Agile Solutions Releases New Guide on Cloud Data Lakehouse

    Milton Keynes, UK, Jan 23 — Leading Data Management expert, Agile Solutions, recently released a guide on what Cloud Data Lakehouse is and the benefit it offers.

    The business explains the difference between a Data Warehouse, a Data Lake, and a Data Lakehouse. 

    A Data Warehouse is a repository for structured data, used to support business intelligence. However, since it only stores structured data, a Data Warehouse comes with certain limitations, such as more storage space required to organize information. For that reason, it also costs more.

    In contrast, Data Lakes are databases that store semi-structured or unstructured data. Since the information is not organized, it requires less storage space and can be accessed quickly. Also, this type of storage costs less.

    However, because it is not structured and organized, it is difficult to extract meaningful information from it.

    A Data Lakehouse, explains the company, lies between these two extremes. While it has the capability to store unstructured and semi-structured data, it also has the power to glean data from it for analytics.

    Unlike a Data Warehouse, where the data is organized, a Data Lakehouse assigns a metadata layer over the stored information. As a result, storing the information is cost effective, yet allows business intelligence reporting and visualization.

    Consequently, Agile Solutions statesthat it’s possible to extract and analyze this information without having to use two different systems. More importantly, this raw and unstructured Data Storage system can be ideal for Machine Learning and Artificial Intelligence integration.

    The business goes on to explain how a Cloud Data Lakehouse is builtand that the architecture can vary based on a business’s needs. Similar to the Data Warehouse and Data Lakes, it will be composed of ingestion, storage, processing, and consumption layers.

    However, it will also have an additional metadata (Data Catalogue) layer. This layer is responsible for storing information about each object in the Cloud Data Lakehouse architecture.

    Since a Cloud Data Lakehouse is effectively Cloud Data Lake storage running under a Cloud Data Warehouse processing capability, it provides a central space for storing all business data. 

    Whether the information is structured, semi-structured, or unstructured, it can all be housed and analyzed together. The raw information does not need to be modified in order to make it compatible with a storage system.

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    As a result, a business can extract detailed information from this raw information for better Data Analytics, the company claims.

    Since a business no longer needs two different Data Storage systems, Data Lakehouse enables the creation of a better, more robust, governance framework.

    Another benefit Data Lakehouse offers is that, since all of the business’ data is in one place, data scientists can construct and carry out their own learning initiatives.

    Data Lakehouse also helps in enabling ACID—or Atomicity, Consistency, Isolation, and Durability—transactions. This is a data transaction model where each transaction is complete and always consistent.

    In short, the list of benefits cited is not exhaustive. However, Cloud Data Lakehouse can be a key aspect in maintaining DataIntegrity and reliability during a business’ Digital Transformation.
    As the provider of data advice, support, and delivery services, Agile Solutions helps businesses ensure that their data reaches its full potential. To learn more about its solutions and the services it offers, please visit

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