Terminology

Advertiser Account

The individual account in, e.g. Google Adwords or Facebook, where campaigns are planned and budget is spent. We usually refer to each individual account as an advertiser, even though sometimes one brand can own multiple accounts.

Aggregate

A mathematical operation that takes values across multiple rows of data and reduces it to one value, reducing the level of granularity of data by grouping dimension values. The most commonly used aggregation within Datatap is to sum up multiple rows across specific key dimensions (columns). Other variations of aggregate include count, min, max average and others. Summing up rows and reducing the granularity can be useful in multiple scenarios: reducing the amount of rows to process data faster or to prepare two datasets to join them together. Within Insights, we can easily show aggregated values by displaying only a single a metric with one dimension, and add granularity by adding more dimensions to our data.

API

An Application Program Interface (API) is a set of routines, protocols, and tools for building software applications. An API consists of a set of rules describing how one application can interact with another, and the mechanisms that allow such interactions to happen. An interaction occurs when one application would like to access data from, or send the data to, another application. Alternatively, one application may request a service from another. For Datatap, we pull in data from Datasources automatically by building connections to, e.g. Google Adwords API, Facebook API, etc. The data returned by the API is most often in XML or JSON format.

Append

Adding new rows of data to the end of an existing dataset, the existing rows are not changed. Can be performed with Datatap as a Transformation (‘append’), or can be a type of operation that Datatap performs during transmission to the Destination (e.g. Google Sheets, BigQuery, database). Note that appending is not the same as overwriting or inserting data.

Attribution model

If a user completes a conversion on a website, several ad touchpoints could have influenced the user to complete the action. 

Example: a user sees a YouTube ad on Day 1, searches on Google and clicks on an ad on Day 7, sees a TV ad on Day 9, directly visits the website on day 12 and requests an appointment to get an offer (conversion).

In theory, an attribution model measures the impact of each touchpoint and aligns the conversion to all touchpoints that have been part of the customer journey.

In practice, customer journeys have become extremely complex over time, so it became almost impossible to track users across several devices/browsers and therefore accurately measure the entire customer journey.

Authentication

Refers to the method of granting Datatap access to a specific datasource application. Usually, authentication is the first step in verifying an API connection. There are multiple types of authentication that an API can support, e.g. Oauth2 protocol, username & password, token based, etc. Whenever possible, we use Oauth2 protocol.

Bug

A part of the product that is not working as intended. Bugs are noticed either by clients and reported to the support team, or noticed by an internal user (e.g. Client Services). Since they hinder the user experience, fixing a bug is usually prioritized over implementing a new feature or building a new API connector. 

Calculated KPI

A calculated KPI is a metric, the result of adding, dividing, subtracting or multiplying other metrics. Example: bounce rate = bounces / sessions. Users can easily create their own calculated KPIs, and should never import rates directly into Insights. Insights: Manage menu -> KPIs & Dimensions -> Create calculated KPI. Other examples include : CTR, Conversion rate, CPC, CPM, etc.

Campaign Monitoring

Section of Adverity Insights that displays only mediaplans and their associated metrics and dimensions as a table. Found under Plan -> Campaign monitoring. It is similar to Explore mode in that users can create multiple views and add them directly to a dashboard, filter and apply conditional formatting. It also contains a set of special columns that are only visible for mediaplans, e.g. delivery rate, expected delivery, buying efficiency.

Conditional Formatting

Originally an Excel feature that allows users to format the contents of a column according to a certain logic e.g. cell should be green if value > 5. Adverity Insights allows users to easily format metrics according to this logic and it is a useful element for highlighting certain values. Insights also supports conditional formatting when comparing one metric to another e.g. cell should be green if value of cell > value of other cell. Insights also supports conditional formatting in the KPI box visualization.

Connection

An authorization granted by a user to access data (usually through an API) within Datatap. Usually, the Connection allows API access to all the data the user has access to within the system. Connection methods are: OAuth2 (e.g. Adwords, Facebook), username and password (Appnexus) or API token/key (Criteo). In certain cases, additional permissions are needed for a user to grant API access or the provider has to create a dedicated API user with separate credentials.

