Say hello to Databricks, a dynamic platform merging the power of Apache Spark with collaborative workspaces allowing flexibility to use multiple programming languages (Python, Scala, SQL) within a single environment. This versatility surpasses the constraints of predefined transformations in pipelines, allowing for more intricate and customized data transformations.
Data Pipeline Hiccups: The ‘Content-Type’ Snag
Let’s initiate our journey by scrutinizing a practical instance we grappled with. The goal was straightforward: transmitting data to Dynamics 365 Business Central from a source via the Microsoft Azure Data Factory pipeline. However, our trajectory encountered roadblocks, notably a persistent ‘Content-Type’ error haunting our API call for the indispensable POST request on the Business Central side.
Databricks Unleashed: Rescuer
Enter Databricks, our game-changer. Not only did it rescue us when it came to posting data in Dynamics 365 Business Central, but it also unfolded a spectrum of options including error handling due to its customization possibilities. Stay tuned as we navigate through this in the later part of our discussion.
What is Databricks?
Databricks is a cloud-based big data processing platform built on Apache Spark. It combines the functionality of Apache Spark with an interactive workspace, making it an ideal environment for data engineering, machine learning, and collaborative analytics.
So, as an example, imagine SQL Server as the source repository, containing the essential data that we aim to store in Dynamics 365 Business Central.
Let’s proceed methodically to comprehend the implementations carried out in Dynamics 365 Business Central.
Note: Before we go forward with using APIs from Business Central, we need to integrate Dynamics 365 Business Central APIs and Web Services with OAuth to access those APIs in third-party applications. Please click here to learn the step-by-step configuration of the same.
1. Started with importing the required libraries.
2. Created a Data Frame and populated it with the data intended for posting in Dynamics 365 Business Central which is from SQL Server table.
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What are DataFrames in DataBricks?
In Databricks, DataFrames are a distributed collection of data organized into named columns. They are a crucial abstraction for working with large-scale data processing using Apache Spark. They are a fundamental structure in libraries like Pandas in Python.
It is a two-dimensional, tabular data structure with rows and columns, commonly used for data manipulation and analysis. It provides a convenient way to organize and process structured data in a format like a spreadsheet.
You can see the visual representation of a Data Frame. Here, the ‘vertical’ property of Data Frame is set to ‘True’ for smooth visuals, you can try ‘newdf.show()’ for non-vertical visual of a data frame.
3. Once the data is stored in a DataFrame, we proceed to map the data to their corresponding fields in the Dynamics 365 Business Central API.
4. Following the storage of data in the array list, we initiate a POST request to Dynamics 365 Business Central, incorporating the necessary headers and URL. Finally that’s how the data gets posted in the API.
5. Finally that’s how the data gets posted in the API.
6. And hence, you can see the data got populated in Dynamics 365 Business Central!
Conclusion
Our experience with Databricks and Dynamics 365 Business Central has been game changing. We tackled challenges in our data pipeline, and Databricks not only provided solutions but also opened doors for creative data handling. The systematic steps in Business Central, using Data Frames and smooth API integration, point towards a future where efficiency meets innovation. This collaboration promises a data environment that keeps evolving, bringing in new possibilities and redefining how we work with data.
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