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AI and Data Glossary
Data lakehouse

Data lakehouse

A data lakehouse is an emerging architectural pattern that combines the strengths of data warehouses and data lakes. It enables organizations to handle structured and unstructured data in a single platform, offering the analytics capabilities of a data warehouse with the flexibility and scalability of a data lake. By unifying these two paradigms, a data lakehouse eliminates the need for complex data integration and reduces data duplication.

What does a data lakehouse architecture look like?

The architecture of a data lakehouse typically features a single storage layer that supports both structured and unstructured data. This storage layer is complemented by tools for data ingestion, processing, and management. At its core, a lakehouse architecture includes:

  • Open file formats: Allowing interoperability with various analytics tools.
  • Metadata layers: Enabling efficient querying and governance.
  • Processing engines: Supporting batch and real-time data processing.
  • Security and governance: Ensuring compliance and data protection.

How do you build a data lakehouse?

Building a data lakehouse involves several key steps:

  1. Define your data strategy: Assess your organizational needs and identify data types and sources.
  2. Choose the right platform: Evaluate platforms like Databricks, Snowflake, or open-source options.
  3. Set up the storage layer: Implement a scalable and cost-effective storage system.
  4. Integrate data tools: Incorporate tools for data ingestion, transformation, and analytics.
  5. Establish governance: Implement robust security and compliance frameworks.

What are the benefits of a data lakehouse?

A data lakehouse offers several advantages:

  • Unified data management: Consolidates structured and unstructured data in one place.
  • Cost efficiency: Reduces the need to maintain separate data platforms.
  • Enhanced analytics: Combines the real-time capabilities of data lakes with the analytical power of data warehouses.
  • Scalability: Adapts to growing data needs without performance degradation.
  • Simplified architecture: Reduces complexity in data pipelines and integration.

What are some data lakehouse solutions?

Popular data lakehouse solutions include:

  • Databricks Lakehouse: Known for its advanced machine learning and data engineering capabilities.
  • Snowflake: While traditionally a data warehouse, Snowflake is expanding its capabilities to support lakehouse-like features.
  • Google BigLake: Combines Google’s data warehouse (BigQuery) and lake (Cloud Storage) capabilities.
  • Open-source options: Tools like Apache Iceberg and Delta Lake provide open formats and community-driven development.
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FAQ

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Is Databricks a data lakehouse? Is Snowflake a data warehouse or data lakehouse?

Databricks positions itself as a data lakehouse solution, leveraging Delta Lake to provide ACID transactions, governance, and performance optimization. Snowflake, on the other hand, is primarily a cloud data warehouse but is gradually incorporating lakehouse functionalities by enabling semi-structured data handling and integrating with data lakes.

Are there open source data lakehouse options?

Open-source solutions for data lakehouses include:

  • Apache Iceberg: Designed for large-scale data tables with support for SQL queries.
  • Delta Lake: Provides ACID transactions and scalable metadata handling.
  • Apache Hudi: Focuses on real-time data ingestion and upsert operations.

These tools allow organizations to build lakehouses without vendor lock-in.

What is the difference between a data warehouse, data lake, and data lakehouse?
  • Data warehouse: Designed for structured data and optimized for analytics.
  • Data lake: Stores raw, unstructured, and semi-structured data at scale.
  • Data lakehouse: Combines the analytical capabilities of warehouses with the flexibility of lakes, bridging the gap between the two.

In essence, a lakehouse provides a single solution for diverse data types and workloads.

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

A data hub serves as a central repository for integrating and sharing data across systems, focusing on connectivity and interoperability. In contrast, a data lakehouse focuses on storage, management, and analytics. While a hub connects data, a lakehouse unifies and processes it for analysis.

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