CSV (file)

Comma Separated Value File.

Dashboard Template

Pre-built dashboard in Insights that functions as a blueprint for other dashboards that might be created across the various business entities. By updating the template structure, users can also do a bulk update to all connected dashboards that were created from the specific template. There is no limit to how many templates users can create. Users can also disconnect a dashboard from a template to prevent it from being overwritten with template changes.

Dashboard Theme

A set of color codes that defines the visual elements of a dashboard, e.g. background, widget border, link color, row background color etc. Adverity Insights comes with a set of pre-defined themes and the default theme for each dashboard can be set up per business entity. Users can easily create a new theme themselves under Manage -> Themes -> Create a new theme or by cloning an existing theme. Themes can easily change the look and feel of a dashboard by displaying the client colors instead of standard colors.

Data Blending

Data blending is a process whereby big data from multiple sources are merged into a single data warehouse or data set. It is not just the merging of different file formats or disparate sources of data but also different varieties of data. Blending data is usually done by performing a data union or outer join (type of SQL join) between multiple sets on a set of key dimensions that are common between both data sets. Within Datatap, a blended data set can easily be achieved by creating a bundle stream or sending to streams to the same table in a database. Within Insights, data is blended when you view different data sources in Explore mode. All these cases require the correct schema mapping so that the common columns across all sources are correctly identified.

Data Extract

Single output file from a Datastream. Each extract has a status indicator listing different suggested actions.

Data Extract Status

Displays the current state of your extract:

• Raw: Not transformed, exactly as provided by the Datasource API.
• Processing: Extract is still being transformed/processed by DataTap.
• Collected: Transformations are complete, extract is stored in DataTap and not being imported into target Destination. This is the final state for extracts of Datastreams with the type ‘Staging’.
• Notified: Notification has been sent to target destination. Based on metadata, target destination can now further process data extracts.
• Transferred: Data extract was transferred to target Destination.
• Imported: Data extract was imported into target Destination.
• Deleted: Data extract was deleted temporarily. Permanent deletion is effective with a grace period of 30 days.

Data Extract View 

The Data Extract View shows a preview image of extracted data. Several options are available, a full breakdown being described here.

Data Lake

A data lake is a storage repository that holds a vast amount of raw data in its native format until it is needed. Many companies use cloud storage services such as Azure Data Lake and Amazon S3, or a distributed file system such as Apache Hadoop. A data lake holds data in an unstructured way and there is no hierarchy or organization among the individual pieces of data. It holds data in its rawest form, it’s not processed or analyzed. Additionally, data lakes accept and retain all data from all data sources, support all data types and schemas (the way the data is stored in a database), which applied only when the data is ready to be used.

Data Mart

A data mart is a subset of a data warehouse, oriented to a specific business line. Data marts contain repositories of summarized data collected for analysis on a specific section or unit within an organization, for example, the sales department.

Data Model

A data model acts as a blueprint for building a data warehouse. There are multiple levels of data modelling, and Adverity supports clients in their data modelling efforts by creating a unified data schema and table associations per Datastream for the relational database/data warehouse destination.

Data Retention

Data retention refers to how many data extracts should be kept on DataTap.

• Retain all: default setting, all fetched extracts are kept unless manually deleted.
• Retain N extracts: only N extracts are kept.
• Retain N fetches: only N fetches are kept. A fetch is a collection of one or multiple extracts with the same scheduled date.
• Retain N days: only extracts generated within the last N days are kept.

PLEASE NOTE:

N + 1 = number of extracts/fetches/days kept. If N=5, 6 are retained etc.

Data Schema

Data Schema can be thought of as tags or labels that can be added to a data extract in order to change how the data will be interpreted by the Destination system.

The most common functions are:

• Defining whether the data type is a metric, or a dimension.
• Amending column names in order to combine disparate data categories, or separate identically named categories.

