Cloud Data Lakehouse

First came Data Warehouses, a place to store structured data that supported business intelligence. Next came Data Lakes, the home of semi-structured and unstructured data - too messy to be contained in the traditional warehouses. 

Both systems have their place, but in today’s fully data-driven landscape, where accessibility and quality matter above all, managing Big Data requires a blend of the two. Combining both of these with the power of the Cloud brings flexibility to match storage and computing to current and future needs.

Enter the Cloud Data Lakehouse. 

What is Data Lakehouse?

The more insights you wish to glean from data, the more sophisticated the storage must be to house it. A Cloud Data Lakehouse offers this assistance, as it pulls together the flexibility of a Data Lake, with the analytical power of Data Warehouses and the power, scalability, flexibility and cost-effectiveness of the Cloud. 

Though the same can be achieved through implementing a warehouse and a Data Lake separately, you run the risk of errors, inconsistencies, and miscommunications. 

Not to mention that creating, implementing, and maintaining two separate storage locations for your data causes costs to rise, plus the overhead of moving data between the two. 

That’s the beauty of a Cloud Data Lakehouse. It is built to house structured, semi-structured and unstructured information like files, images, videos, text, and audio - creating a low-cost storage facility that can be used by business intelligence reporting, visualizations, data engineers and data scientists alike. 

They are able to access the information they need without using multiple systems - perfect for machine learning and artificial intelligence integration. 

How is Data Lakehouse made?

The architecture of a Cloud Data Lakehouse can vary depending on your needs. In general, it takes the best features of both Data Warehouses and Data Lakes to create a synergistic solution.  

Like its predecessors, a Cloud Data Lakehouse will consist of ingestion, storage, processing, and consumption layers. 

But the one thing that sets it apart from stitching two separate systems together is the metadata (Data Catalogue) layer. The metadata layer provides information about all objects in your Cloud Data Lakehouse architecture - including the name, type, size, and nature of all data ingested and stored.

This allows the implementation and use of Data Management systems, improving the output quality of your entire data pipeline. 

The benefits of a Cloud Data Lakehouse

As a Cloud Data Lakehouse is effectively a Cloud Data Warehouse processing capability built on top of your Cloud Data Lake storage, it becomes a single space for all your data while enabling further processing, high-level machine learning, business intelligence, and streaming capabilities. 

It also means you do not need to move or restructure any data, however, the benefits aren’t exhaustive:

Improved Data Analytics

As data teams will have detailed information on raw and processed data without the need to extract or manually categorize it, thanks to the metadata layer, they can carry out improved analytics directly from your data storage.  

Aids in the creation of a robust Data Governance framework

Your entire data architecture is ruled by your Data Governance framework, and by implementing a Cloud Data Lakehouse you do not need to create different rules for different systems.

Provides a central location to construct Data Science initiatives

As all data is stored here, with the help of metadata, data scientists can construct and carry out their own learning initiatives. 

Creates a home for semi-structured and unstructured data to exist 

Semi-structured files like JSON, XML, CSV, Parquet, as well as unstructured information such as  video, images, raw text, and audio no longer need to be altered and structured to fit into a rigid warehouse. This freedom allows for deeper insights in the future.  

Enables ACID transactions

ACID stands for Atomicity, Consistency, Isolation, and Durability - and when in place, every data transaction is complete and will ensure it never falls into an inconsistent state. Modern Cloud Data Lakehouses are now able to support this mission-critical data handling capability.

This list is not exhaustive, but if part of your initiative is to achieve the highest possible data integrity and reliability, then a Cloud Data Lakehouse is a key aspect in your data transformation.  

The Agile Approach with Data Lakehouse

Our approach begins with analyzing your business needs. Together with our consultants, we are able to pinpoint the goals you wish to achieve with the system. Through our partners Amazon WebServices, Microsoft Azure, and Snowflake, we’re able to design and deliver your Cloud Data Lakehouse or Warehouse that is aligned for present and future goals.

Throughout the process, our consultants will advise as well as help you manage ongoing operations and enable your own staff to take on operational management. We bring the expertise and skills in tailoring our Partners’ products to meet your specific needs, and ensuring your organization is ready to maximize the business benefit.