Data Lakehouse

What is a Data Lakehouse?

A data lakehouse combines the flexibility of a data lake with the structured features of a data warehouse, offering a unified platform for storing and analyzing diverse structured and unstructured data. A hybrid data lakehouse architecture leverages on-premises and cloud for data processing and storage.

How have Data Warehouses changed over the past few decades?

Traditional data warehouses were built for structured data residing on-premises. They primarily supported business intelligence (BI) functionalities like reporting and dashboards. Modern data warehouses accommodate a wider range of data formats, support cloud platforms for storage and processing, and integrate data science tools and machine learning algorithms to extract deeper insights from data.

Data warehouses have evolved from siloed, structured data repositories to flexible, cloud-based platforms that support advanced analytics across diverse data format. They are becoming more automated and user-friendly while prioritizing security and data governance. 

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What are Data Lakes?

Data lakes are purpose-built architectures designed to handle and store vast amounts of unstructured and semi-structured data from diverse business applications, systems, and devices across an enterprise. Unlike traditional databases, they can store various data types like raw text, images, and videos, utilizing a file API and cost-effective storage infrastructure.

Their scalability and accessibility make data lakes advantageous for advanced analytics, especially for machine learning and artificial intelligence algorithms. The use of open file formats enhances interoperability, yet it's crucial to note that data lakes inherently lack robust mechanisms for ensuring data integrity and governance.

Without proper management, data lakes may accumulate redundant and disorganized data, leading to "data swamps." Navigating such swamps complicates extracting meaningful insights. Lack of built-in governance necessitates vigilant user oversight to maintain data integrity, metadata, and access controls.

Despite these challenges, data lakes remain valuable in contemporary data architectures, offering a cost-effective solution for processing heterogeneous data. Organizations often deploy additional technologies like data cataloging and metadata management to prevent data swamps, ensuring structured, reliable, and analytically suitable data within the lake.

What are the key differences between a Data Lake, Data Warehouse, and Data Lakehouse?

Data warehouse, data lake, and data lakehouse represent distinct data management architectures, each tailored to specific enterprise data challenges.

Data structure and schema:

  • Data lakes store data in its original format, regardless of structure.
  • Data warehouses are designed for structured data with a predefined schema.
  • Data lakehouses can handle both structured and unstructured data, offering both flexibility while incorporating schema for improved data management.


Data processing:

  • Data lakes store raw, unprocessed data.
  • Data warehouses stored pre-processed, cleaned, and transformed before being stored ensuring it’s ready for analysis.
  • Data lakehouses can store raw data but also allow for processing within the lakehouse environment.

Focus and use cases:

  • Data lakes are ideal for exploratory analysis, uncovering hidden patterns in large datasets, and supporting advanced analytics like machine learning.
  • Data warehouses are built for business intelligence (BI) and reporting. They provide historical data analysis for trends and insights to support strategic decision making.
  • Data lakehouses combine the strength of advanced analytics on all data types while offering some level of data governance for BI purposes.

Cost and scalability: 

  • Data lakes are generally more cost-effective due to storing data in raw formats on lower-cost storage solutions. They are highly scalable to accommodate growing data volumes.
  • Data warehouses can be more expensive due to processing and structured storage requirements. Scalability might be limited compared to data lakes.
  • Data lakehouses offer a balance between cost and scalability. They can be cost-effective for storing raw data but might incur additional processing costs within the lakehouse environment.

How do Data Lakehouses prevent Data Swamps?

Data lakes offer a flexible storage solution for vast amounts of data, but without proper management, they can turn into data swamps – repositories of disorganized, low-quality data that's difficult to analyze. Data lakehouses specifically address this issue by incorporating features from data warehouses to prevent data swamps from forming. Here's how:

Data Governance:

  • Data lakehouses implement data governance practices like defining data ownership, access controls, and data quality standards. This ensures data consistency, accuracy, and traceability, preventing the accumulation of irrelevant or unreliable data in the lake. Data warehouses already have these practices in place.
  • Data lakes, on the other hand, often lack strong governance, leading to uncontrolled data ingestion and potential duplication or errors.