Data Throughput (processed rows)

Processed Rows are defined as the number of rows of all extracts that were fetched from a data source, after transformations are applied. When no transformations are applied, it equals the number of rows of the un-transformed extracts. When a Datatap stack has a limit on processed rows, the statistics icon will be visible in the left-hand side menu and can be accessed to monitor the current and historical usage.

Data Warehouse

A data warehouse is basically a database (or group of databases) specially designed to store, filter, retrieve, and analyze very large collections of data, or data across multiple applications or sources. They can be hosted on-premise (e.g. IBM, Oracle) or in the cloud (e.g. Amazon Redshift, Snowflake). A data warehouse can actually be made of different types of databases, relational and non-relational ones. Google also offers its BigQuery service to be used as a data warehouse.

Database

An organised collection of data, can be either relational (table based, SQL) or non-relational (object based, NoSQL). It is the most basic building block of any data architecture, where data can live and is usually isolated. A database can become part of a data warehouse where combining multiple databases is possible, as well as running analysis across all sources.

Database Schema

A database schema is the skeleton structure that represents the logical view of the entire database. It defines how the data is organized and how the relations among them are associated. It formulates all the constraints that are to be applied on the data.

Database Table

The main element of a relational database, where data is stored in columns and rows (like very large Excel files). A database can contain multiple tables. Tables are essentially sets of data that can later be queried or joined together.

Dataset

A collection of related sets of information, composed of separate elements, but which can be manipulated individually or as a single entity within Datatap.

Datasource

A Datasource is any data repository from which Datatap can extract data via a Connection and Datastream.

Datastream

Datatap specific element. A Datastream represents a specific type of defined query to an API that can be scheduled to run automatically to retrieve data. The result of this query (‘fetch’) is stored as a .csv file (‘extract’) and can then be sent to a Destination (e.g. database, Adverity Insights, File server etc). Datastreams are created by users within the Datatap interface (or through using the DT management API). There is no limit to how many Datastreams users can create.

Datastream Status

The Datastream status can be viewed and configured on the Datastream’s dashboard. It defines whether it is fetched automatically and if data is being imported into the target system. 

Further information about setting Datastream Status can be found here.

Date Format

The format of a date column e.g. 13.02.2019 or Wed, Feb 13th 2019 or 2019-02-13. Adverity Insights only accepts 2019-02-13. Within Datatap, users can easily use the ‘convertdates’ Transformation to format their date columns into a standard format. Usually only needed for file uploads, as APIs already supply the correct format or we can convert it in the background before we store it.

Destination

The location where data is loaded to from Adverity Datatap. We distinguish between "active" and "passive" destinations.
Active = data is pushed to the destination.
Passive = the destination pulls data from Datatap.
Currently, we support the following destinations: Adverity Insights, Relational databases (e.g. MySQL, PostgreSQL etc.), File servers (e.g. SFTP, S3, Azure Blob Storage), Google Sheets, Google BigQuery, and "passive" destinations Google Data Studio (GDS) and Tableau. Active destinations are set up within the destinations menu and assigned to an organisation.

Dimension

A dimension is a variable field, similar to a metric, except where a metric is always an integer value, a Dimension will always be treated as a string. I.e. a Dimension could be written as a number, but it would be considered text by Datatap and no calculations can be performed with that field.

Draft Dashboard

Dashboard that is not publicly visible to any users except the creator. By default, all dashboards will be created with status "Draft" and will only be available once the creator publishes them. Users also have the ability to revert a public dashboard back to draft status, and invite collaborators to work with them on existing drafts.

Drilldown

Feature that allows a user to access multiple layers of data in a visualization or table. Within Insights, to use a drilldown, a table must contain at least 2 dimensions in a tree structure. The drilldown can be achieved by collapsing/expanding the table rows. Within a visualization, this is possible by sliding the ‘with drilldown’ slider. Only certain visualizations allow for a drilldown, such as bar chart or pie chart.  

ETL Process

Extract, Transform, Load.
The process of copying data from one or more sources into a Destination system which represents the data differently from the source(s) or in a different context than the source(s).

