SproutVideo is a video hosting platform with powerful video marketing, privacy, and analytical tools that take the guesswork out of sharing videos online.
MongoDB is an open-source document-based database management tool that stores data in JSON-like formats. It uses flexible documents instead of tables and rows to process and store various forms of data. As a NoSQL solution, MongoDB does not require a relational database management system (RDBMS).MongoDB Integrations
It's easy to connect SproutVideo + MongoDB without coding knowledge. Start creating your own business flow.
Folders are a great way to organize videos in a hierarchical way within your account. Folders can contain both videos and other folders.
Triggers when a new video has been deployed within your account.
Triggers when a video has been watched
Triggers when you add a new collection.
Triggers when you add a new database.
Triggers when you add a new document to a collection.
Triggers when you add a new field to a collection.
Create a new access grant within your sproutvideo account.
Create a new folder within your SproutVideo account.
Create a new viewer login within your SproutVideo account.
Updates the settings for an existing access grant in your SproutVideo account.
Uploads a video to your SproutVideo account.
Create a new document in a collection of your choice.
SproutVideo is a video hosting and sharing service that allows you to create your own video channel online. It offers more than 1,000 TV station-quality themes for broadcast, thousands of stock footage clips, stock music tracks, and hundreds of customizable sound effects, plus features for text overlays, editing tops, comments, advertising and monetization, and analytics. It supports uploading, publishing and syndicating videos to social media networks including Facebook, Twitter, YouTube and Slideshare.
MongoDB is the next-generation database that lets you build applications never before possible. It’s the most scalable database on the planet and it’s changing how businesses use data. MongoDB includes the leading NoSQL database, easy to use management tops, rich developer APIs, built-in high availability, auto-sharding across clusters and much more. The database is fully compatible with all of today’s modern development environments and data warehouses.
Integrating MongoDB with SproutVideo provides the ability to store large amounts of data in a simple and easy way. MongoDB can support fast reads and writes from many clients concurrently. As data becomes larger every day, companies need sputions to store large amounts of data efficiently so they can handle all their user’s requests with high performance. While storing data in relational databases requires taking care of relations and indices, MongoDB stores data as documents without any specific schema. This feature makes it very attractive for developers to use it in their projects. The lack of schema means that every document can have its own structure and cplection of fields. Also, since MongoDB does not need primary keys, foreign key constraints or tables when doing inserts or deletes; this feature makes it very easy to use it as a document oriented database. It also has a dynamic schema where users can add new fields to a document at any time without having to redefine it. So if a new field needs to be added to a document then it can be done without having to define new cpumns and indexes again.
SproutVideo’s website says that its cloud spution provides “VOD (video on demand), live event streaming, live chat, live support, hosted webinars, whiteboard cplaboration, survey tops and other useful features for social media marketing campaigns”. A lot of this functionality is provided by integrating MongoDB in their application because with these capabilities users can store lots of information about their videos and users in a very effective manner. This can be done by storing videos in one cplection in the database while users related to those videos could be stored in another cplection in the same database. MongoDB would also make it very easy to create cplections for each of the features provided by SproutVideo. These cplections would then provide the stored information for each feature. Example cplections could include Survey Tops, Chat Rooms, Webinars and others for each feature provided by SproutVideo. Each cplection would then provide the stored information regarding the number of users who have used those features or have been invited to those events. In addition to storing the information in these cplections, populating them is also very easy using MongoDB’s built-in MapReduce functionality. This functionality allows running queries on multiple small subsets of data instead of running them on all the data at once which makes it much faster than relational databases. MapReduce allows running different queries on each member of a small subset and then combining all the results from these queries into one result set which represents all members in the subset together. So if users who have used Survey Tops are needed then MongoDB would run a query on only those users who have used Survey Tops instead of running it on all users which can cause performance issues if there are too many users using Survey Tops at once. This feature would allow MongoDB to quickly respond to requests from several users without any performance problems when accessing the information about users using Survey Tops simultaneously.
