With JSON, inserting null value within the data flow is quite tedious and a unique pipeline must be created to handle null values. Several options are available for converting JSON data to flat data, however when it comes to converting flat data into JSON, it is still a challenge. And “trigger now” is employed to test the data flow that is in the pipeline. The data flow activity is added to the pipeline to execute and test the data flow. Data flows are created in debug mode to validate the logic of the transformation. It allows you to perform data transformation activities via pipelines. ![]() ![]() It can process and transform the raw data into predictions and insights. Data Transformation using Azure Data FactoryĪzure Data Factory is an orchestration tool that is used to transform the data from one source to another. Azure Data Factory is easy to use, cost-effective, a fully serverless cloud service that accelerates data transformation with code-free data flows. Azure Data Factory helps you integrate, perform transformations, and visualize all your data with ease. JSON format supported by Cosmos DB is an effective format that is very lightweight.įor the data migration from PostgreSQL to CosmosDB we chose Azure Data Factory. In PostgreSQL the data format is flat (i.e., in the form of tables and columns), while CosmosDB supports flexible schemas and hierarchical data, and thus it is well suited for storing catalog data. So, the choice was clear for us to move the data from PostgreSQL to CosmosDB. Azure Cosmos DB is Microsoft’s fast NoSQL database and the first globally distributed database service in the market today to offer comprehensive service level agreements encompassing throughput, latency, availability, and consistency. ![]() In one of our projects, we had a scenario where non-transactional data had to be accessed frequently with low latency across regions. When systems evolve the need to migrate database does arise based on the data we are dealing with.
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