The major use case I have for CData Sync is towards the ETL part. The way it works can be summarized in two sentences. Earlier ETLs required that you map a field manually, and if you had any new field, you had to manually adapt it. With CData Sync, this process is automated. If you have a new field, it updates the schema structure and adds that attribute automatically. If you have additional fields, whether you have one, two, three, or four, it goes ahead, updates that particular schema, and maps the data to that field automatically. It is such a time-saver in the ETL domain because previously we had to find out which particular mapping needed updating, and then go into those mappings to adapt them. With CData Sync, you do not have to do anything like that. You only have to update the source to indicate that it now has extra attributes, and the tool takes care of the rest.
The main use case is that the stack is MongoDB, with the main data source from Mongo. CData Sync comes as a third party that is layered on Mongo. Power BI is directly connected to CData Sync, so we can stream the data from Mongo and visualize it. The other unique thing about our use case is that aside from streaming live transactional data for informed decisions, we are in multiple markets at the moment in several African countries. This has been challenging because streaming data for different currencies and different local currencies, then accumulating everything in USD, makes everything look muddled up. However, it has been good with CData Sync since we can have everything in one currency.
CData Sync simplifies data integration by allowing seamless data movement between sources, improving data accessibility and flow across business platforms.
CData Sync enhances organizations' ability to synchronize data from various platforms efficiently. Its intuitive dashboard and robust connectivity options make it ideal for IT departments seeking streamlined data management. With minimal setup effort, it integrates seamlessly with other data systems, offering reliable performance...
The major use case I have for CData Sync is towards the ETL part. The way it works can be summarized in two sentences. Earlier ETLs required that you map a field manually, and if you had any new field, you had to manually adapt it. With CData Sync, this process is automated. If you have a new field, it updates the schema structure and adds that attribute automatically. If you have additional fields, whether you have one, two, three, or four, it goes ahead, updates that particular schema, and maps the data to that field automatically. It is such a time-saver in the ETL domain because previously we had to find out which particular mapping needed updating, and then go into those mappings to adapt them. With CData Sync, you do not have to do anything like that. You only have to update the source to indicate that it now has extra attributes, and the tool takes care of the rest.
The main use case is that the stack is MongoDB, with the main data source from Mongo. CData Sync comes as a third party that is layered on Mongo. Power BI is directly connected to CData Sync, so we can stream the data from Mongo and visualize it. The other unique thing about our use case is that aside from streaming live transactional data for informed decisions, we are in multiple markets at the moment in several African countries. This has been challenging because streaming data for different currencies and different local currencies, then accumulating everything in USD, makes everything look muddled up. However, it has been good with CData Sync since we can have everything in one currency.