Sep 10, 2017 · LO: 11.5: Describe the meaning of big data and the demands big data will place on data management technology. Difficulty: Difficult Classification: Concept AACSB: Information Technology 23) When a data repository (including internal and external data) does NOT follow a predefined schema, this is called a: A) data dump. B) data ocean. C) data lake.
Oct 31, 2017 · When a data repository (including internal and external data) does NOT follow a predefined schema, this is called a: data stream. data dump. data ocean. data lake. 4 points Question 10 1. An organization that decides to adopt the most popular NoSQL database management system would select: Redis.
Mar 05, 2020 · A data lake is a data warehouse without the predefined schemas. As a result, it enables more types of analytics than a data warehouse. Data lakes are commonly built on big data platforms such as Apache Hadoop. See the following video …
LO: 11.5: Describe the meaning of big data and the demands big data will place on data management technology. Difficulty: Difficult Classification: Concept AACSB: Information Technology 23) When a data repository (including internal and external data) does NOT follow a predefined schema, this is called a: A) data dump. B) data ocean. C) data lake.
Schemas are ways in which data is organized within a database or data warehouse. There are two main types of schema structures, the star schema and the snowflake schema, which will impact the design of your data model.
Snowflake schema: While not as widely adopted, the snowflake schema is another organization structure in data warehouses. In this case, the fact table is connected to a number of normalized dimension tables, and these dimension tables have child tables.
A data warehouse, or enterprise data warehouse (EDW), is a system that aggregates data from different sources into a single, central, consistent data store to support data analysis, data mining, artificial intelligence (AI), and machine learning. A data warehouse system enables an organization to run powerful analytics on huge volumes ...
OLAP (for online analytical processing) is software for performing multidimensional analysis at high speeds on large volumes of data from unified, centralized data store, like a data warehouse. OLTP, or online transactional processing, enables the real-time execution of large numbers of database transactions by large numbers of people, typically over the internet. The main difference between OLAP and OLTP is in the name: OLAP is analytical in nature, and OLTP is transactional.
The main difference between OLAP and OLTP is in the name: OLAP is analytical in nature, and OLTP is transactional. OLAP tools are designed for multidimensional analysis of data in a data warehouse, which contains both historical and transactional data. Common uses of OLAP include data mining and other business intelligence applications, ...
Common uses of OLAP include data mining and other business intelligence applications, complex analytical calculations, and predictive scenarios, as well as business reporting functions like financial analysis, budgeting, and forecast planning.
A data lake is a data warehouse without the predefined schemas. As a result, it enables more types of analytics than a data warehouse. Data lakes are commonly built on big data platforms such as Apache Hadoop.
The relational database model is the most used database model today. However, many other database models exist that provide different strengths than the relational model. The hierarchical database model, popular in the 1960s and 1970s, connected data together in a hierarchy, allowing for a parent/child relationship between data. The document-centric model allowed for a more unstructured data storage by placing data into “documents” that could then be manipulated.
A relational data model is easy to understand and use. In a relational database, data is organized into tables ( or relations ). Each table has a set of fields which define the structure of the data stored in the table.
Some other examples of data are: an MP3 music file, a video file, a spreadsheet, a web page, a social media post, and an e-book.
Databases can be organized in many different ways by using different models. The data model of a database is the logical structure of data items and their relationships. There have been several data models. Since the 1980s, the relational data model has been popularized.
The term refers to such massively large data sets that conventional data processing technologies do not have sufficient power to analyze them. For example, Walmart must process millions customer transactions every hour across the world.
The term business intelligence is used to describe the process that organizations use to take data they are collecting and analyze it in the hopes of obtaining a competitive advantage. Besides using their own data, stored in data warehouses (see below), firms often purchase information from data brokers to get a big-picture understanding of their industries and the economy. The results of these analyses can drive organizational strategies and provide competitive advantage.
Data is a valuable resource in the organization. However, many people do not know much about database technology, but use non-database tools, such as Excel spreadsheet or Word document, to store and manipulate business data, or use poorly designed databases for business processes.