Data Warehouse Stocks List

Data Warehouse Stocks Recent News

Date Stock Title
Nov 21 SNOW MongoDB, Other Data Software Stocks Rally On Strong Snowflake Results
Nov 21 SNOW Nasdaq Edges Higher; Baidu Shares Fall After Q3 Results
Nov 21 SNOW Why Are Snowflake (SNOW) Shares Soaring Today
Nov 21 SNOW Why Is Snowflake (SNOW) Stock Soaring Today
Nov 21 SNOW Why Is Datadog (DDOG) Stock Rocketing Higher Today
Nov 21 SNOW Datadog (DDOG) Shares Skyrocket, What You Need To Know
Nov 21 SNOW Here's Why Snowflake Stock Skyrocketed Today
Nov 21 SNOW Snowflake Stock Has Best Day Ever After Earnings. It May Mark the End of the Nvidia Powered Chip Trade.
Nov 21 SNOW Snowflake Stock Soars on Wall Street Beat
Nov 21 SNOW Snowflake Q3 Earnings Beat Estimates, Revenues Rise Y/Y, Stock Up
Nov 21 SNOW Snowflake Stock Explodes Over 30% on AI Growth and Game-Changing Partnership
Nov 21 SNOW Dow Jones Struggles With Little Help From Nvidia After Earnings; Snowflake Is A Big Winner (Live Coverage)
Nov 21 SNOW Q3 2025 Snowflake Inc Earnings Call
Nov 21 SNOW Dow Jones Rises On Surprise Jobless Claims; Nvidia Reverses From Record Highs
Nov 21 SNOW These Stocks Are Moving the Most Today: Nvidia, Alphabet, Snowflake, MicroStrategy, Deere, Palo Alto, PDD, and More
Nov 21 SNOW Stocks to Watch Thursday: Nvidia, MicroStrategy, PDD, Snowflake
Nov 21 SNOW Snowflake shares surge on rosy forecast, AI deal with Anthropic
Nov 21 SNOW Snowflake stock skyrockets by 25% on Q3 beat, guidance raise
Nov 21 SNOW Snowflake builds as it erases year of losses after Q3 earnings
Nov 21 SNOW Snowflake's Q3 Performance Is Just The Tip Of The Iceberg
Data Warehouse

In computing, a data warehouse (DW or DWH), also known as an enterprise data warehouse (EDW), is a system used for reporting and data analysis, and is considered a core component of business intelligence. DWs are central repositories of integrated data from one or more disparate sources. They store current and historical data in one single place that are used for creating analytical reports for workers throughout the enterprise.The data stored in the warehouse is uploaded from the operational systems (such as marketing or sales). The data may pass through an operational data store and may require data cleansing for additional operations to ensure data quality before it is used in the DW for reporting.
The typical extract, transform, load (ETL)-based data warehouse uses staging, data integration, and access layers to house its key functions. The staging layer or staging database stores raw data extracted from each of the disparate source data systems. The integration layer integrates the disparate data sets by transforming the data from the staging layer often storing this transformed data in an operational data store (ODS) database. The integrated data are then moved to yet another database, often called the data warehouse database, where the data is arranged into hierarchical groups, often called dimensions, and into facts and aggregate facts. The combination of facts and dimensions is sometimes called a star schema. The access layer helps users retrieve data.The main source of the data is cleansed, transformed, catalogued, and made available for use by managers and other business professionals for data mining, online analytical processing, market research and decision support. However, the means to retrieve and analyze data, to extract, transform, and load data, and to manage the data dictionary are also considered essential components of a data warehousing system. Many references to data warehousing use this broader context. Thus, an expanded definition for data warehousing includes business intelligence tools, tools to extract, transform, and load data into the repository, and tools to manage and retrieve metadata.

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