What is a data lakehouse?

Data lakehouses solve problems that exist in older data systems. They work well for business analysis and machine learning projects. Companies use data lakehouses to save money and reduce complexity when they want to avoid having separate systems for different types of data. This approach is becoming more popular because it can handle both traditional business reporting and advanced analytics on one platform.

What is the difference between a data lakehouse and a data warehouse?

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A data warehouse and a lakehouse are both data storage systems for storing data that differ in the type of data they store and how this data is used. A data warehouse stores structured data that has been cleaned and organized in advance, while a lakehouse accepts both structured and unstructured data without requiring advance organization. Warehouses use a schema-on-write approach, which means data follows strict rules before it enters the system. Meanwhile, lakehouses use a schema-on-read approach, which allows data to be stored first and structured later when needed.

A warehouse works best for business reporting and analysis, which requires reliable, consistent data. They offer strong performance for business intelligence but lack flexibility for unstructured data and cost more to scale. Data lakehouses support both business intelligence and data science workloads in one system. They handle large amounts of raw data while still providing data management features. Data lakehouses cost less to scale and work better with modern data tools and programming languages.

What are the key features of a data lakehouse?

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Data lakehouses combine advanced data management capabilities with flexible storage options in a single platform to combine the best features of data warehouses and data lakes.

• Transaction support: Ensures data consistency when multiple users make changes simultaneously.
• Data quality enforcement: Prevents bad information from entering the system.
• Metadata layers: Organize and describe data, making it easier to find and use.
• Support for open file formats: Examples include Parquet or ORC, which allow different tools to access data without conversion.
• Integration with BI tools and dashboards: No data movement.
• Raw data access: For data science teams to build machine learning models.
• Version control features: To track changes to data over time.
• Advanced security controls: Protect sensitive information.
• Cloud storage compatibility: Supports cost-effective and scalable operations.

These comprehensive features make data lakehouses suitable for businesses of all sizes that seek to unify their data architecture. By implementing a data lakehouse, companies can reduce complexity while improving both analytical capabilities and operational efficiency.

What is the data lakehouse architecture?

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The data lakehouse architecture consists of several key layers that work together: the storage layer, metadata layer, processing layer, and access layer. The storage layer is the lowest one, and holds raw data in open file formats on low-cost storage systems. Next is the metadata layer, which catalogs and organizes the data without changing its location. The processing layer handles data movement, transformation, and quality checks through tools like Spark or Flink. At the top, the access layer connects users and applications to the data through SQL, APIs, or direct connections.

This architecture includes several important components that facilitate how it works and provide both flexibility and structure. Data ingestion tools bring information into the system, storage management services handle file organization and optimization, query engines analyze data quickly, and data quality enforcement tools check and fix problems with incoming data. Most data lakehouses also include security systems that control who can access different types of information.

What are the advantages and disadvantages of a data lakehouse?

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Data lakehouses offer compelling benefits for businesses that seek unified data platforms. However, they also present implementation challenges that require careful consideration. An understanding of both sides helps companies make informed decisions about adopting this technology.

✅ Advantages include:

• Significant cost savings by reducing duplicate systems and storage requirements.
• Support for both traditional analytics and advanced machine learning in one platform.
• Improved collaboration by making all company data available to more teams.
• Easier scaling compared to traditional warehouses when data volumes grow.
• Better adaptation to changing business needs without major restructuring.
• Strong compatibility with modern data tools that data scientists prefer.

❌ Likewise, there are some disadvantages to data lakehouses:

• Potential performance issues for some specialized workloads compared to pure data warehouses.
• Newer technology with fewer established best practices and experienced professionals.
• Requirements for more custom configuration and tuning to work well.
• Investment needed in training staff on new concepts and tools.
• Complex and time-consuming migration from existing systems.
• Reduced compatibility with some older business intelligence tools compared to traditional warehouses.

Companies must weigh these trade-offs against their specific requirements and capabilities when considering data lakehouse adoption. Success depends on having adequate technical resources and commitment to managing the transition effectively.