Unlocking Business Value with Data Lakehouse Examples

A data lakehouse is a game-changer for businesses, offering a flexible and scalable way to store and process data.

By combining the best of data lakes and data warehouses, data lakehouses provide a single platform for both raw and processed data, reducing costs and improving insights.

This approach allows companies to unlock business value by gaining a deeper understanding of their customers, markets, and operations.

With a data lakehouse, businesses can easily integrate data from various sources, making it easier to identify trends and patterns that inform strategic decisions.

A data lakehouse is a centralized repository that combines the benefits of a data lake and a data warehouse.

It's designed to store and process both structured and unstructured data, making it a one-stop-shop for data analysis.

Data lakehouses often use open-source technologies like Apache Hadoop and Apache Spark.

They can be deployed on-premises, in the cloud, or in a hybrid environment.

A data lakehouse typically has a data catalog that provides metadata about the stored data, making it easier to discover and use.

This metadata can include information about data sources, formats, and usage patterns.

A data lakehouse is a game-changer for businesses, offering a centralized data repository that can store and manage structured, semi-structured, and unstructured data. It's a cost-effective solution that reduces engineering and ETL costs by using low-cost cloud storage options.

One of the key features of a data lakehouse is its ability to support ACID (atomicity, consistency, isolation, and durability) properties, ensuring data consistency when multiple users concurrently read and write data. This is achieved through transaction support.

A data lakehouse also provides a unified data platform that combines the functionalities of data warehouses and lakes into a single platform. This integration simplifies data management and accelerates analytics processes.

The open data foundation of a lakehouse is a key benefit, allowing various engines to concurrently work on the same data, enhancing accessibility and compatibility. Data is stored in open file formats like Apache Parquet and table formats such as Apache Hudi, Iceberg, or Delta Lake.

Here are some key features of a data lakehouse:

By using a data lakehouse, businesses can enjoy improved data quality and consistency, thanks to strict schema adherence and transactional consistency. This minimizes write job failures and ensures data reliability.

A data lakehouse architecture is a modular and open design that allows for selection of best-of-breed engines and tools according to specific requirements. This means that the implementation of a lakehouse can vary based on the use case.

The storage layer of a data lakehouse is the data lake layer for all of your raw data, usually a low-cost object store for all your unstructured, structured, and semi-structured datasets. It’s decoupled from computing resources so compute can scale independently.

The data lakehouse architecture consists of three main layers: Storage, Staging, and Semantic. Here's a brief overview of each:

This modular design allows for scalability, flexibility, and adaptability to changing requirements.

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A data lakehouse architecture is a modular and open design that allows for the selection of best-of-breed engines and tools according to specific requirements.

The architecture consists of three main layers: storage, staging, and semantic. The storage layer is the data lake layer for all of your raw data, usually a low-cost object store for all your unstructured, structured, and semi-structured datasets.

The staging layer is the metadata layer that sits on top of your data lake layer, providing a detailed catalog about all the data objects in storage. This layer enables you to apply data management features, such as schema enforcement, ACID properties, indexing, caching, and access control.

The semantic layer, also known as the lakehouse layer, exposes all your data for use, where users can access and leverage data for experimentation and business intelligence presentation.

Here's a breakdown of the layers:

Given the variability in complexity and tool stack, large-scale implementations may require tailored approaches.

Data lakehouses offer a cost-effective way to manage data, leveraging the low-cost storage of cloud-based data lakes while providing sophisticated data management and querying capabilities. This dual advantage makes it an economical choice for startups and enterprises alike.

The storage layer in data lakehouse architecture stores ingested data in low-cost stores like Amazon S3. This decouples object storage from compute, allowing organizations to use their preferred tool or APIs to read objects directly from the storage layer.

A schema-on-write approach, combined with Delta schema evolution capabilities, enables changes to the data without rewriting downstream logic. This means you can make changes to the data layer without affecting the logic that serves data to end users.

Data Management is a crucial aspect of any data storage system. A lakehouse architecture makes it cost-effective by leveraging cloud-based data lakes with sophisticated data management and querying capabilities.

By using a schema-on-write approach, you can make changes to your data without rewriting downstream logic, ensuring efficient data management and accessibility.

The catalog layer plays a key role in upholding data quality and governance standards by establishing policies for data validation, security measures, and compliance protocols.

Unity Catalog and other options like AWS Glue and Hive Metastore can be used to implement the catalog layer, ensuring that data is easily accessible to query engines.

Lakehouses eliminate non-monetary costs associated with running and maintaining ETL pipelines and creating multiple data copies, streamlining operations and reducing costs.

The storage layer in a data lakehouse architecture is where ingested data is stored in low-cost stores like Amazon S3.

This decoupling of object storage from compute allows organizations to use their preferred tool or APIs to read objects directly from the storage layer.

Data is stored in open file formats such as Parquet.

Metadata, which includes the schemas of structured and unstructured datasets, is also stored in the storage layer.

Organizations can read objects directly from the storage layer, giving them flexibility and control over their data.

The ingestion layer is where data from various sources lands in its raw format. This includes transactional and relational databases, APIs, real-time data streams, CRM applications, NoSQL databases, and more.

Tools like Amazon Data Migration Service (Amazon DMS) can be used to import data from RDBMSs and NoSQL databases. Apache Kafka is another option for handling data streaming.

Data lands in a cloud-based low-cost data lake like Amazon S3, where it's stored as raw Parquet files. This allows for a "schema-on-read" approach, where data isn't processed immediately upon arrival.

Once the data is in place, transformation logic can be applied to shift towards a "schema-on-write" setup. This organizes the data for specific analytical workloads like ad hoc SQL queries or machine learning.

Data can be registered according to a data governance model and required data isolation boundaries using Unity Catalog. This allows for tracking the lineage of the data as it's transformed and refined.

Implementing a data lakehouse can be a complex task, especially for large-scale implementations that require tailored approaches.

The modular and open design of data lakehouse architecture allows for the selection of best-of-breed engines and tools according to specific requirements.

This means that the implementation of a lakehouse can vary based on the use case, taking into account factors such as workloads and security.

Large-scale implementations may require a combination of different tools and engines to meet their specific needs.

A data lakehouse can be designed to accommodate a variety of workloads, from batch processing to real-time analytics.

The variability in complexity and tool stack can make it challenging to implement a lakehouse architecture that meets all requirements.

However, with careful planning and a clear understanding of the use case, it's possible to create a data lakehouse that meets the needs of the organization.

Given the variability in complexity and tool stack, a data lakehouse implementation may require a phased approach to ensure that all requirements are met.