Data Lakehouse in Action: Architecting a modern and scalable data analytics platform

The Data Lakehouse architecture is a new scalable data architecture paradigm that addresses the limitations of current patterns. It combines multiple patterns based on an organization's needs and maturity level,using cloud computing platforms like Azure. The book covers the principles,components,and practical implementation of the architecture,including scenarios and components for data ingestion,storage,processing,serving,analytics,governance,and security. It is for data architects,big data engineers,data strategists,and practitioners looking to enable large-scale analytics.

Format: Paperback / softback
Length: 206 pages
Publication date: 17 March 2022
Publisher: Packt Publishing Limited

The Data Lakehouse architecture is a novel approach to data architecture that addresses the limitations of current patterns. It provides a scalable and efficient way to store and analyze large amounts of data, making it ideal for modern data analytics applications.

Key Features:

Ingestion: The Data Lakehouse architecture allows for the ingestion of data from various sources, including databases, sensors, and social media platforms. This data is stored in a centralized repository, making it easy to access and process.

Storage: The Data Lakehouse architecture uses a combination of object storage and file storage to store data. Object storage is used for large, unstructured data, while file storage is used for structured data. This allows for efficient storage and retrieval of data.

Processing: The Data Lakehouse architecture uses a distributed processing framework, such as Apache Spark or Apache Hadoop, to process data. This allows for fast and scalable processing of large datasets, making it ideal for machine learning and data science applications.

Serving: The Data Lakehouse architecture uses a data lake as a central repository for data. This data can be accessed by various analytics tools and applications, making it easy to perform data analysis and visualization.

Governance: The Data Lakehouse architecture provides a centralized governance framework for data management. This includes policies and procedures for data access, security, and retention.

Security: The Data Lakehouse architecture emphasizes data security and privacy. It uses encryption, access controls, and other security measures to protect sensitive data.

Implementation: The Data Lakehouse architecture can be implemented using cloud computing platforms, such as Amazon Web Services (AWS), Microsoft Azure, or Google Cloud Platform (GCP). These platforms provide the infrastructure and tools needed to build and manage a Data Lakehouse.

Benefits:

Scalability: The Data Lakehouse architecture is highly scalable and can handle large amounts of data. It can easily expand to accommodate growing data volumes and processing requirements.

Efficiency: The Data Lakehouse architecture uses a distributed processing framework, which allows for efficient processing of large datasets. This reduces the time and resources required to analyze data and enables faster decision-making.

Flexibility: The Data Lakehouse architecture is highly flexible and can be customized to meet the specific needs of different organizations. It can be integrated with other data analytics tools and applications, making it easy to build complex data pipelines.

Cost-effective: The Data Lakehouse architecture is cost-effective compared to traditional data warehousing solutions. It eliminates the need for expensive hardware and software and allows for efficient data storage and processing.

Overall, the Data Lakehouse architecture is a promising approach to data architecture that provides a scalable, efficient, and flexible way to store and analyze large amounts of data. It is ideal for modern data analytics applications and is expected to become the dominant data architecture paradigm in the future.

Weight: 392g
Dimension: 188 x 233 x 16 (mm)
ISBN-13: 9781801815932