What is a data lakehouse?

Scalability and flexibility in data management

Data lakehouses can seamlessly scale to accommodate growing data volumes across diverse data types, providing businesses with the agility to adapt to changing data landscapes.

Microsoft OneLake in Fabric is an open data lake that can infinitely scale, ingest structured and unstructured data, and process massive amounts of data, all while optimizing performance across analytics engines.

Data lakehouses incorporate robust security measures to safeguard sensitive data. OneLake, for instance, uses industry-leading security and governance tools to ensure the quality of your organization’s data, and that only the right people have the right access to that data. This helps your organization stay compliant with industry regulations and protected against unauthorized access.

Through cost-effective cloud storage and optimized data processing, data lakehouses offer an affordable solution for storing and analyzing large-scale data, both structured and unstructured. Microsoft Fabric further reduces costs by providing a single pool of capacity and storage that can be used for every workload.

By giving data scientists and analysts the ability to perform real-time analytics on streaming data, data lakehouses allow organizations to respond quickly and proactively to changing conditions as they arise. Workloads like Fabric Real-Time Intelligence can ingest and transform streaming data, query in real time, and trigger actions in response.

Data lakehouse architecture consists of several key components that work together to create a unified system for managing and analyzing data. Here’s a detailed breakdown of each component:

1. Ingestion. The ingestion layer is responsible for collecting data from various sources, including databases, applications, IoT devices, and external APIs, both batch and in real time. Fabric Data Factory allows you to implement data flows and pipelines for ingesting, preparing, and transforming data across a rich set of sources. This layer ensures that all relevant data—structured, semi-structured, and unstructured—is available for analysis, providing a comprehensive view of the organization’s landscape.

2. Storage. The storage layer serves as the foundation of the data lakehouse, handling large volumes of raw data using scalable and cost-effective storage solutions. This layer allows data to be stored in its raw format, accommodating various data types, such as text, images, and videos, while eliminating the need for rigid schemas so that the data can be more scalable.

3. Metadata. The metadata layer catalogs data assets and maintains schema information, which ensures data quality for efficient querying. Data teams can understand the context and structure of the data they are working with, resulting in more effective insights.

4. API. The API layer provides the interface that developers, data scientists, and analysts use to access and interact with data. This layer is crucial because it allows different applications and users to work with the data without requiring deep technical knowledge of the underlying architecture.

5. Consumption. The consumption layer encompasses the tools and platforms that give each user the ability to analyze and visualize data. This includes business intelligence (BI) tools like Power BI, as well as data science and machine learning workloads like Fabric Data Science, that use the data stored in the lakehouse. The consumption layer turns raw data into actionable insights, empowering stakeholders across the entire organization to make data-driven decisions.

Whether you’re migrating your data or setting up an entirely new solution, implementing a data lakehouse involves several critical steps. Here’s a step-by-step overview of the process, including key considerations:

1. Assess the landscape. First, you’ll want to identify all your existing data sources, including databases, applications, and external feeds. To understand storage requirements, you’ll want to categorize the data in those sources as structured, semi-structured, or unstructured.

2. Define requirements and objectives. Next, it is essential that you clearly outline your goals, which will help you determine your needs based on anticipated data volume and growth. To protect your sensitive data, you’ll also want to identify the compliance requirements that you’ll need to meet.

3. Choose tech stack. Choose a cloud or on-premises storage solution that supports your data lakehouse needs, then evaluate options for data processing and analytics. You’ll also want to select the tools you’ll be using for cataloging, governance, and lineage tracking.

4. Develop migration strategy. To minimize disruption when developing a migration strategy, you’ll want to plan for a phased migration, starting with less critical data. You should evaluate data quality, identify necessary cleansing or transformation tasks, and establish backup strategies to ensure data integrity.

5. Create pipelines. Once you’ve established your migration strategy, it’s time to set up processes for batch and real-time data ingestion sources using APIs. To further streamline data ingestion, you may also want to consider implementing automation tools, like Microsoft Power Automate, to reduce manual intervention.

6. Configure storage management. When configuring the storage system, you’ll want to do so according to the defined structure for each data type. You’ll need to establish metadata management practices to ensure data discoverability, and you’ll also need to define access permissions and security protocols for safeguarding data.

7. Establish analytics framework. At this point, you’ll want to connect your BI and analytics tools, like Power BI, for reporting and visualization. You’ll also need to provide developers with the necessary frameworks, tools, and access points for machine learning and advanced analytics.

8. Monitor, optimize, and iterate. When you’re done with implementation, you’ll want to regularly assess performance, evaluate storage and processing capabilities using end-to-end monitoring functionality like that found in Fabric. You’ll also want to establish a feedback mechanism with users to identify areas for improvement and optimization.

The world’s top organizations are using data lakehouse architectures to optimize the use of their data, boost decision-making, and drive innovation across operations. Here are a few notable examples of successful implementations:

1. A single source of truth
Netherlands-based food supply chain company Flora Food Group sought to consolidate multiple analytics tools into a single, more efficient platform, so they looked to Fabric to unify their reporting, data engineering, data science, and security channels into one solution. By connecting all their data streams, the company was able to simplify its platform architecture, reduce costs, and offer more detailed and timely insights to its customers, in turn enhancing service delivery and customer satisfaction.

2. Advanced analytics and machine learning
Melbourne Airport, the second busiest airport in Australia, needed to upgrade its data analytics capabilities to improve operational efficiency and passenger experience. By adopting Fabric, the organization was able to consolidate data from a vast range of data sources, including parking, sales, and airport operational systems, as well as expand access to data-driven insights for both technical and non-technical business users. As a result, the airport has gained 30% increased performance efficiency across all data-related operations.

3. AI and deep learning
Digital innovation company Avanade aimed to enhance strategic decision-making processes within their organization using AI technologies. By unifying their data estate with Fabric, and by training over 10,000 employees in data analytics, Avanade lay the foundation for users to more easily adopt AI. Users were able to use the skills they learned to develop customized AI solutions, including different dashboards built on natural language and Copilot in Power BI.

4. Real-time insights
Dener Motorsport, the premier organizer for the Porsche Carrera Cup Brasil, was tasked with providing comprehensive, up-to-date data on car performance and repair to both engineers and patrons alike. By adopting Fabric and implementing its real-time analytics, storage, and reporting features, the organization was able to better support stakeholders with actionable, real-time insights. At a recent race, engineers were even able to identify a failing engine in a Porsche race car, prompting them to remove the car in the interest of safety.