What Does a Single Data Pipeline Look Like?

A single data pipeline would consolidate your data stack. This means you could bypass all the difficulties of finding individual programs to perform a critical function in your data pipeline and instead rely on a single program. Sounds too good to be true? Let's explore how to make it a reality.

How to Achieve a Single Data Pipeline-The Toric Way

The major drawback to data platforms is how they require complicated coding knowledge in setup, exploration, and maintenance.  To break down the barrier to data insights, Toric takes a different route. It starts with the interface. 

A spreadsheet interface is limiting. Instead, Toric uses an interactive dataflow chart. It’s more than a visual representation of your process, it is your process. This intuitive interface is the key to unlocking all seven critical components of a data pipeline in a single workspace.

1. Connect to your data, wherever it is. Even drag and drop to add data.

The interactive dataflow offers several ways to connect with data. You can drag and drop data from your computer within the Dataflow, or connect to your data using an integration. Toric can process and transform millions of records interactively within the dataflow interface. This interface is flexible enough to pull complicated data in ways spreadsheets can’t. One example is the Toric Revit integration, which allows users to interact with 3D views from CAD files to pull data on specific components for analysis. 

2. Treat Toric as a data lake.

Toric’s workspace is hosted in your browser or iOS app and your data is hosted in the cloud. There is no need for an additional data warehouse when all your data can be stored and within the platform. Due to the nature of the interface, you do not need to clean or process your data before uploading it to Toric. Toric is flexible to act as your data lake even if you chose to use a different program for other essential data pipeline functions.

3. Transform and view data as you build your dataflow using nodes & node inspector.

In this workspace, you interact with your data more flexibly through nodes. These nodes enable you to perform individual functions, including data cleanup and transformation. Within this interface you can inspect the data in each individual node using the node inspector panel. 

You do not have to think about the order in which you clean and transform your data.  In fact, you can clean up your data while you build your dataflow. An added benefit of utilizing nodes is the option of classifying data types so you can prune and digest your data in new ways. 

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4. Blend your data intuitively.

The dataflow interface was built to provide a straightforward way to connect and process different data sets.

A dataflow diagram is traditionally a representation of how your data connects, but you can instantly connect different data for a deeper view in this interface. An added benefit is that this tool can give non-technical users easier access and understanding between different data sources and how they connect.

5. Analyze data and build reports at the same time.

As you explore your data using the nodes in the dataflow interface, you can take advantage of the data app panel to build elements of your report in the same view you are using to explore your data. Instead of switching between applications, you can provide context to your data exploration as you build your dataflow. 

6. Create visualizations directly in your dataflow.

Taking your data analysis further, the data app panel enables you to create interactive data visualizations.

Not only can you create graphs, bars, charts and more, but you can also do it while you explore your data and make it available for reports as you explore your data.

7. Share data stories instantly.

With this interactive dataflow, we have simplified data transparency. We already touched on how you can create data apps with context and notations as you explore your data. But you can also create pages as you explore your data and make these views available directly from Toric to stakeholders.

Data apps make it easy to dive deeper into the dataset as questions arise.  

Bias information can be costly to an organization, by utilizing a tool that creates data transparency when sharing insights allows stakeholders to explore data from different contexts immediately. Thus, enabling them to make better-informed decisions.

Automate the process.

Taking it a step further, the best part about dataflows is that they live and sit on top of your data. It’s easy to create a dataflow template and replace data for consistent data flows and reports with just a few clicks. Try it for yourself using our templates. 

Key Takeaway

While organizations and teams all want to be data-driven, conventional data pipelines and data stacks are too complicated to facilitate the behavior to drive data based decisions. While they can technically check the box for a pipeline’s critical components, they miss a key ingredient- accessibility. 

By using a flexible dataflow interface that consolidates complicated tools into a single data pipeline you can enable data exploration from stakeholders- making data agile and insights accessible and getting one step closer to fostering data-driven cultures.