Building a product that delivers for user’s expectations is hard, but at Datagran we’ve always listened and worked hard to deliver on those expectations. Today, we are proud to introduce a brand new Integrations dashboard. One that will give companies the data governance they need to feel confident about our technology. We hope you enjoy it as much as we enjoyed building it.

Integrations Overview

One of the most popular ways teams use Datagran today is by integrating multiple data sources into our tool. This process is done through ELT and it is a straightforward procedure of inputting information that eliminates the need of extracting data from multiple sources such as a warehouse, servers, CMS, and so on and storing it in different tools for processing, analysis, and modeling. Datagran’s integration process allows teams to centralize their data in one place, know what sources are ready to use for analysis, Machine Learning model building, and visualizing, as well as reuse them across multiple projects living inside of their workspace at once.

Today, we are going one step further with the release of the Integrations Uplift. Just as the name implies, the newly designed uplift offers a deep-dive of all their data sources with added functionality like Extractions and Settings. For this initial release we are focusing in MSSQL and MySQL, with more options rolling out very soon.

The first big change introduced in the Overview panel is the Extractions component.

Tracking Data Sources & Data Row Overview

Currently, when a user integrates a data source in Datagran, it’s not clear how much information is being replicated. Users are left wondering whether they extracted half of their data or all of it. Moreover, they don’t have a visual representation of the data rows they are pulling out for their projects.

With the new data row chart, teams will be able to easily view exactly how many data rows were replicated from their source during their billing period. How does it work? We built this tool to clearly visualize the total number of data pulled from a said data source, nestled inside of their Workspace, teams will have a clear number displaying the rows replicated, and a bar chart that will help them track the load of rows used during a billing period. 

Furthermore, they will still be able to see all the data sources available in Datagran on the left-hand side menu. While on the right side of the dashboard, they will be able to see the data sources actively integrated into their workspace.

Each data source will now display an icon indicating its status which will let the user know if said integration is successfully integrated, in-process or if it had an error while being integrated.

Get a Closer Look At The Integration

Additionally, each integration will have its own overview where users can dig deeper into its contents. 

By clicking on the three dots next to the integration’s status, users can either delete the integration or go to the overview. The overview will provide a summary of the integration, its extractions, the streams in the process of replication, and settings.

The integration’s summary breaks down the extraction status, the streams preparing to be loaded, the rows loaded in the last 24 hours, and the total rows loaded over the billing period.

Streams are set up in the initial steps of the integration process, and they are flows of data that are extracted from a data source, and they will now have their own tab, to help teams view exactly which streams are being extracted, the rows inside of each of them and the date of the extraction.

Currently, users can choose which streams they want to replicate from their integration, but sometimes, they may want to stop a certain stream. With Stream Replication, teams will have complete control over the streams replicated, their status, and a timeline of the last extractions performed for that specific integrations. A toggle will give them the ability to turn them on and off so they can decide which ones to channel into their projects. 

For example, if a team wants to run a regression analysis model with two integrations like Redshift and Hubspot, but they only need five streams from each integration, they can simply turn off all the remaining streams except the ones they intend to use in the model.

Additional to stream replication, users will be able to add a brand new stream right away by simply clicking on the Add Stream button. This will allow them to choose the stream they want to replicate and select the type of extraction, and even create a custom schema. 

Our objective is to provide teams with the freedom and ease to work with their data without the need for custom code if only need be.

To wrap it all up, the integration’s configuration panel will allow users to view and edit their integration’s setup information, replication frequency, and ultimately, the ability to delete the integration.

When a user integrates a data source, Datagran used to replicate data on a daily basis. Now, we will give users the option to choose the replication frequency for each integration, so teams can replicate their data as often as daily, weekly, or monthly. This is a powerful feature considering the speed at which, for example, customers pour information into businesses. By replicating the information as fast as possible, companies can interact with their customers in almost real-time, increasing the chances of new opportunities along the customer journey.

It’s now crystal clear for the entire team to see the data sources available to them, the ones currently being used in projects, the number of data rows replicated and a breakdown of this usage during a billing period. 

We couldn’t be happier to bring these new features to you and can’t wait to hear about the amazing things you will build with them. Our users continue to use Datagran in creative and exciting ways, and we’re looking to learn even more from you this year. Request access to our platform and all the new features we are and will continue to roll out throughout the year.

If you ever want to share your feedback, you can always drop us an email at support@datagran.io.