Civil Rights
Movements, leaders, victories and the continuing fight for equality.
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Explore the people, places, events, achievements, struggles and stories that shaped our journey.
Movements, leaders, victories and the continuing fight for equality.
Innovation, patents, science, technology and world-changing contributions.
Pioneers, champions, Negro Leagues, records, activism and excellence.
Meet the people whose lives, choices and achievements shaped the journey.
Black towns, communities, institutions and places where history happened.
Moments that changed communities, movements, institutions and the nation.
In August 1908, a white mob attacked Springfield, Illinois’s Black community, destroying homes and businesses and lynching two Black men. National outrage over the violence helped spur the movement that created the NAACP the following year.
MORE →Reflects the personal views, recollections, and perspective of the author, Mike Davis.
This is a personal recollection on the Move fire on May 13, 1985
Data loading, or simply loading, is a part of data processing where data is moved between two systems so that it ends up in a staging area on the target system.
With the traditional extract, transform and load (ETL) method, the load job is the last step, and the data that is loaded has already been transformed. With the alternative method extract, load and transform (ELT), the loading job is the middle step, and the transformed data is loaded in its original format for data transformation in the target system.
Traditionally, loading jobs on large systems have taken a long time, and have typically been run at night outside a company's opening hours.
Two main goals of data loading are to obtain fresher data in the systems after loading, and that the loading is fast so that the data can be updated frequently. For full data refresh, faster loading can be achieved by turning off referential integrity, secondary indexes and logging, but this is usually not allowed with incremental update or trickle feed.
Data loading can be done either by complete update (immediate), incremental loading and updating (immediate), or trickle feed (deferred). The choice of technique may depend on the amount of data that is updated, changed or added, and how up-to-date the data must be. The type of data delivered by the source system, and whether historical data delivered by the source system can be trusted are also important factors.
Full data refresh means that existing data in the target table is deleted first. All data from the source is then loaded into the target table, new indexes are created in the target table, and new measures are calculated for the updated table.
Full refresh is easy to implement, but involves moving of much data which can take a long time, and can make it challenging to keep historical data.[1]
Incremental update or incremental refresh means that only new or updated data is retrieved from the source system.[2][3] The updated data is then added to the existing data in the target system, and the existing data in the target system is updated. The indices and statistics are updated accordingly. Incremental update can make loading faster and make it easier to keep track of history, but can be demanding to set up and maintain.[1]
Tricle feed or trickle loading means that when the source system is updated, the changes in the target system will occur almost immediately.[4][5]
When loading data into a system that is currently in use by users or other systems, one must decide when the system should be updated and what will happen to tables that are in use at the same time as the system is to be updated. One possible solution is to make use of shadow tables.[6][7]
a data warehouse typically is a collection of historical data designed for decision support, so it is updated from the sources periodically, mostly on a daily basis. today's business however asks for fresher data. real-time warehousing is one of the trends to accomplish this, but there are a number of challenges to move towards true real-time. this paper proposes 'multi-stage trickle and flip' methodology for data warehouse refreshment. it is based on the 'trickle and flip' principle and extended in order to further insulate loading and querying activities, thus enabling both of them to be more efficient.
Source: Wikipedia. Article content is retrieved live through the MediaWiki API.
Data loading, or simply loading, is a part of data processing where data is moved between two systems so that it ends up in a staging area on the target system. With the traditional extract, transform and load (ETL) method, the load job is the last step, and the data that is loaded has already been transformed. With the alternative method extract, load and transform (ELT), the loading job is the middle step, and the transformed data is loaded in its original format for data transformation in the target system. Traditionally, loading jobs on large systems have taken a long time, and have typically been run at night outside a company's opening hours.
The Data Loading and Analysis System (DaLAS) is an electronic database used by the Federal Bureau of Investigation and United States Intelligence Community for counterintelligence and counterterrorism investigations. It is used to store copies of seized digital media, including disk images of CD-ROMs, DVDs, hard drives, mobile phones, and raw network feeds, as well as scans of physical documents. DaLAS supports the upload, processing, and classification of media, and provides a central, remotely accessed, searchable repository of data. The full details of DaLAS, including the number of files and total amount of stored data, are classified. During a 2011 investigation in the aftermath of the 2009 Fort Hood shooting, a query of Nidal Malik Hasan's personal email account returned a result on a hard drive image stored on DaLAS. The drive had been seized in 2007 in an unrelated New Jersey tax case. The match was a message posted to a web forum by Hasan on February 10, 2005, asking whether doctors should prescribe intoxicating medications under Sharia law.
Datavault or data vault modeling is a database modeling method that is designed to provide long-term historical storage of data coming in from multiple operational systems. It is also a method of looking at historical data that deals with issues such as auditing, tracing of data, loading speed, and resilience to change, as well as emphasizing the need to trace where all the data in the database came from. This means that every row in a data vault must be accompanied by record source and load date attributes, enabling an auditor to trace values back to the source. The concept was published in 2000 by Dan Linstedt. Data vault modeling makes no distinction between good and bad data ("bad" meaning not conforming to business rules). This is summarized in the statement that a data vault stores "a single version of the facts" (also expressed by Dan Linstedt as "all the data, all of the time") as opposed to the practice in other data warehouse methods of storing "a single version of the truth" where data that does not conform to the definitions is removed or "cleansed". A data vault enterprise data warehouse provides both a single version of facts and a single source of truth. The modeling method is designed to be resilient to change in the business environment where the data being stored is coming from, by explicitly separating structural information from descriptive attributes. Data vault is designed to enable parallel loading as much as possible, so that very large implementations can scale out without the need for major redesign. Unlike the star schema (dimensional modelling) and the classical relational model (3NF), data vault and anchor modeling are well-suited for capturing changes that occur when a source system is changed or added, but are considered advanced techniques which require experienced data architects. Both data vaults and anchor models are entity-based models but anchor models have a more normalized approach.
In databases, change data capture (CDC) is a set of software design patterns used to determine and track the data that has changed (the "deltas") so that action can be taken using the changed data. The result is a delta-driven dataset. CDC is an approach to data integration that is based on the identification, capture and delivery of the changes made to enterprise data sources. For instance it can be used for incremental update of data loading. CDC occurs often in data warehouse environments since capturing and preserving the state of data across time is one of the core functions of a data warehouse, but CDC can be utilized in any database or data repository system.
Before the 1921 destruction of Tulsa’s Greenwood District, Black residents had created a remarkable center of business and community life. The district included stores, professional offices, entertainment venues and homes owned by Black citizens. Understanding Greenwood means learning what was built—not only what was burned.
MORE →Mae Jemison, aboard Space Shuttle Endeavour in 1992.