Data Warehouse Stocks List
|2019-09-13||DWS||Narrow Range Bar||Range Contraction|
|2019-09-13||GLH||Crossed Above 20 DMA||Bullish|
|2019-09-13||GLH||Crossed Above 50 DMA||Bullish|
|2019-09-13||GLH||Shooting Star Candlestick||Bearish|
|2019-09-13||GLH||Doji - Bearish?||Reversal|
|2019-09-13||GLH||Lizard Bearish||Bearish Day Trade Setup|
|2019-09-13||GLH||Gilligan's Island Sell Setup||Bearish Swing Setup|
|2019-09-13||GLH||MACD Bullish Centerline Cross||Bullish|
|2019-09-13||GLH||Pocket Pivot||Bullish Swing Setup|
|2019-09-13||PPT||Bollinger Band Squeeze||Range Contraction|
|2019-09-13||PPT||Non-ADX 1,2,3,4 Bearish||Bearish Swing Setup|
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.