to overcome vendor lock-in, performance, and high-cost issues that come with increased proprietary technologies. 

Challenges of Data Lakehouse

Data Lakehouse is the solution to challenges in data lakes and warehouses. However, data lakehouse itself has many disadvantages. 

The main disadvantage of the lakehouse is that it is very new. Everything that this architecture claims has not come to practice on a large scale, which is why it is difficult to see the promised gains in this unified platform. Critics are also claiming that managing computing resources, decoupling storage, and enforcing data governance policies can be complex in this tool.

In addition to this, currently, there is no tool that is entirely dedicated to lakehouse architecture. There are tools like Google Cloud’s BigLake that are trying to automate, but it’s still under development. Therefore, you have to go through a steep learning curve to apply this architecture manually for your data management task needs.

Conclusion

A data lakehouse is a relatively new architecture that handles modern dynamic data. It is an ideal storage system for organizations wanting a unified solution for data management. Data lakehouse features like analytical flexibility, schema evolution, transactional workload, and data governance allow you to harness your data assets’ full potential. Additionally, it eliminates the need for switching between data warehouses and lakes, streamlining the data integration process. However, data lakehouse is still in its early stages of development, so careful consideration is required before making any important organizational decisions. 

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