Data lakehouse architecture combines the elements of a data warehouse with those of a data lake. It focuses on implementing the data structures of a data warehouse, while also incorporating the management features of data lakes, to create a more cost-effective and competent solution for data storage. Data lakehouses are especially useful for data scientists as they facilitate business intelligence and machine learning.
Owing to the limitations of data warehouses and lakes, multiple systems tend to be used by several companies for data storage. This includes a data lake, multiple data warehouses, and a variety of other specialized systems.
However, doing so results in three major issues, which include lack of openness, limited support for machine learning, as well as lack of flexibility and high costs. All these issues can be solved to a great extent by using the lakehouse architecture.
A lot of companies do operate their data warehouses independently of their data lakes, while some combine their data lake with their data warehouses in a single data platform. But both of these systems can result in multiple complications and high expenses. While on the other hand, a data lakehouse serves as a single platform for data warehousing and Data Lake.
The capacity to derive intelligence from various types of unstructured data , including images, videos, text and audio, makes the handling of such data crucial for all business organizations. Data Lakhouse has emerged as one of the most competent and cost-effective solutions available today that helps companies to access timely data insights.
Some of the major features of the lakehousethis architecturestructure are:
- Transaction support: ACID transactions support across multiple read and write data pipelines ensures consistency as multiple parties concurrently read or write data, typically using SQL.
- Schema enforcement and governance: Enables reasoning about data integrity, and has robust governance and auditing mechanisms.
- Open Source File Formats: It has been developed on standardized and open source file formats like (Delta Lake, Apache Iceberg, Apache Hudi)
- API: Offer an API via which a variety of tools and engines, including machine learning and Python/R libraries, can efficiently access the data directly
- Language Support: Apart from SQL access, the data lakehouse architecture also supports a number of other engines and tools like machine learning and Python/R libraries
- Machine learning support:
- Support for diverse data types: It allows for allowing storing, refining, analyzing, and accessing data for several new applications, including audio, images, video, semi-structured data, and text
- Competent non-SQL direct reads: It provides direct access of large volumes of data for running ML experiments with the usage of R and Python libraries
- Support for DataFrame API: The built-in declarative DataFrame API should be provided with with query optimizations enables data access in ML workloads as ML systems like TensorFlow, PyTorch and XGBoost tend to have adopted DataFrames as the main abstraction for manipulating data
- Data Versioning for ML experiments: ProvideIt provides snapshots of data that helps data science and ML teams to both access and revert to earlier versions of data for the purpose of rollbacks and audits
- High reliability and performance at a budget-friendly price
- Performance optimizations: They Facilitate diverse types of optimization techniques like caching, multi-dimensional clustering, data skipping, and so on
- Schema enforcement and governance: They Provide support for DW schema architectures like star/snowflake-schemas, as well as offer robust auditing and governance mechanisms
- Low-cost storage: Lakehouse architecture is usually developed by using diverse low cost storage options like Google Cloud Storage and Amazon S3