Explore Mode

Insights specific element. Area of Adverity Insights where users can look at existing data ("explore data") as well as create tables and visualizations to be placed on a dashboard. Explore mode is private for each user and users need to be assigned rights to be able to access it. Data in Explore mode is shown according to the time range chosen and the fields (dimensions and metrics). Optional filters can also be applied (e.g. on Datasource or campaign). 

Extract

The result of a Datastream Fetch which is stored as a .csv file on the dedicated Datatap storage element. By default, each fetch will generate one extract with a unique naming pattern so that extracts do not overwrite each other. The extract will contain the fields that are defined within the Datastream settings. Once fetched, they will take on status "collected". Extracts can be then transformed using scripts and/or sent to a Destination. By default, extracts are not deleted (also see Local Data Retention). 

Extract Filenames

A Datastream setting that automatically splits up one extract into multiple, based on a specific date column.

"Unique by Day" is the default setting, and ensures that only on extract is saved for each day, as defined by the column selected using the "Key Date Column"drop-down box. It also automatically applied a naming convention: e.g. "adwords-123-20190213.csv". In this example, the name indicates that is an Adwords Datastream extract for February 13th 2019. When fetching a time range, e.g "last 7 days", "Unique by Day" will also ensure that there is only one extract per day and most recent data overwrites the older data.
It is most often used in combination with the Destinations Google Data Studio, File Server, BigQuery, Tableau.

"Unique by Fetch" will store each individual fetch result as a new and unique file, resulting in extracts being named with a  combination of Datastream slug-id-hash e.g. adwords-97-jfkljg189248.csv.

Fetches 

Within Datatap, Fetch Jobs is an overview section on the Datastream or organization dashboard that indicates the status of a fetch (in %). It will give a list of all fetches that have been scheduled, whether by a user or by the system. It gives the possibility to manually stop a specific task. 

Flat File Database

A flat file database stores data in plain text format. In a relational database, a flat file includes a table with one record per line. The different columns in a record are delimited by a comma or tab to separate the fields. Unlike a relational database, a flat file database does not contain multiple tables. Also see CSV (file).

Frozen Dashboard

Freezing locks a dashboard in its current state and prevents any new data imports from being displayed within data tables and visualizations. Dashboards can be locked by a user or automatically by the system if they have not been accessed or edited for 40 days. A dashboard can be unfrozen by its creator or the dashboard collaborators if not set to public. 

Generic Web Connector

Datastream that can execute any kind of GET (or POST) request to obtain data from a specific API. Depending on the API, this might also need a set of credentials (e.g. username and password, API token etc.). This can also be used to access data tables on html sites. The fetch results will be stored as an extract like with all other APIs and can then be transformed and imported to a Destination. Usually the generic web connector Datastream can be a useful workaround to showcase what kind of data can be pulled from a specific API or to temporarily pull data until a real API connector has been built. 

Helpcenter

Website that comprises all relevant information for clients using an Adverity product. Our Helpcenter is reachable at https://help.adverity.com/hc/en-us and is hosted through the provider Zendesk. All relevant sections are publicly available (no log-in required). Information within the Helpcenter is maintained by the product team and CS team.

Hosting Type

Refers to where a Datatap or Insights stack is hosted, from the following three options:

• Adverity Cloud - The default option, all data is stored within Adverity servers.
• Client Cloud - All data is stored in servers owned and managed by the client.
• On Premise - Data is stored offline in an isolated repository. This option is not preferred unless in exceptional circumstances, and only be special arrangement.

Hub

One type of business entity within Adverity Insights. The three types are: Company, Hub & Tenant. Implementing a hub & tenant structure is a good way to separate out data within Insights for speed, and to administer user rights. While a hub just groups multiple tenants, it does not hold any data within itself. It is a useful way to administer user rights. A hub can have multiple hubs underneath. All user rights, logos and themes are inherited top-down within the hub and tenant structure.

