Getting Started
This sections covers the technical steps to enable data sharing and start playing with your data. Already connected? Take a look at the available data.
Table of contents
Scopes
Two types of users can enable Data Sharing on Pennylane and retrieve data, with two different scopes:
- business owner users who can access data for their own business exclusively;
- accouting firm users who can access data for their entire firm’s portfolio.
Given a query, while the structure of the data returned will be the same, the actual data returned will vary depending on the scope of the user performing the query.
Important
Data Sharing is an advanced integration coming with some of our plans only. Read more about plans here.
Authenticating
Regardless of your scope and the way you are planning to connect, the first step will be to activate Data Sharing on Pennylane:
- as a business owner user, this is done from your connectivity settings, under the data sharing tab;
- as an accounting firm user, this is done from your firm settings, under the data sharing tab.
Upon activation, you will be given the following information, that will be needed to connect:
- Server
- The server URL
- Port
- Combined with the URL, it allows to reach the server
- Database
- Location of the data inside the data warehouse
- Username
- Unique user identifier; your scope and permissions are linked to it
- Password
- Randomly generated password needed for the user to authenticate
Do not share this sensitive information with anyone. Any doubts? Do not hesitate to reset your password from the same page: this will make the previous password obsolete and prevent any connections with it.
🔒 Strong, unique, random passwords are automatically generated to keep your data safe. We do not store them, and we only display them to you once, at the moment they are created.
Connection Options
Under the hood, Data Sharing is powered by a data warehouse that users connect to and perform queries on. Data warehouses are a powerful and widespread technology, backed by an active ecosystem of tools making it simple to connect. We have listed some of the most common options below.
Business Intelligence Tools
BI tools are probably the fastest way to connect and extract actionable insights from your data, and usually do not require any advanced technical configuration.
If you are not sure where to start, you can find a non exhaustive list of tools on our website. We have also documented a detailed process to connect with Power BI, and this video goes even beyond by building a reporting dashboard from scratch.
ETL Tools
You may want to import data into your own system, for example to join it together with other sources, or run more complex analyses on it. We recommend relying on Extract-Transform-Load (ETL) tools when possible to make this process easier. Some examples:
JDBC, Python and ODBC Drivers
Official lower level drivers and clients can be downloaded to connect from a wider range of applications, in case the none of the above approaches would suite your need.
⚠️ Disclaimer
For an optimal experience, we do not recommend this lower level option, see quotas below.
Quotas
In order to guarantee stable and preditable performance, we enforce a few limitations when users connect and retrieve data:
- the number of parallel connections if capped
- idle sessions are terminated after a period of time
- resource constraints are applied for extremely heavy or slow queries
☝️ A word from the legal team
More generally, you shall refrain from any abnormal, abusive or fraudulent use, as well as, in general, any use which would be in conflict with the laws and regulations in force.
Schemas
Datasets available are grouped together under meaningful schemas, and access permissions are defined at schema level. In other words, if you have access to a given schema, you will be allowed to query any table under that schema.
Data schema and table documentation available on this website is automatically generated. Any updates to the documentation will be closely followed or preceded by matching changes in the data itself.