Data Schema:

  • Data lakehouses allow for some schema definition, which helps organize data to a certain extent. This makes data discovery and retrieval easier for analysis compared to completely unstructured data lakes. While it doesn't impose the rigid structure of a data warehouse, it provides a middle ground for better data management.
  • Data lakes store data in its original format, which can be unstructured or semi-structured. While this offers flexibility, it increases the complexity of data exploration and analysis.


Data Lifecycle Management:

  • Data lakehouses facilitate data lifecycle management. This involves processes to identify, classify, and potentially archive or delete data based on predefined rules. This helps prevent the accumulation of irrelevant or outdated data in the lakehouse, keeping it lean and efficient.
  • Data lakes often lack proper data lifecycle management, leading to data sprawl and difficulty in finding valuable insights amongst irrelevant information.


Data Quality Tools:

  • Data lakehouses can integrate with data quality tools to cleanse and validate data during ingestion or within the lakehouse environment. This helps ensure the accuracy and reliability of the data stored in the lake.
  • Data lakes may require separate data cleansing processes before analysis, adding complexity and potential delays in deriving insights.

What are the advantages of a Data Lakehouse?

Data lakehouses offer several advantages that make them a compelling choice for organizations looking to leverage all their data for better decision-making and analytics. Here are some key benefits:


  • Unified Data Platform: Data lakehouses combine the strengths of data lakes and data warehouses, providing a single platform to store and manage all your data, regardless of format (structured, semi-structured, unstructured). This eliminates the need for separate systems for different data types, simplifying data management and making data readily accessible for various analytics projects.
  • Improved Data Governance: Data lakehouses incorporate data governance features from data warehouses. This allows you to define data ownership, implement access controls, and establish data quality standards. This ensures the consistency, accuracy, and traceability of your data, preventing the formation of data swamps – repositories of disorganized, low-quality data that's difficult to analyze.
  • Flexibility and Scalability: Data lakehouses inherit the flexibility of data lakes. You can store all your data "as-is" without worrying about pre-defined structures. Additionally, they offer the scalability of data lakes, easily scaling up or down to accommodate growing data volumes.
  • Support for Advanced Analytics: By storing raw, unprocessed data, data lakehouses provide a foundation for various analytics needs. This data is valuable for training machine learning models and performing other forms of advanced analytics like data mining and artificial intelligence.
  • Cost-Effectiveness: Data lakehouses can be more cost-effective than traditional data warehouses. They often leverage lower-cost storage solutions for raw data and may implement data lifecycle management to remove unnecessary data, reducing storage requirements.
  • Faster Time to Insights: Having all your data in a single, accessible location simplifies data discovery and retrieval. This allows data analysts and data scientists to spend less time searching for data and more time extracting valuable insights.
  • Improved Decision-Making: By providing a holistic view of your data, data lakehouses empower data-driven decision making. You can combine insights from various data sources to gain a deeper understanding of your customers, operations, and market trends, leading to better-informed business decisions.

What are the elements of a Data Lakehouse?

A data lakehouse architecture is a hybrid method of data management that combines the freedom of a data lake with the structured qualities of a data warehouse. It has two significant layers at a high level:


Lakehouse Platform:

  • Direct Query Access: The processing layer provides direct querying of data stored in the lake without the need for it to be loaded into a data warehouse or converted into a proprietary format. This direct access enables BI apps, AI, and ML technologies to make better use of data.
  • Tool agnosticism: Any processing engine can read the data in its original format, giving varied tools and systems the ability to examine the prepared data. This adaptability adds to improved processing and analysis performance and cost-effectiveness.


Processing Layer:

  • Direct Query Access: The processing layer provides direct querying of data stored in the lake without the need for it to be loaded into a data warehouse or converted into a proprietary format. This direct access enables BI apps, AI, and ML technologies to make better use of data.
  • Tool agnosticism: Any processing engine can read the data in its original format, giving varied tools and systems the ability to examine the prepared data. This adaptability adds to improved processing and analysis performance and cost-effectiveness.


Key Principles: ACID Compliance for Database Transactions:

  • Atomicity: Ensures that transactions are either entirely concluded or not handled at all. In the case of a process stoppage, this avoids data loss or corruption.
  • Consistency: Maintains predictable and consistent transaction results while maintaining data validity based on set criteria and keeping overall data integrity.
  • Isolation: Ensures that ongoing transactions are untouched by others until completed, allowing several parties to read and write concurrently without interruption.
  • Durability: Ensures that transaction-induced changes remain even in the face of system failures by enabling persistent storing of transaction-induced changes.


