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Data Lakehouse

Data Lakehouse

By: A Staff Writer

Updated on: May 19, 2023

Data Lakehouse

The article delves in-depth into Data Lakehouse – the latest data storage and management concept evolution. The ever-growing technological capabilities in data management have given rise to numerous innovative solutions, including the data lakehouse. However, despite the buzz around it, the concept may not be entirely clear. Let’s explore the definition, use cases, architecture, challenges, pitfalls, and best practices.

Defining Data Lakehouse

The data lakehouse is a new kind of data architecture that combines the best elements of two traditional data architectures: data lakes and data warehouses. The objective is to provide businesses with a unified platform to support big data analytics and machine learning alongside more traditional business intelligence (BI) and reporting.

Data lakes are designed to store vast amounts of raw, unprocessed data, usually in a semi-structured or unstructured format. On the other hand, data warehouses hold structured, cleansed, and processed data ideal for analytical querying and reporting.

A data lakehouse seeks to offer the benefits of both systems, combining the scalability and flexibility of data lakes with the strong governance, reliability, and performance of data warehouses. The result is a unified, versatile platform that handles diverse data processing and analytics workloads.

Differences between Datawarehouse, Data Lake, and Data Lakehouse:

While Data Warehouses, Data Lakes, and Data Lakehouses may seem similar at first glance due to their roles as data storage and management solutions. However, they significantly differ in structure, functionality, and purpose.

Let’s delve into the specifics:

Data Warehouse

A Data Warehouse is a large, centralized data repository that supports business intelligence (BI) activities, particularly analytics and reporting. It primarily stores structured data that adheres to a predefined schema or model, such as relational databases.

Key Features:

  • Data is typically organized, cleaned, transformed, and cataloged before storage.
  • Supports SQL (Structured Query Language) and provides fast query performance.
  • Built for a single version of the truth – consistent, quality data that aid in decision-making processes.
  • Due to its emphasis on structured data, it may not handle semi-structured or unstructured data efficiently.

Data Lake

Contrarily, a Data Lake is a vast repository that stores “raw,” unprocessed data in its native format, encompassing structured, semi-structured, and unstructured data. It is designed for big data and machine learning purposes.

Key Features:

  • Data lakes are schema-on-read, meaning data can be stored in its native format, and structure is only imposed when reading the data for analysis.
  • Designed to store massive volumes of data, offering more scalability than traditional data warehouses.
  • Potentially useful for data scientists and machine learning engineers who need access to raw data.
  • However, a lack of proper governance can lead to a “data swamp” – disorganized and difficult-to-navigate data resources.

Data Lakehouse

A Data Lakehouse is a relatively new approach designed to merge the benefits of both data warehouses and data lakes. It maintains a data lake’s raw data storage scalability but also integrates a data warehouse’s data management features and performance.

Key Features:

  • Data is stored similarly to a data lake, including structured, semi-structured, and unstructured data.
  • Provides schema enforcement at the time of data ingestion (schema-on-write) along with the schema-on-read capabilities, offering a cleaner, more organized version of a data lake.
  • Supports various data processing and analytics workloads, including those for machine learning and BI.
  • Enhances data governance with data quality checks, lineage tracking, cataloging, and data access control capabilities.

Use Cases

The data lakehouse can be highly beneficial for numerous applications, including:

  • Data Science and Machine Learning: To train their models, Data scientists and machine learning engineers must access large volumes of raw data. A data lakehouse provides a platform to store this data and supports the powerful processing frameworks required for these tasks.
  • Business Intelligence: A data lakehouse can also handle structured data queries essential for BI applications. This allows for reliable, accurate reporting and analytics, leveraging the data stored in the lakehouse.
  • Real-Time Analytics: Data lakehouses can support real-time or near-real-time analytics. This is particularly useful for applications that require immediate insights, such as fraud detection, supply chain management, or social media monitoring.


In a typical data lakehouse architecture, data is ingested from various sources, such as transactional databases, log files, IoT devices, etc. This data is stored in a data lake’s raw, unprocessed form, typically built on a scalable, distributed file system like Hadoop HDFS or cloud storage like Amazon S3.

However, unlike a traditional data lake, in a data lakehouse, data undergoes schema enforcement and data quality checks at the time of ingestion, known as schema-on-write. This is in addition to schema-on-read capabilities native to data lakes. This means that data in the lakehouse is already cleansed and structured, ready for querying.

For analytics and machine learning tasks, data is read from the lakehouse using a variety of processing engines. These can range from big data processing frameworks like Apache Spark to SQL engines for structured data queries.

Data governance is another key feature of the data lakehouse. Metadata about the stored data is collected and managed to ensure data consistency, traceability, and discoverability. This can involve cataloging data, tracking data lineage, and implementing data access controls.

Challenges and Pitfalls

While a data lakehouse provides numerous benefits, it also comes with its own set of challenges and pitfalls:

  • Data Governance: While the data lakehouse is designed to improve data governance, implementing this effectively can be challenging. Creating a “data swamp” is risky without careful management – a lakehouse full of disorganized, inconsistent, or redundant data.
  • Complexity: Integrating the features of both data lakes and data warehouses into a single platform can lead to increased complexity. This can make setting up, managing, and using the lakehouse more challenging.
  • Performance: Balancing the diverse workload requirements of big data processing, machine learning, and structured data querying can be difficult. There can be a risk of suboptimal performance if the lakehouse is not appropriately designed and managed.
  • Security and Compliance: Given the sensitive nature of some of the data stored, maintaining security and compliance is a crucial challenge. Strict data access controls and audit trails should be implemented, and data encryption should be used where necessary.

Best Practices

To overcome the challenges associated with implementing a data lakehouse and ensuring its practical use, the following best practices should be followed:

  • Implement Strong Data Governance: A robust data governance framework should be established from the beginning. This includes cataloging data, enforcing schemas, tracking data lineage, and setting data access controls.
  • Balance Flexibility and Control: Try to balance, allowing users to perform diverse tasks and maintain data consistency and reliability control.
  • Leverage Cloud Technologies: Using cloud storage and compute resources can help manage the scalability and performance requirements of the data lakehouse. Many cloud providers also offer built-in tools for data governance and security.
  • Invest in Skills and Training: Ensure your team can effectively manage and use the data lakehouse. This can involve training in specific technologies and frameworks and more general data management and analytics skills.

In conclusion, the data lakehouse presents an innovative approach to managing and analyzing data by combining the best of both worlds: the flexibility and scalability of data lakes and the reliability and governance of data warehouses. Furthermore, businesses can make better-informed decisions about adopting this emerging technology by understanding its use cases, architecture, challenges, and best practices.

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