An Overview of Cloud Data Warehouses, Cloud Data Lakes and Data Lakehouses

The IT environment is constantly changing, but something that has stayed consistent over the years is the need for organizations to perform accurate analysis, create reports and derive results to make critical business decisions. We’ll review how companies are achieving this through cloud data warehouses, cloud data lakes and lakehouses.

Data analytics has expanded beyond business intelligence reporting, and the demand for advanced analytics on a variety of new data sources has increased to reveal deeper insights for predictive modeling, data mining etc. This gave rise to the concept of data lakes. With the evolution of cloud computing, organizations can bring data lakes and data warehouses together to support reporting, exploration, and advanced analytics.

Data lakehouses bring capabilities from both approaches into one architecture, combining the flexibility of a data lake with the structured tables and querying capabilities commonly associated with a data warehouse.

Let’s look at some of the key features of cloud data warehouses and cloud data lakes, and where a data lakehouse fits.

An Overview of Cloud Data Warehouses, Cloud Data Lakes and Data Lakehouses

Cloud Data Warehouse

Cloud platforms such as Azure Synapse Analytics and Microsoft Fabric give organizations options for migrating legacy data warehouses and modernizing their data management architecture.

Cloud data warehouses commonly use massively parallel processing (MPP) to spread analytical workloads across computing resources. Storage and computing capacity can scale depending on demand, within the limits of the platform and the organization’s budget. Some services also allow computing resources to be paused when not in use. With consistent data definitions and quality controls, a cloud data warehouse can provide a single source of truth that helps make business decisions easier.

Here are some cloud data warehouse benefits:

  • Data warehouses are consolidated and widely used for key operational business reporting

  • Highly modeled and used to solve specific set of current problems like using data to explain the answers, check the trends, and share insights across the organization

  • Data elements are well defined and transformed into the right structure to derive required results hence follows a ‘schema-on-write’ approach

  • Supports governed, secure data that business users can understand, provided the organization establishes access controls, data quality standards, and clear ownership.

An Overview of Cloud Data Warehouses

Cloud Data Lake

A cloud data lake is a repository that can retain data in its raw form, allowing multiple potential uses of data from a single load. It can contain unstructured data like emails, social media posts, videos, and audio; semi-structured data like JSON and XML; and structured data from business applications. Modern data lakes can also organize data into raw, cleaned, and curated layers as it is prepared for different uses. Modern data lakes, when managed right, work as a great platform to easily store, load, integrate, and analyze data.

Why consider a cloud data lake?

An Overview of Cloud Data Lakes

  • Data Lake architecture supports storage of denormalized tables or files enabling users to carry out their self-service analytics on the same data source.

  • Can support high-volume, high-velocity data when combined with the right ingestion and processing tools. Streaming analytics requires these additional tools to process incoming data with minimal latency.

  • Opens access to diverse data sources previously unavailable to the business.

  • Extensively used by data scientists to enable exploratory data analysis.

  • Used for discovery purposes, data lakes make it easier to run complex machine learning applications/algorithms.

  • Can be used as a landing area before the data is consumed by the data warehouse for the current reporting needs.

  • Preserves the native form of data thereby storing history and enables addition of newer data.

  • Often follows a ‘schema-on-read’ approach, allowing users to apply structure when they analyze the data. Cleaned and curated layers can also enforce schemas to improve consistency.

  • Can use ETL (Extract, Transform and Load) or ELT (Extract, Load and Transform), depending on where and when the data needs to be prepared. Both approaches are also used with data warehouses.

Where Does a Data Lakehouse Fit?

A data lakehouse combines the flexibility of a data lake with capabilities commonly associated with a data warehouse, such as structured tables, schema enforcement, and reliable transactions. This allows teams to use lake data for business reporting as well as data science and machine learning.

Microsoft Fabric, for example, offers both warehouse and lakehouse options. A warehouse may be a good fit for teams working primarily in SQL on structured reporting data. A lakehouse may suit teams working with a wider variety of data and using tools such as Apache Spark. The choice depends on the workloads, the team’s skills, and how the data will be managed.

An Overview of Cloud Data Lakehouses

Having looked at the features we can conclude that data warehouses and data lakes are both data repositories that store and handle data for various business needs, but serve different purposes. They are usually complementary to each other and are utilized depending on the needs of the organization.

For instance, visualization tools like Power BI can connect to Azure Data Lake Storage (ADLS) to work with supported files, as well as connect to a data warehouse for business reporting. Microsoft Fabric also brings warehouse and lakehouse data together through OneLake, its shared data storage layer.

The right architecture depends on what the organization needs to do with its data. A warehouse can support consistent business reporting, while a lake can preserve diverse data for exploration and future uses. A lakehouse can bring these needs closer together. In each case, data quality, security, governance, and cost management need to be part of the design.

Microsoft Cloud & AI Platforms Partner and Specialist
Microsoft AI Business Solutions Partner

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