Instruction

Specific set of code that can be applied to a dataset as part of transformations. Types of instructions are ‘select’, ‘addfieldx’, ‘convertx’, ‘map’, ‘join’, etc. When created and saved by a user, one or multiple instructions form a script. The instructions themselves can be added through a drag-and-drop interface for users. A script can contain as many instructions as needed, and instructions are always executed in chronological order. The entire list of instructions, their definition and examples are found in the Datatap Documentation. Other examples are also found in the Helpcenter. 

Join

A join (also called SQL join) is an operation that combines data from one or multiple tables or datasets together based on common fields in each to meaningfully enrich datasets. Within Datatap, users can perform joins on datasets using the ‘join’ Transformation instruction and join together multiple Datastreams. There are 4 different types of ‘joins’: left, right, inner and outer. Datatap also offers a special kind of join based on time intervals (intervaljoin). Depending on what kind of data is joined, each has a slightly different application and use case. A join is usually performed with "keys" or common fields that identify which rows should be matched up. If the data to be joined is on different levels of granularity, it might also be necessary to aggregate them to the same level to avoid duplicates or incorrect joins.

Here is a good explanation including examples: http://www.sql-join.com/sql-join-types

JSON

JSON (Javascript Object Notation) is a text-based, human-readable data interchange format used for representing simple data structures and objects in Web browser-based code. JSON consists of ‘name : object’ pairs and punctuation in the form of brackets, parentheses, semicolons and colons. Within Datatap, we usually receive data from API connectors in a JSON format and then convert them to .csv files in the backend so that extracts are easily readable for users. We can also read JSON responses through the generic web connector Datastream.

Key Column

• Key Columns are used to uniquely identify a Dataset. They are required to ensure that data is overwritten correctly
• Only datasets where all key columns are matching are overwritten. If using Adverity Insights as Destination key columns can be combined with other overwrite options.
• Only columns that are defined as dimension in the target system can be used as key columns

STEPS FOR SETTING KEY COLUMNS:

• Go to the Datastream’s Column Mapping
• Set green switch(es) to Key Column
• Save mapping

KPI Box

A specific type of visualization that is used to display total values of KPIs to highlight certain values. Can have icons added and supports conditional formatting.

Local Data Retention

Datastream setting that defines how many extracts should be stored ("retained") in the Datatap storage. The default is to store all extracts fetched. There are other retention options, e.g. only retaining the last 10 days, the last 10 extracts or the last 10 fetches.

Management API

Refers to our own API that is used to entirely manage Datatap without accessing the interface. The use of the API is available to all Datatap clients. If clients use the API instead of logging into the interface to manage all their processes, then they could integrate it into their own infrastructure and build a platform on top of it. The documentation is available within the documentation section of Datatap.

Manual Fetch

Fetch that is executed by a user as opposed to a scheduled fetch that is executed by the system. Manual fetches can be executed with a fixed date range or with the date range that is set for the Datastream e.g. last 7 days (reschedule fetches within the selected time window). A manual fetch can be executed "raw" -> no transformations will be applied and the extract will not automatically be sent to the Destination.

Manual vs. Scheduled

Defines whether a Datastream should run automatically or not. If set to manual, no Fetches will be executed. If set to scheduled, a user can define the frequency for scheduling (e.g. every 1 day) and the time at which the fetch should be executed (in UTC).

Mapping Table

Datatap specific element that allows functionality similar to an Excel VLOOKUP to look up values. It might be used to translate values, e.g. renaming the values from the field ‘device’ across all Datasources.

How-to article:

https://help.adverity.com/hc/en-us/articles/360002318013-Mapping-Table

Mediaplan

Project plan that combines media spending and advertising goals in one cohesive strategy. Used by media agencies to plan and track their objectives by clients and campaigns. A mediaplan contains values for planned metrics, allocated budget and is then used to track performance while the campaign is being executed. By comparing planned with actual data, they can evaluate how well their campaign is doing and how to improve performance. Since it involves matching up of planned and actual data across multiple sources, it can be a big pain point for advertisers and agencies to keep track of, and Adverity allows users to manage this process more seamlessly with a designated Insights area to track this and add to a dashboard (also see Campaign Monitoring).