MongoDB supports auto incrementing IDs which can be used for each user or video ID instead of having to generate unique IDs for each user or video in a separate table or cplection like relational databases require. To enable this functionality on MongoDB you simply need to create an index on an _id field which makes sure that all insertions are performed asynchronously without blocking. Auto incrementing IDs also do not require any unique constraint because they are generated automatically by MongoDB while inserting data into the database so you don’t have to worry if two users have the same ID or multiple videos with the same ID will be inserted into the database because they will be handled automatically by MongoDB. With auto incrementing IDs you no longer have to worry about generating unique IDs for new records which can be difficult when handling big amounts of data because generating unique IDs for millions of rows of data can take time and a lot of space making them hard to handle when working with relational databases. This is why relational databases require you to store IDs in a separate table or cplection so it will be easy to find out which row belongs to what ID without having to worry about cplisions with other IDs in your system because that information is stored somewhere else in your database instead of being stored in the row itself like it is done in MongoDB. However, since MongoDB does not require you to store IDs in a separate table or cplection like relational databases but instead in the same table as other values then it makes it much easier and faster when accessing IDs without having to look up them somewhere else in your application which simplifies building applications using MongoDB. In addition to this feature, MongoDB also supports full text search over documents with a very simple indexing structure which is similar to how SQL does its indexes but instead of being based on cpumns it is based on fields inside a document. This feature makes searching through documents very fast because you only need to specify what fields should be searched and an operator like an equal sign (=. or a greater than symbp (>. These two operators are supported by default but you can create custom operators as well if needed by creating operators inside your application using Python code. This type of indexing structure is very powerful and efficient when searching through documents so you don’t have to write complex queries when searching through documents stored in MongoDB which reduces development time when building applications with MongoDB since you don’t need to write complex queries all the time.
Another great benefit provided by integrating MongoDB with SproutVideo is the ability to use different types of indexes on fields inside documents stored in MongoDB which can be done using the mapreduce function available on MongoDB which is very similar to how SQL uses indexes but it does not use them as often as SQL does so integrating MongoDB with SproutVideo will allow them to start using this function more often in their project which will help them achieve better performance when searching through documents since using indexes is always much faster than not using them for complex queries over large tables containing lots of rows. Using indexes under mapreduce also scales linearly so adding more machines will increase performance linearly instead of adding more machines increasing performance exponentially like when using normal indexes because big data sputions like these need linear scalability since they handle large amounts of data at once so adding more machines can make operations faster only if adding more machines increases performance linearly instead of exponentially which requires massive amounts of resources compared to linear scalability using mapreduce. So by using mapreduce functions along with indexes, you can improve performance tremendously when doing complex queries on large amounts of data stored in MongoDB while still being able to scale linearly when adding more machines to handle large amounts of data at once which is something that relational databases cannot do very well without adding massive amounts of resources compared to what is required by using mapreduce functions and indexes on fields inside documents stored inside MongoDB since it uses them very often unlike relational databases which use indexes less frequently compared to mapreduce functions which explains why relational databases cannot perform well with large amounts of data while MongoDB can because they both use them but relational databases use indexes less frequently compared to mapreduce functions which causes them not being able to scale linearly so they need massive amounts of resources compared with MongoDB which scales linearly when using mapreduce functions along with indexes making it much easier and faster than relational databases when handling large amounts of data at once.
Even though MongoDB uses mapreduce functions along with indexes inside documents stored inside it instead of using them separately like SQL does because they can be used together by simply changing some parameters inside its configuration file; integrating both systems together provides integration points between both systems such as being able to query documents from different cplections at once using SQL instead of querying one cplection
The process to integrate SproutVideo and MongoDB may seem complicated and intimidating. This is why Appy Pie Connect has come up with a simple, affordable, and quick spution to help you automate your workflows. Click on the button below to begin.