Long-running tasks such as large weekly or monthly data migrations can be set up to run at times when impact on server performance is non-critical. Studio 3T also comes with its own scheduler that runs within the application, whenever the IDE is live. You can also save migration configurations to be executed later, either to or from MongoDB. You can choose to migrate the whole database or just selected tables and the operations tab in the bottom left of the GUI reports on success or failure of the migration. A range of mapping options such as one-to-one or one-to-many relationships are customisable using the mappings editor. By default, each table is mapped to a collection with the same name and all the columns in the table are included as top-level fields in the new MongoDB collection, each with the most appropriate data type as detected from the original SQL type. If you choose to migrate data from SQL to MongoDB, your selected tables will be created as import units. Studio 3T is the nearest equivalent in functionality to SSMS in the MongoDB sphere, and also takes care of the conversion and mapping between relational tables and documents as part of the migration process, in either direction between SQL and MongoDB. I now use Studio 3T, and must declare an interest here because I write occasional articles for them. In the past, I’ve used various third-party tools for Mongo/RDBMS transfers, including RazorSQL, Talend, Kettle and SSIS.
Razorsql and sql servewr drivers#
The free or cheap drivers consume more time and effort to get them to work. There are good commercial ODBC products available for MongoDB, but they are generally very expensive.
Razorsql and sql servewr driver#
Although MongoDB supports joins, they aren’t fast, and it is asking a lot of a driver to bridge the divide between document and relational representations of data. The normalised form of the hierarchical data isn’t necessarily obvious, and the driver may well need help to provide the best schema. This will work, but you are losing something by the very fact that a collection of MongoDB documents, within a database, can have embedded objects and arrays that must somehow be normalised. You declare the MongoDB database as a linked server, and you can then access it and write to it as if it were a SQL-92 database. Therefore, the seasoned database developer will reach naturally for ODBC, as the most obvious way of sharing data with MongoDB. A good driver can be obtained to access almost any major application, such as Google Analytics, Oracle, Jira, Paypal, Facebook, PostgreSQL or MongoDB. ODBC also provides a standard discovery interface that enables the source to determine the capabilities and metadata of the target. Basically, it allows databases to communicate with other databases and applications by providing a standard translation layer that behaves like a standard SQL ANSI SQL-92 database. The ODBC and JDBC standards have been important in making this happen, and even now, over 25 years later, it is still an impressive way of achieving database connectivity. SQL Server has always thrived on its ability to share data with a whole range of different databases and data services. In doing so, it discusses the different requirements for interchange, and appropriate methods. This article aims to answer the question of whether there are any problems of data interchange with MongoDB. SQL Server users are accustomed to the relative ease with which they can get data from, and send data to, other databases systems.