![]() proprietaryĬonsidering open source is all about understanding how much you’re willing to carry the burden of updating and maintenance. Tools that extract, transform, and load data from a MongoDB database come in multiple flavors. Skip straight to the tools → Tooling considerations for extracting data from MongoDB This should allow you to select a tool without capability overlap in your existing data stack.īut first, let’s go over some key considerations for choosing a data extraction tool with a MongoDB connector. However, by supporting projections, an application can allow its users to select only a subset of fields.īecause there is a good chance that these capabilities already exist within your tool stack, we’ve created a list of 17 tools that don’t necessarily support mapping and inferring schema info and projections. By default, MongoDB returns all fields of a document.Applications should infer schema information by examining a subset of documents, and its users should be able to add fields that were not present in the subset.Applications should allow users to map JSON elements within documents to downstream (tabular) data structures, like support for unwinding arrays and embedded documents using the appropriate data relationships (e.g., 1:1, 1:n, and n:n).Here are some conceptual expectations that MongoDB sets: For this reason, they have developed best practices for ETL tool vendors that would like to interact with MongoDB databases and, in turn, receive certification. Despite their strengths, they recognize that all modern business intelligence practices are centered around tabular data. NoSQL’s adoption was mainly driven by software engineers who embedded these databases’ developer-friendly APIs within their applications.Īs a first-mover, MongoDB commercialized the NoSQL database and successfully went public in 2017 with a mission to disrupt the database market. MongoDB is a convenient and popular way to store data with structure that changes rapidly. It supports the storage of non-tabular data in the form of semi-structured documents. ![]() With non-tabular data, transforming the data and loading it in a table optimized for analytical jobs is less straightforward.įor example, take a NoSQL database like MongoDB. When data is extracted from a relational database, mapping the fields to their destination is a relatively easy task. ETL is the process of extracting, loading, and transforming data from various sources into a central data repository and is an essential step for turning data into insights.
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