Meta-Section

• Every Data Extract has a meta header with additional meta information.
• This information can be utilized by any Target System or within Datatap itself. Examples are column- & row counts, scheduled date, time range start & end and custom meta information.
• There are several instructions within the scripting engine which can operate on this meta data.
• The metadata for each extract will vary depending on the Datastream type. 
• Certain metafields are always present e.g. datastream_display_name, datastream_extract_scheduled etc.

Metadata

Metric

Non-Relational Database

A non-relational database is any database that does not follow the relational model provided by traditional relational database management systems. Also referred to as NoSQL databases. Data may be stored as simple key/value pairs, as JSON documents, or as a graph consisting of edges and vertices, depending on the type of database and data. Examples: MongoDB, Couchbase, Cassandra, Scylla, Redis, Elasticsearch.

OAuth2

When granting Datatap access to a 3rd party system via OAuth2, it inherits the grantee’s permissions for the respective 3rd party system with the given scopes. Datatap will never manage, create or delete any items in a publisher system.

STEPS TO GRANT ACCESS

• Open a Connection and create OAuth2 Link for a Datasource (e.g. Facebook).
• Follow link and login to Datasource with a user account that has access to the desired  account(s).
• For better customer side control it is recommended to initially setup a dedicated email account (e.g. adverity@myCompany.com ) that is then granted access to the accounts
• This e-mail address if afterwards used to authorize access for DataTap
• User grants permission with user account.
• User can always review and revoke permissions/scopes he has granted Adverity Datatap in respective profile settings of the Datasource.

For more information about OAuth please read: https://en.wikipedia.org/wiki/OAuth

Organization

Structural element within Adverity Datatap that enables separation of access to the different sections e.g. Datasources, Datastreams, target Destinations, for users. One organization can only have one destination assigned. Within Datatap, users can maintain their own organisation structure. 

How-to article: https://help.adverity.com/hc/en-us/articles/360002033373-Organisation

Overwrite Options

Datastream setting that defines how data should be overwritten for Adverity Insights or Destination database. The recommended overwrite option is usually: Datastream + date range. This means that all historical data of a stream will be kept in the destination and meaningfully overwritten based on the date range of the new extract, without overwriting data from other streams or deleting all previous data. Note that overwrite options are NOT relevant for Tableau, Google Data Studio, PowerBI, File Server, Google BigQuery or Google Sheets, which need to be handled differently. 

PreSense

Adverity product currently under development. Presense will gather data from Datatap and run algorithms to detect data outliers, flag them, and make recommendations based on optimal performance. 

Primary Key

Field that is designated within a database table to hold a unique identifier for each row, e.g. a product ID. The primary key is then used to overwrite existing data when the table is updated.

Public Dashboard

An Insights dashboard that can be seen by all users within a business entity. By default, dashboards are created in "Draft" state and have to be changed to status "Public". A dashboard may be Public, but not editable by any other users than its creator.  

Quickfilter

Interactive filter element that can be placed on a dashboard to filter on the underlying data and display the result for a single user. There are two types of quickfilters: date range quickfilter and dimension quickfilter. The quickfilter is not permanent (hence the name) and the filtering that is done will revert back to the normal state when a user leaves the dashboard. The dimension quickfilter supports any dimension and also combinations of multiple dimensions. Interaction with the quickfilter is one of the three actions a shared dashboard user can take on a dashboard.

Rate Limit

Limit to how many requests or how often requests can be made to a specific API. The API may reject requests for a variety of reasons, ranging from having too many concurrent connections to the requester forming a poor request for high amounts of data. It is usually based on the requester ID, e.g. through username and password or a specific API token. For example, an account may only be allowed to query the API 10 times every hour. If a rate limit is exceeded, then the API will not return any data and return an error message instead (usually Error 429). Depending on the API, there might be a way to increase the threshold to be able to make more requests (e.g. through paying an additional fee).

Ready State

"Ready state" is a term used to refer to data extracts that have been successfully imported to Datatap and are now held static in the system (i.e. extracts with the status "Collected" or "Imported").

