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Data Warehouse

What is a Data Warehouse?

A data warehouse is a centralized repository that stores current and historical data from multiple sources in a business-friendly manner for easier insights and reporting. Data warehouses are typically used for business intelligence (BI), reporting, and data analysis, making them essential for businesses looking to optimize their decision-making processes.

Data warehouses enable quick and easy analysis of business data uploaded from operational systems such as point-of-sale systems, inventory management systems, or marketing or sales databases. Data may pass through an operational data store and require data cleansing to ensure data quality before it can be used in the data warehouse for reporting.

What are Data Warehouses Used For?

Data warehouses are used in BI, reporting, and data analysis to extract and summarize data from operational databases. Information that is difficult to obtain directly from transactional databases can be obtained via data warehouses. For example, management wants to know the total revenues generated by each salesperson on a monthly basis for each product category. Transactional databases may not capture this data, but the data warehouse does.

Data Lake vs. Data Warehouse

A data lake is an unstructured or semi-structured data repository that allows for the storage of vast amounts of raw data in its original format. Data lakes are designed to ingest and store all types of data — structured, semi-structured, or unstructured — without any predefined schema. Data is often stored in its native format and is not cleansed, transformed, or integrated, making it easier to store and access large amounts of data.

A data warehouse, on the other hand, is a structured repository that stores data from various sources in a well-organized manner, with the aim of providing a single source of truth for business intelligence and analytics. Data is cleansed, transformed, and integrated into a schema that is optimized for querying and analysis.

Benefits of Data Warehouses

  • Consolidate data obtained from many sources; acting as a single point of access for all data, rather than requiring users to connect to dozens or even hundreds of individual data stores.
  • Historical intelligence. A data warehouse integrates data from many sources to show historic trends.
  • Separate analytics processing from transactional databases, improving the performance of both systems.
  • Data quality, consistency, and accuracy. Data warehouses use a standard set of semantics around data, including consistency in naming conventions, codes for various product types, languages, currencies, and so on.

Challenges with Data Warehouses

  • Not suitable for large unstructured data sets.
  • May require significant transformation before data can be analyzed.
  • Can get expensive quickly, especially if vendors bill for compute-usage.

Why compromise? The Data Lakehouse

The IOMETE Data Lakehouse combines the strengths of data warehouses and data lakes.