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Data Merge, also known as data consolidation, refers to the process of combining multiple datasets into a single dataset. Datasets are typically stored in different data sources, including relational databases, NoSQL databases, cloud storage, and big data platforms. By merging data from different sources, businesses can analyze their data more efficiently and obtain new insights for better decision-making.
The process of Data Merge involves connecting to multiple data sources, selecting the required datasets, and defining the rules for combining the data. The rules can specify the merging criteria, such as the common columns between the datasets, or how to handle duplicates and missing data.
Data Merge can be automated using software tools that can connect to multiple data sources, and provide pre-built templates or customizable options for defining the merging rules. Automated Data Merge tools can also provide options for scheduling automatic merges, performing incremental merges, and visualizing the merged data.
Data Merge provides several benefits to businesses, including:
Data Merge can be used in various scenarios, including:
Data Merge is closely related to other data processing and analytics concepts, including:
Data Merge is a useful technique for businesses that want to leverage the benefits of data lakehouses, which can store large amounts of diverse data in a scalable and cost-effective manner.
With Dremio, users can easily connect to multiple data sources, and use Data Merge to consolidate data for analysis and reporting. Dremio's Data Reflections feature can also accelerate Data Merge operations by materializing the merged data for faster access and query performance.