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Data Connectors: Your Key to Seamless Data Integration

Technology   |   Alex Gnibus   |   Dec 19, 2023

Data connectors might not always grab the spotlight, but they’re the unsung heroes in the world of analytics, playing a crucial role in the seamless transfer and transformation of data. Data connectors act as bridges facilitating smooth communication between disparate datasets, applications, or storage systems that may use different formats or structures. The significance of connecting diverse and non-traditional data sources cannot be overstated in a technology environment where AI and IoT are reshaping how we handle data.

Data connectors aren’t just table stakes. They can be a key differentiator for your business. They’re at the heart of modern data stacks, empowering businesses with automation and democratization in their data pipelines. This evolution allows business analysts to dive into data analysis without constantly relying on data engineers or IT teams for support.

So, don’t overlook the humble connector. It could be your key to faster time to insight.

How data connectors facilitate a modern data stack

Designing an effective data stack is crucial for transforming raw data into valuable insights. Surprisingly, only about 30% of companies manage to extract accessible value from their data.

What is one step towards fixing this? The data connector.

Connectors are vital in your data journey, seamlessly bridging the gap between raw data and actionable insights. They are the cornerstone of a modern data stack, enabling a fluid data pipeline that accelerates your path to insight. A connector can differentiate between a valuable data stack and a useless one. It can take you from weekly dashboards to hourly insights.

Ultimately, the goal is to craft a data architecture that not only serves your business needs but also empowers it. This means selecting data solutions equipped with connectors that align perfectly with your unique business requirements, ensuring that your data works for you, not against you.

Best practices with data connectors

When deciding which connectors are most important, start with the business value and work backward. Identify the high-priority data sources that your business needs. Which functions and departments are using data? What’s most important for getting insights?

For example, your Chief Revenue Officer might need a dashboard to monitor sales performance that refreshes daily. In this case, your analytics platform may need a Salesforce and Tableau connector to go from Salesforce to prep and analysis to your Tableau dashboard.

This will also help you determine what kind of functionality you need with various data sources – do you need to be able to both read and write data to a particular data source?

Capabilities to look for:


  • Can your connectors refresh data frequently and load it automatically into your data storage and analytics platform?
  • Can you automate a workflow so that you’re not even thinking about connecting to the data?


  • Does your analytics solution enable you to build custom connectors? Your data stack and the analytics tools you use should allow you to choose from pre-built connectors or create your own with APIs.
  • Does your data platform offer a place to buy (or sell!) custom connectors? For instance, Alteryx Marketplace allows you to shop for additional connectors, such as a JIRA connector or a ServiceNow Input Tool.

ETL for large data volumes

ETL (Extract, Transform, Load) was a preferred method for cost-effective data transformation since storage costs were so high. But when Snowflake, Google BigQuery, Databricks, and Amazon Redshift hit the scene, they opened the door to transforming data after loading it – called ELT (Extract, Load, Transform).

What does that mean for connectors? You need to ensure your connector supports the ETL process by making it easy to bulk-load large amounts of data and then transform that data directly into the data warehouse. Questions to ask:

  • Can your connector make it efficient to load large amounts of data?
  • Does your data platform enable bulk loading with data warehouses like Snowflake and Redshift?
  • Do you have in-database connections that enable pushdown processing, so you can perform transformations directly on the data post-load?

Data Connector Use Cases in Action

 The image is an informative graphic showcasing the connectivity and integration capabilities of the Alteryx platform. It features a central large rectangle labeled "Alteryx" with arrows pointing to and from it, indicating data flow. The diagram is divided into sections, each with a circle containing logos and names, representing different types of data sources and destinations. From left to right, the top row includes "Network Files" with logos for Google Drive, SharePoint, Dropbox, Excel, and others. The bottom row includes "Data Stores" with logos for MongoDB, Teradata, Hadoop, and more; "Enterprise Apps" with logos for Workday, SAP, ServiceNow, Salesforce, Oracle, and more; and "Databases" with logos for AWS, Spark, Hadoop, MongoDB, Microsoft, and others. There is an orange arrow coming from the bottom pointing upwards towards Alteryx labeled "In-Database Pushdown" with logos for Databricks, Snowflake, Oracle, SAP HANA, MySQL, and others. On the right side of Alteryx, the diagram shows "BI Platforms" with logos for Qlik, Tableau, Looker, and more; and "Files" with icons for PDF and Excel, plus more. Above the Alteryx rectangle, text reads "90+ Connections & Drivers," indicating the extensive integration options available.

Toyo Engineering automates ELT processes using an API connection

Toyo Engineering, specializing in large-scale chemical plant construction, faced challenges with time-consuming data extraction and management processes involving SQL, CSV, and Excel. Using Alteryx, Toyo automated these processes, significantly reducing manual workload and errors. Toyo set up an API connection with its 3D modeling system (Smart 3D) to streamline report generation and data extraction, saving over 38,000 hours on one project. This automation led to better quality control, reduced design re-works, and facilitated Toyo’s digital transformation, opening new opportunities for innovation and efficiency.

London North Eastern Railway seamlessly outputs to Tableau for fast-paced insights

Initially, London North Eastern Railway (LNER) struggled with limited data analysis capabilities in Excel, which led them to adopt Tableau for better visualization and accessibility. Once they had Tableau, they saw a higher demand for deeper self-service insights, prompting LNER to integrate Alteryx. Alteryx’s ability to handle large data volumes and various types and its easy connectivity to Tableau significantly enhanced LNER’s data processing. This integration allowed LNER to automate and streamline data workflows, including analyzing IoT data from door sensors on trains. The combination of Alteryx and Tableau enabled LNER to create interactive visualizations and make real-time decisions, improving operational efficiency and passenger experience​​.

“The ability to have an Alteryx workflow that can run every five minutes with the latest data refreshed, push straight to Tableau, and have the Tableau feeding off that live data, it just means that the staff and teams can make real-time decisions if they need to.”

Data connectors are essential tools for modern data analytics and serve as bridges between data sources and platforms. Facilitating seamless data integration enables businesses to transition from basic data handling to sophisticated analytics.

What’s Next

View what Integrations are available on Alteryx.

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