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Topics started by tereza.cihelkova

5 Topics

T
GoodData Employee
tereza.cihelkovaGoodData Employee
published in Data sources - KB articles

ADD - Distributing Data to Multiple WorkspacesF.A.Q.

IntroductionAutomated Data Distribution (ADD) is the primary tool used to load data from your data warehouse or data storage into the GoodData platform. One of the key features that ADD offers is quickly distributing data from a single data source across multiple GoodData workspaces. This is especially useful when you’re building customer-facing analytics and have a separate workspace for each of your customers and want each workspace to contain only that customer's data. BackgroundFor the distribution to work, the workspaces need to be organized in a certain way. You can organize them manually using GoodData’s APIs, and lightweight UI called grey pages (available on any pricing tier) or utilize GoodData’s automated LifeCycle Management (available on higher pricing tiers).The below picture shows the required structure. A data product has to be defined and has to contain at least one segment. Each segment has one master workspace, which serves as a template for client workspaces - this

3541
thiago.mgoncalves
16 days ago
T
GoodData Employee
tereza.cihelkovaGoodData Employee
published in Solution architecture - KB articles

Provisioning and Change Management Automation in GoodData Platform

IntroductionLifecycle management (LCM) is a set of features in the hosted GoodData Platform that allow you to easily automate the provisioning of workspaces and users and handle change management. In this article, we’ll explain how these features work and show you examples of typical deployments. Parts of LCMIn GoodData, LCM has several different parts. At the lowest level, it’s a hierarchical organization of workspaces (which we’ll go into in more detail later). Then there are REST APIs that allow you to manipulate this structure. On top of the APIs is Ruby SDK that makes it easier to work with the APIs, and on top of that are so-called LCM Bricks - which are simply packaged pieces of code that fulfill specific functions - e.g., the Workspace provisioning brick manages workspaces in the LCM hierarchy. GoodData provides you with these bricks and allows you to easily deploy and configure them so that you don’t need to write scripts over the APIs to perform certain common tasks. For most

7564
Michael Ullock
GoodData Employee
16 days ago
T
GoodData Employee
tereza.cihelkovaGoodData Employee
published in Solution architecture - KB articles

GoodData.CN: Workspace Hierarchy and Inheritance Tutorial

IntroductionOne of the key features of GoodData.CN that makes multi-tenancy easy is workspace hierarchy. It allows you to set up parent-child relationships between workspaces. The child workspaces inherit the logical data model, insights, and dashboards from their parents. This means that you can easily:provision workspaces with pre-built content for each of your customers or teams update the pre-built content in all child workspaces by simply updating the content in their parent workspace Step by Step TutorialPrepare a Parent WorkspaceTo test this out, you need one workspace with some content. You can either use a workspace you’ve built, or you can download and import a layout we prepared for you. The layout is designed to work with the sample Postgresql database that is a part of the GoodData.CN Community Edition.To import the layout we provided, go to the Homepage in the browser and create an empty workspace by clicking on “Create workspace”:Next, click on “Connect Data”. If this is

3070
T
GoodData Employee
3 years ago
T
GoodData Employee
tereza.cihelkovaGoodData Employee
published in Data sources - KB articles

Mapping Your Source Data to a Workspace

IntroductionTo use your data in GoodData, you need to physically load the data into a workspace. A logical data model (LDM) that describes the data needs to be defined in the workspace before data can be loaded. Once the model is ready, you need to define a mapping between your source data and the workspace's logical data model. The mapping tells the platform which fields from your source data to load into which fields in the LDM. In this article, we’ll discuss two different ways to map the data. Naming convention vs. explicit mapping Naming convention (also called “output stage”)The first type is auto-mapping through a naming convention. This basically means that you have to prepare a special set of tables or views (so-called “output stage”) in your data source that follows a very specific naming convention, which allows the platform to auto-map these columns on the datasets and columns of the LDM. For example, the below-pictured LDM would auto-map to the below-pictured table via the

2990
T
GoodData Employee
3 years ago
T
GoodData Employee
tereza.cihelkovaGoodData Employee
published in Data sources - KB articles

ADD - Incremental Data Loading

IntroductionAutomated Data Distribution (ADD) allows you to load data into each dataset in a workspace either in full or incremental mode. Full load means that all data in the target dataset will be replaced with all applicable data from your source table or file every time ADD runs. This can mean transferring unnecessarily large amounts of data. On the other hand, incremental loads allow you to transfer new and updated rows only every time ADD runs, making for faster updates. How does it workIf your data source is a database: To turn on incremental load into a dataset, add a x__timestamp field to the corresponding source data table. ADD automatically recognizes this field, and ADD will switch to incremental loads for any dataset loaded from this table.  The absence of the x__timestamp field in a source table indicates that the mapped datasets will be loaded in full.  Every time ADD runs in the incremental mode, it saves the maximum loaded x__timestamp value to an LSLTS (Last Successfu

5980
T
GoodData Employee
3 years ago
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