In this digital world, data is an important asset; however, organizations are searching for storage solutions that will help them manage big data’s volume, latency, resiliency, and data access requirements. Traditionally, companies used existing tech stacks that delivered the same capabilities as a warehouse or lake but had adjustments in handling massive amounts of semi-structured data. These approaches often resulted in high costs and data duplication across all businesses.
The emergence of data lake houses as a hybrid data architecture aims to deliver better benefits as it eliminates data silos, anticipating unified and Hadoop-based storage for analytics that could consolidate data storage and analysis.
Therefore, for a better understanding of Data Lakehouse, AITech Park brings you this exclusive article where we will talk about the architecture of Data Lake House with a few case studies and application areas.
The Architecture of a Data Lakehouse
We are well aware that Data Lake House is a flexible storage with all the data management features that can handle massive amounts of data of various types, from structured to semi-structured and unstructured, while ensuring data governance, quality, and reliability. However, the data lake house is incomplete without discussing its architecture.
Metadata Layer
The metadata layers act as a data lake house catalog that helps in managing information about data stored within the structure, format, and lineage. This layer supports data governance and access control, a unified view of data assets, making it easier for users to find the understandable information that they might need.
Processing Layer
This is where the data transformation takes place, as it involves cleaning, enriching, and transforming raw data into a more usable format for analysis. Utilizing processing engines such as Apache Spark or Databricks, this layer can handle both batch processing for large-scale data sets and real-time processing for essential insights.
Governance and Security Layer
To run the data lakehouse, data security, and governance are important to ensure data integrity, quality, and compliance with privacy regulations; they help in protecting against unauthorized access. This also encompasses policies and mechanisms that aid in data access, control, auditing, and ensuring that data usage is adhered to all organizational standards and legal processes.
With time, the Data Lake House architecture has become more flexible and powerful as it enables companies to gain insights from large datasets and further efficiently manage data to make data-driven decisions faster. This transmission also introduces data observability that will play an important role in monitoring and maintaining the data quality of the datasets within the lakehouse.
To Know More, Read Full Article @ https://ai-techpark.com/the-introduction-of-data-lakehouse-architecture/
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