Data Types
typically stored in a table with relationships between the different rows and columns, like in a spreadsheet or database. Highly organized and well-defined.
Structured data
organized into a hierarchy, but without full differentiation or any particular ordering. Although this data type doesn’t have a formal structure, it contains tags or other markers that make it easier to analyze than unstructured data.
Semi-structured data
information that either doesn’t have a predefined data model or isn’t organized in a predefined manner.
Unstructured data
an organized collection of data stored in tables and accessed electronically from a computer system.
database
stores and provides access to data points that are related to one another. Highly consistent, reliable, and best suited for dealing with large amounts of structured data.
relational database
Less structured in format and doesn’t use a tabular format of rows and columns. Follows a flexible data model, which makes them ideal for storing data that changes its organization frequently or for applications that handle diverse types of data.
non-relational database (NoSQL database)
is a place to store data for analysis. The central hub for all business data.
data warehouse
A repository designed to ingest, store, explore, process, and analyze any type or volume of raw data, regardless of the source, like operational systems, web sources, social media, or Internet of Things, or IoT.
data lake
the proprietary customer datasets that a business collects from customer or audience transactions and interactions.
First-party data
describes data from another organization, such as a partner or other business in their supply chain, that can be easily deployed to augment a company's internal datasets. The organization does not directly own this data, but it’s relevant to their business.
Second-party data
datasets collected and managed by other organizations that don’t directly interact with a company's customers or business.
third-party data
the initial creation of a unit of data; this could be a click on a website, the swipe of a card, a sensor recording from an IoT device, or countless other examples.
Data genesis
to extract data from the system in which it’s hosted and bring it to a new system.
ingestion
is where the collected raw data is transformed into a form that’s ready to derive insights from.
Data processing
is where the data lands, can be found, and is ready for analysis and action.
Data storage
means setting internal standards—data policies—that apply to how data is gathered, stored, processed, and disposed of.
Data governance
provides direction for business-oriented action.
Data analysis
Data pushed to the relevant business procedures and decision makers so that action can be taken and the value chain completed. Common examples are applications that make automated decisions and business intelligence dashboards that guide humans toward better, more informed decisions.
data activation
stored in a packaged format that contains the binary form of the actual data, and relevant associated metadata–such as creation date, author, resource type, and permissions–and a globally unique identifier.
object storage
considered best for frequently accessed, or “hot,” data. It’s also great for data that’s stored for only brief periods of time. (a storage classes in Google Cloud Storage.)
Standard storage
This option is best for storing infrequently accessed data, like reading or modifying data on average once a month or less. Examples might include data backups, long-tail multimedia content, or data archiving. (a storage classes in Google Cloud Storage.)
Nearline storage
This is also a low-cost option for storing infrequently accessed data. Meant for reading or modifying data, at most, once every 90 days. (a storage classes in Google Cloud Storage.)
Coldline storage
This is the lowest-cost option, used ideally for data archiving, online backup, and disaster recovery. (a storage classes in Google Cloud Storage.)
Archive storage
Which strategy describes when databases are migrated from on-premises and private cloud environments to the same type of database hosted by a public cloud provider?
Lift and shift
fully managed relational databases, including MySQL, PostgreSQL, and SQL Server as a service.
Google Cloud SQL