A data lakehouse is a hybrid data storage and analytics architecture that combines the features of a data lake and a data warehouse. It addresses the limitations of traditional data warehouses and data lakes, providing a unified platform for storing and analyzing structured and unstructured data. Here’s a breakdown of the key components to help you understand the differences between a data lake, data warehouse and a data lakehouse and the role of data lakehouses in analytics:
Data Lake vs Data Warehouse vs Data Lakehouse
Data Lake
Storage for Raw Data
Data lakes serve as vast repositories capable of storing massive amounts of raw and diverse data types, such as text, images, videos, logs, and more. Unlike traditional databases that require a predefined schema, data lakes embrace a schema-on-read approach. This means data can be ingested without the need for upfront structuring, providing a central location for organizations to store data in its raw and original form.
Schema-on-Read
The schema-on-read paradigm allows for greater flexibility in handling unstructured and semi-structured data. In a data lake, data is stored in its native format, and the schema is applied only when the data is accessed or queried. This approach is particularly beneficial when dealing with data sources where the structure may evolve over time or when exploring new datasets. It enables organizations to store diverse data sets without the need for extensive upfront modeling, making data lakes suitable for handling the volume and variety of data generated in modern business environments.
Data Warehouse
Structured Data Processing
Data warehouses, in contrast to data lakes, are optimized for processing and analyzing structured data. Structured data, with a fixed and predefined schema, is organized into tables and optimized for complex query processing. This makes data warehouses highly efficient for tasks such as business intelligence, reporting, and decision support where structured data is the primary focus.
High Performance
Data warehouses are designed for high-performance analytics. They employ techniques such as indexing, partitioning, and pre-aggregation to optimize query response times. By structuring data in a way that facilitates quick retrieval and analysis, data warehouses are well-suited for scenarios where rapid and consistent access to structured information is critical. This performance optimization is especially valuable for applications requiring real-time or near-real-time analytics, enabling organizations to make data-driven decisions with minimal latency.
Data Lakehouse
Unified Platform
A data lakehouse aims to integrate the best features of data lakes and data warehouses into a unified platform. Leveraging technologies like Delta Lake, it provides a common space for storing both raw, unstructured data from data lakes and structured, processed data akin to data warehouses. This integration bridges the gap between the flexibility of data lakes and the structured analytics capabilities of data warehouses, offering a comprehensive solution for organizations dealing with diverse and evolving data sources.
Schema Evolution
One significant advantage of a data lakehouse is its support for schema evolution. In traditional data warehouses, changing data schemas often necessitates a labor-intensive process of reloading and restructuring the entire dataset. In a data lakehouse, schema evolution allows for the seamless modification of data structures without the need for a full reload. This adaptability is crucial in dynamic environments where data sources evolve over time, enabling organizations to incorporate changes without disrupting ongoing analytics processes.
How Data Lakehouse Analytics Work
Ad Hoc Analysis
Data lakehouses empower analysts and data scientists to perform ad hoc analysis on raw, unstructured data. This capability is particularly valuable for exploratory data analysis, allowing users to delve into the data without the constraints of predefined structures. Analysts can uncover insights, patterns, and trends in the raw data, providing a more comprehensive understanding of the information at hand.
SQL Support
Many data lakehouses support SQL queries, making it easier for users familiar with SQL to interact with the data. This SQL support facilitates a seamless transition for users accustomed to working with traditional data warehouses, enabling them to apply their existing skills to analyze and query data in the unified environment of a data lakehouse. This feature enhances the accessibility of the platform and fosters collaboration among teams with varying levels of technical expertise.
A data lakehouse combines the flexibility of data lakes with the performance and structure of data warehouses, providing a comprehensive solution for modern analytics needs. It allows organizations to handle a wide variety of data types, support evolving schemas, and deliver high-performance analytics for business intelligence and data-driven decision-making.
Oracle Fusion Analytics Warehouse (FAW), a packaged service combining Oracle Analytics Cloud (OAC) and powered by Oracle ADW, operates on Oracle Cloud Infrastructure (OCI) and integrates with various infrastructure services. It extracts and loads data from Oracle Cloud Applications into an Oracle ADW instance, utilizing OAC to customize or create dashboards. Comprising a data pipeline, data warehouse, semantic model, and prebuilt content like KPIs and dashboards, Oracle manages the service from deployment to maintenance. Positioned as the analytics layer atop multiple cloud applications, both Oracle and non-Oracle, FAW enables the consolidation of diverse data models into a shared instance of ADW. Users can avoid the need for a dedicated IT team as Oracle handles automated management, ensuring seamless updates and security patches without impacting business operations.
