![]() ![]() For example, you can take a look at all the data in collection1: Lastly, you can immediately start with queries of all sorts to evaluate the data from your NoSQL data source with the usual SQL expressions that you are familiar with. Table representation of the MongoDB collection’s documents Step 3 – Use When the server is done, you have virtual tables with a virtual schema that are ready to be used: ![]() In addition, it is also possible to set the maximum depth which is to be searched through within the document entries. When you know more about the structure of the data sources, you can exploit this and tell the server how many documents have to be analyzed at most to get the schema. This process, however, might take some time because the properties that can occur in the document entries of the collection have to be found. The data schema is presented to you and can be used like any ordinary relational database. Step 2 – Wait and seeĪfter you click ‘finish’, the Data Virtuality Server retrieves all the necessary information to build a virtual data structure in a relational style. Select the MongoDB entry from the list of data sources.Įnter all required information to establish the connection. Go to the dialogue in order to add a new data source. Usually, of course, you would need to provide some. For the sake of this tutorial, a simple MongoDB server was used with no additional credentials required. Enter your credentials and the correct connection parameters, and leave the rest to Data Virtuality. Simply go to the dialog to add a new data source and select “MongoDB”. Step 1 – Connecting to the MongoDB Data SourceĪdding MongoDB is just as easy as could be. Below are the steps you take to be able to query MongoDB databases with Standard SQL Statements. Apart from connecting to the NoSQL source and waiting for the structure to be detected by Data Virtuality, no more steps are necessary. Through the coherent structure of tables and the sophisticated transformation that Data Virtuality performs automatically, MongoDB can easily be accessed and queried with just regular SQL statements, and the content can be combined with other data sources, such as relational databases. So how can you query MongoDB Databases with standard SQL statements in an easier wayĪll the manual processing is not necessary anymore if you use the Data Virtuality Server. This is, strictly speaking, possible but it requires a lot of time because special processes are needed that allow for transformations of the given NoSQL database so that you have a common data model, which suits your needs. Clearly, if there aren’t any tables and not all entries from a collection have a specific property then you cannot use a typical SQL syntax:īut a more serious drawback is the hardship you have to undergo if you want to combine a MongoDB collection with a regular relational database and evaluate it. First, there is the problem of incompatibility with ANSI-SQL and its derivatives. ![]() Unfortunately, this flexibility comes with two trade-offs when implementing and actually using MongoDB. Here is a quick example of what different entries from the same collection could look like: But each also has some extra properties that are important to it and are stored together. ![]() NoSQL, which is an abbreviation for ‘Not only SQL’, offers the possibility to have data sets (collections) that consist of entries which might have a few properties in common. A widely known representative of this approach is MongoDB (Mongo = humongous). One of its major achievements is to allow for a more flexible approach in terms of data structure. The world of databases in general, and of Big Data analysis in particular, largely make use of the paradigm introduced by NoSQL. In this post we will show you an easier way to query MongoDB databases using Data Virtuality. Combining a MongoDB collection with a regular relational database in order to assess it comes with a lot of challenges, especially when it comes to how much time you need to spend. ![]()
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