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Data Lakehouse

What is a Data Lakehouse?

A data lakehouse is a data repository that combines the best features of data warehouses and data lakes. Enabled by a new, open system design, data lakehouses implement similar data structures and management features to those in a data warehouse directly on the low-cost storage used for data lakes. This approach offers simplicity, flexibility, and affordability, allowing data teams to access and analyze data without switching between multiple systems. Data lakehouses ensure teams have the most complete and up-to-date data for data science, machine learning, and business analytics projects.

Key Technologies Enabling the Data Lakehouse Revolution

Several key technology advancements have facilitated the rise of data lakehouses:

  • Metadata Layers: Metadata layers, like the open-source Apache Iceberg, sit on top of open file formats and track which files are part of different table versions, offering rich management features such as ACID-compliant transactions, streaming I/O support, time travel, schema enforcement, and data validation.

  • High-Performance Query Engines: New query engine designs provide high-performance SQL execution on data lakes. Optimizations include caching hot data in RAM/SSDs, data layout optimizations, auxiliary data structures, and vectorized execution, enabling data lakehouses to achieve performance on large datasets that rivals popular data warehouses.

  • Optimized Access for Data Science and Machine Learning: Open data formats used by data lakehouses (e.g., Parquet) make it easy for data scientists and machine learning engineers to access data. Tools like pandas, TensorFlow, PyTorch, and others popular in the DS/ML ecosystem can seamlessly access data in the lakehouse.

Evolution of Data Architectures: From Data Warehouses to Data Lakes and Beyond

Background on Data Warehouses in Business Intelligence

Data warehouses have a long history in decision support and business intelligence applications, though they were not suited or were expensive for handling unstructured data, semi-structured data, and data with high variety, velocity, and volume.

Emergence of Data Lakes for Data Science and Machine Learning

Data lakes emerged to handle raw data in various formats on cheap storage for data science and machine learning applications. However, they lacked critical features from data warehouses, leading to challenges such as lack of transactions, data quality enforcement, and consistency/isolation.

Overcoming Challenges with Two-Tier Data Architecture

In a two-tier data architecture, data teams have often stitched data warehouses and data lakes together, resulting in duplicate data, extra infrastructure cost, security challenges, and significant operational costs. This approach requires regular maintenance and often results in data staleness, posing concerns for data analysts and scientists.