This design balances the cost-effectiveness of a data lake with the flexibility to make ready-to-analyze data available to multiple systems. It allows numerous parties to view and write data simultaneously while adhering to transactional solid rules, making it a versatile and dependable solution for current data management concerns.

AI and Data Lakehouses

Data lakehouses and AI are a powerful combination that can unlock significant value for organizations. Here's how:


AI Benefits from Data Lakehouses:

  • Access to all Data: Data lakehouses store all types of data, structured, semi-structured, and unstructured. This provides AI models with a richer and more comprehensive dataset to train on, leading to potentially better performance and more accurate insights.
  • Improved Data Quality: Data governance features in data lakehouses help ensure data consistency and accuracy. This is crucial for training reliable AI models, as poor quality data can lead to biased or inaccurate results.
  • Flexibility for Experimentation: Data lakehouses support storing raw, unprocessed data. This allows data scientists to experiment with different data preparation techniques and feature engineering approaches to optimize AI model performance.
  • Scalability for Growing Data: As AI models evolve and require more data for training and retraining, data lakehouses can easily scale to accommodate these growing data needs.
  • Cost-Effectiveness: Data lakehouses offer a cost-efficient way to store large amounts of data compared to traditional data warehouses. This allows organizations to experiment with various AI models without worrying about excessive storage costs.


How Data Lakehouses Facilitate AI Projects:

  • Data Preparation: Data lakehouses can integrate with data cleansing and transformation tools, streamlining the process of preparing data for AI models.
  • Model Training and Development: The ability to store and access large datasets within the data lakehouse environment facilitates efficient model training and development cycles.
  • Machine Learning Model Management: Data lakehouses can serve as a central repository for managing different versions of machine learning models and their associated data.
  • Operationalization of AI Models: Once trained, data lakehouses can provide the data pipeline for deploying and serving AI models in production.


Overall, data lakehouses play a critical role in the AI lifecycle by providing a secure, scalable, and well-governed platform for data storage, management, and access. This empowers organizations to build, train, and deploy powerful AI models that drive better decision-making and innovation.

HPE Data Lakehouse Solutions

Data lakehouses are revolutionizing data management by combining the flexibility of data lakes with the data governance of data warehouses. HPE Ezmeral Data Fabric takes this a step further by providing a unified data lakehouse that seamlessly integrates with your existing data infrastructure, both on-premises and in the cloud.


  • Unify and Simplify: HPE Ezmeral Data Fabric breaks down data silos and manages all your data; structured, semi-structured, and unstructured, in a single location. HPE Ezmeral Data Fabric simplifies data governance with built-in tools, ensuring data quality and security for reliable analytics.
  • Power your AI and Analytics: HPE Ezmeral Data Fabric provides a robust foundation for advanced analytics and AI initiatives. It offers easy access to all your data for training machine learning models, conducting in-depth data exploration, and fueling data-driven innovation.
  • Openness and Flexibility: Built on an open-source foundation, Ezmeral Data Fabric avoids vendor lock-in and offers the flexibility to accommodate your specific data needs. Whether you require an on-premises solution, a cloud-based data lakehouse, or a hybrid approach, Ezmeral Data Fabric adapts to your environment.
  • Scalability for Growth: Effortlessly scale your data storage and processing as your data volumes increase. Ezmeral Data Fabric handles growing datasets with ease, ensuring you have the power to analyze all your information.
  • Cost-Effectiveness: Store your data efficiently with Ezmeral Data Fabric's integration with lower-cost storage solutions. This helps you optimize costs without compromising on data accessibility or quality.


HPE Ezmeral Data Fabric empowers customers to:

  • Transform raw data into actionable insights.
  • Make data-driven decisions with confidence.
  • Gain a competitive edge through advanced analytics and AI.


Available on the HPE GreenLake edge-to-cloud platform, this unified data experience allows teams to securely connect to data where it resides today without disrupting existing data access patterns. It includes a scale-up data lakehouse platform optimized for Apache Spark that is deployed on premises. Data scientists are able to leverage an elastic, unified analytics platform for data and applications on-premises, across the edge, and throughout public clouds, enabling them to accelerate AI and ML workflows.