Extracts which are "Raw", "Deleted" or "Processing are not in a Ready State.

Real-Time Widget

A special type of widget within Adverity Insights that directly queries the Facebook or Google Ads API to show real-time data within Insights (not through Datatap). Has to be configured by the Adverity support team and is not available for other Datasources.

Regular Expression (Regex)

A specific pattern used to find and replace a set of characters that matches a certain logic, e.g. ‘starts with __’, ‘contains __’, ‘ends with ___’ etc.

Regular expressions are found within multiple places in Datatap, most commonly within scripts. An example is the instruction 'cutre': cut fields using a regular expression. Regular expressions can also be included in any instruction that contain a python expression like 'addfieldx' or 'select'.

Relational Database

A type of database that is table based and can be managed through SQL queries. A relational database is a collection of data items with pre-defined relationships between them. These items are organized as a set of tables with columns and rows. Examples: MySQL, PostGreSQL, Microsoft SQL Server, Oracle DB, MariaDB.

Resource Quota

Optional quota limit for a Datatap stack that monitors how much throughput the stack is using. The quota limit is defined within the commercial agreement and monitored by the Client Services team.

S3

Amazon Simple Storage Service (S3) is a cloud storage solution offered by AWS used to store data. Within S3, data is split up into buckets (similar concept to folders on a file server). By default, each Datatap instance has its own S3 bucket where all extracts are stored. If needed, clients can also set up their own storage. 

Scheduling

Datastream setting that defines how many often a fetch should be executed when the Datastream is set to run automatically. The default is every day. Users may schedule fetches to run more frequently (e.g. every 12 hours). However, it must be noted that while we do not limit users, there are usually limits imposed by the API that will restrict the ability to request data very frequently (also see Rate Limit).

Schema Mapping

Datastream-specific configuration that defines the name, datatype and usage of each field from the stream for the Destination (also see Data Schema). There is a default schema mapping that can be used and once a field has been assigned a schema value ("mapped"), this information is retained for the future.

How-to article: https://help.adverity.com/hc/en-us/articles/360001864554-Schema-Mapping-Defaults

Script

One or multiple Transformation instructions that can be arranged in a chronological order and attached to a Datastream. One script can be used across multiple Datastreams. Within a script, the individual instructions can be added and arranged using a drag and drop interface.

Shared Dashboard

A dashboard that is accessible by one or multiple external users, who otherwise do not have any access rights to Insights using the dashboard URL. A dashboard share can be revoked anytime. Obtaining access to a shared dashboard is a "view-only" mode, and visitors cannot modify anything about the dashboard. They can interact with all charts and graphs using drilldown or sorting, as well as use existing quickfilters, and optionally, also export the dashboard.

SQL

Structured Query Language. Developed to communicate with relational databases. Also see SQL query.

SQL Query

While SQL itself refers to the overall collection of statements and functions, a query is a specific request made to a SQL server to retrieve, insert or modify some specific data, e.g. adding more data to a data table, or getting all data only for a given time range. Within Insights, Explore mode and the dashboard also call upon the underlying Insights Data Cube to retrieve the data that is shown in a widget or a table, or when a widget is refreshed. Depending on how big the request is, the query can take a few seconds or much longer to complete.

Stack

The dedicated infrastructure environment for Datatap & Insights that is built for each client. A stack can be hosted by Adverity, in the client's cloud, or on-premise (also see hosting types). The stacks are reachable through their own dedicated URL e.g. client.datatap.adverity.com or client.adverity.com. There are also stacks that are only for internal use, like showcase.datatap, demo-insights.adverity.com, etc.

Stacks are split into multiple Workspaces.

Storage

The dedicated data lake for each Datatap stack where all the extracted data ("extracts") are stored. By default, this will be an S3 bucket, but clients can set up their own storage elements, like Azure Blob or Google Cloud Storage. The storage is not visible or accessible to users.

