MongoDB is a great document store database which can be used in most types of app development projects.
MongoDB is built using C++ language, and it is free of charge (there’s an open-source edition). It was created in 2007, and it has only grown more popular ever since. Nowadays, many believe that it has become the most frequently used NoSQL database.
However, just because it is so great doesn’t mean that it can work on its own. Take a look at six things to know before you start with MongoDB implementation at your organization.
Use the right tools
No one can deny that getting information from a database and getting it in an understandable form is one of the most important steps in the process of intelligible insights extraction from the data. This is no different even when you have a MongoDB (NoSQL database) implemented at your company.
A problem many organizations face once they begin gathering and storing data in various places is reconciling data from traditional relational (SQL) and NoSQL databases.
Panoply is one of the very few efficient MongoDB ETL tools that help with a MongoDB database.
It is a bit different from the vast majority of ETL tools because the all-in-one data platform combines the ETL process with a managed cloud data warehouse. In simpler terms, importing data from MongoDB and other popular data sources just involves a few clicks without having to define a data warehouse schema beforehand.
It also handles all the scaling and query performance optimization automatically. This makes it easy for data users of all levels to access MongoDB data easily with SQL.
MongoSyphon is a light, open-source ETL tool that transforms data into documents in JSON or XML format. It can also work the other way, sending documents directly into MongoDB, differing from other ETL tools that try to create relational structures.
MongoSyphon is a great tool for individuals with intermediate to advanced knowledge of SQL, since there is no GUI involved.
Transporter is an open-source tool that transfers data between various databases. It was created by Compose and it links to different databases through the use of adaptors, which can be configured into a data source and/or sink.
Of course, the MongoDB adaptor has dual functionality, and it can read from or write to MongoDB databases. The adaptor establishes communication between each database by converting data to JSON documents.
Krawler is an open-source ETL tool designed by geospatial consultants Kalisio. The tool’s main purpose is to extract geographic and geospatial data and convert it into more readable formats. Krawler also has a priority of reducing the time it takes to download and analyze information.
Enjoy great performance
The main advantage of MongoDB over other databases is its performance under pressure.
This is especially true when managing ad hoc queries on data that are usually updated in real-time.
MongoDB can retrieve specific fields, ranges, locations, values as well as respond to regular expression queries. MongoDB uses SQLesc kind of query language, so it is not difficult to learn, and its capability in handling dynamic queries sets it apart from CouchDB, which requires setting up views first.
Many companies carried out experiments for MongoDB’s performance and one time a company found that MongoDB can handle up to 5 queries per millisecond without a problem.
MongoDB is consistent
MongoDB is best situated for applications that require consistent data presentation for users.
So, if your main goal is the availability of a database only for data entry during data visualization, consistency is not a problem. You are safe to go with CouchDB or some other rival.
However, if that is not the case, think well before making the final decision. Take into account the performance characteristics before anything else.
The order of stages
Don’t forget that the order of stages in aggregation is crucial. In a database system with a query optimizer, the queries that users write are explanations of what they want rather than how to get it.
It is similar to ordering in a restaurant. One would usually just order the dish rather than give detailed instructions to the cook regarding the way the dish should be cooked.
Well, in MongoDB, users are instructing the cook. Essentially, a user needs to make sure that the data is reduced as early as possible in the pipeline through $match and $project, sorts happen only once the data is reduced and that lookups happen in the order that the user intends.
Having a query optimizer that removes unnecessary work, orders the stages optimally, and chooses the type of join can somewhat spoil users. MongoDB gives users more control, but at a cost, which translates to less convenience.
Load balancing on multiple servers
The vast majority of document storage databases are equipped with features to allow scalability over multiple servers. Contrary to SQL databases, multiple server-based databases can be easily clustered in NoSQL.
MongoDB is no different, but experts found that its performance on multiple server-based apps is better in comparison to CouchDB. However, it is fair to say that their use cases were a bit biased towards MongoDB during those experiments.
JSON-based document storage schema
For instance, YouTube API outputs its data in JSON format, and that means that MongoDB is great at handling requests of that nature.
As you can see, MongoDB really is a great solution. It works great for projects that require schema-less storage of data, and it has the capability to handle gigantic databases.
The database is best suited for creating CMS platforms, blogging platforms, data analytics platforms, eCommerce websites, document storage portals, Metadata storage, and location-based applications.
Now you most likely have no doubts that MongoDB is one of the most popular databases of its kind today. So, it’s time to see how you can implement it at your own company to better handle all the data.