See also: https://help.adverity.com/hc/en-us/articles/360002206853-Storage

Subtable

A collection of data that is part of the main table and can be specifically accessed and modified. Within an extract in Datatap, users can create subtables by adding a ‘select’ instruction that has a specific subtable defined. Subtables can be useful to work with because they can later be joined back to the main dataset with some additional information to enrich the main table. Instead of having to create two datasets, a subtable can used to duplicate the existing dataset and modify it later.

Acts like a temporary table which can either be altered by (almost) every instruction within a transformation script or by some import instructions (Excel, XML, ZIP, etc.) upon import.

All instructions can be used to create subtables from the current state of a transformation script by adding the subtable key with a custom label ‘subtable’;  ‘label_of_subtable’. The new subtable will then hold the current state with the given instruction applied.

All other instructions can then be applied on that subtable like any regular table.

Further, it is possible to create multiple subtables within a single transformation script. Please be aware that this might increase processing time.

Target System

Tenant

One type of business entity within Adverity Insights. The three types are: Company, Hub & Tenant. Tenant was formerly known as "client". Implementing a hub & tenant structure is a good way to separate out data within Insights for speed, and to administer user rights. While a hub just groups multiple tenants, it does not hold any data within itself. If a tenant is deleted, all the associated dashboards and data is deleted as well. Tenants can be renamed. Users can decide which currency symbol should be used per tenant and can set a dedicated color theme, logo, dashboard theme and labels for each tenant.

Tenant Mapping

Logic used to assigned extracts to specific Insights tenants. If the Destination of an Organization is Adverity Insights, each Datastream will require Tenant Mapping. There are two options: Single tenant mapping (select a tenant from the dropdown and all the data from this Datastream will be sent to the tenant) or using a mapping table (lookup logic to automatically split up and send extracts to specific tenants). Tenant mapping tables require a source column like account or account ID.

Time Range

Datastream setting that defines how many days back data should be fetched when the Datastream is set to run automatically. The default is yesterday. Depending on which Datasource, it might be useful to set a time range that is larger, e.g. 7 days, 21 days, or 30 days.

Transformation

Synonym for a script. Can be one or multiple instructions that can be attached to one or multiple Datastreams.

Transformation Preview

Mode within Datatap that lets you preview the result of an attached script within a Datastream extract. The preview mode is a useful way to see the differences between before & after a script is applied before the action is carried out. For very large files, the preview might not always load.

Transpose

A function in Excel that lets users change the format of columns and rows. Through transposing, data in columns is rotated so that it appears as rows instead. Within Datatap, users can use the instruction ‘melt’ to achieve this. Within Insights, users can create a transposed table in Explore mode by clicking ‘transpose’.

User Role

Set of rights that are assigned to a user within Adverity Insights. Defines which areas of the platform are visible (e.g. Explore, Dashboards, Campaign Monitoring etc.). Usually defined by the admin.

Value Table

List of values that should be used in a mapping table with a drop-down function (instead of writing in values automatically). A value table can either be populated manually, automatically (collecting values from a Datastream) or have an automated sync provider (Adverity Insights).

How-to: https://help.adverity.com/hc/en-us/articles/115000962714-Value-Table

Visualization

Graphical representation of data (as opposed to a table, which displays data in numbers). Insights supports a large number of different visualizations types e.g. bar chart, pie chart, heatmap, worldmap, bubble chart etc. A complete overview is available within the Insights Showcases. Users can build visualizations within the Explore mode.

White Labeling

White labeling refers to adapting the branding and look of the Insights stack according to client's colors, logos and domain. We can adapt the following: custom domain, custom log-in screen, remove footer, remove Adverity logo, Custom e-mails for user created, dashboard shared, password reset (e.g. in Dutch instead of English).

Widget

Element that makes up a dashboard. There are multiple types of widgets: data tables, data visualizations, Quickfilters, text widgets, image widgets, campaign monitoring widgets and real-time widgets. Widgets can be resized to be fitted next to each other. Widgets can be cloned on a dashboard and also embedded within an iFrame integration.

Zendesk

Ticketing platform used by the Client Services team to receive customer support questions. Also used to host the Helpcenter.