Are you looking for the right environment to perform real-time analytics? Do you find it confusing to choose between a SQL or NoSQL database like MongoDB? Well, look no further! This article will answer all your queries. Follow our comprehensive guide to understand MongoDB real time analytics and how you can use it to track performance metrics and make effective data-driven decisions.
This article gives you in-depth knowledge about the factors to consider before selecting a SQL or NoSQL environment for your analysis work.
Upon a complete walkthrough of the content, you will be able to perform MongoDB real time analytics with ease. It will also help you choose the correct environment that suits your business needs.
Table Of Contents
What is MongoDB?
It is a high-performance document-oriented database that is powered by a NoSQL structure. It makes use of collections (tables) each having multiple documents (records) & which allows the user to store data in a non-relational format.
MongoDB stores its data as objects which are commonly identified as documents. These documents are stored in collections, analogous to how tables work in relational databases. MongoDB is known for its scalability, ease of use, reliability & no compulsion for using a fixed schema among all stored documents, giving them the ability to have varying fields (columns).
For further information on MongoDB, you can check the official site here.
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Prerequisites
- Working knowledge of MongoDB.
- MongoDB is installed on the work host station.
- A general idea about SQL and NoSQL databases.
- A general idea about real-time analytics.
SQL Databases Vs NoSQL Databases For Performing Real-Time Analytics
SQL databases are known for their flexibility in terms of allowing users to retrieve, filter & aggregate data from various tables. It even allows users to combine data from multiple tables using joins.
Data integrity has been a shining feature of SQL databases. This ensures that data is validated across the tables, thus ensuring no unauthorized insertion in the table. SQL databases are known for consistency, and regularity and work exceptionally well with complex queries. SQL, although convenient and powerful, it falls behind in certain aspects. In recent times of frequent changes in terms of business or user requirements, relational databases have suffered due to many reasons:
- Fixed schemas have been a major setback for such databases, making them ill-suited for changing business.
- Issues related to limited scalability.
These limitations have led to the NoSQL databases gaining a lot of popularity as they manage these limitations quite easily. NoSQL databases are built for operational needs, for real-time applications. They support horizontal scaling and are used for storing millions of records. They support massively parallel & high-performance data processing that can cope with today’s data demands. These are designed to counter the increasing data complexity and its handling.
NoSQL databases provide the ability to store and access unstructured data. They support the high-performance processing of information even at a massive scale. These databases also support exploratory & predictive analysis, thereby making them the ideal solution.
The decision of choosing between SQL & NoSQL depends on the nature and volume of data an organization is working with. If the work requires geospatial queries, text searches, or a lot of image processing power, then a NoSQL database would be the ideal solution. On the other hand, if the data can fit in a plain spreadsheet or isn’t massive in volume then a SQL database would be the correct option.
How to perform MongoDB Real Time Analytics?
MongoDB wasn’t originally developed for analytics however with data growing at an exponential rate and the need for having real-time capabilities, in terms of monitoring updates or availability of data, becoming more and more fundamental, it needed these features to survive.
MongoDB has grown immensely with time to counter such requirements and hence now it supports a lot of analytics capabilities directly built-in the database. There are mainly two methods to perform analytics using MongoDB:
Method 1: Replicating A MongoDB Database Into A SQL Database
Replicating data into a SQL database allows users to keep on using MongoDB as their production database and use the relational format to analyze data with ease. SQL can now be used on this relational version of MongoDB data. This allows users to access and manipulate data with ease and combine data from multiple tables using indexes to perform insightful analysis.
SQL brings in a lot of conveniences when working with lengthy aggregations and complex data joins. However, data replication is not as easy as it sounds. This requires an ETL job which might be complicated as it requires transferring data from a NoSQL environment to a SQL environment. These ETL jobs also need external hardware and the support of data engineers & analysts to work properly.
Method 2: Data Virtualization
Data virtualization is a method that can be used for MongoDB real-time analytics. This method is the ideal solution to counter the limitations of replicating databases.
Various tools provide an interactive & user-friendly interface. These tools can be connected with MongoDB with ease and allow the users to query or manipulate their data stored in MongoDB. Users can now develop visualizations and perform real-time analysis in just a few clicks making use of smart & easy to use dashboards and customer-facing reports. The advantage here is that it doesn’t require any additional hardware or tedious ETL jobs to analyze data.
One such tool is Apache Spark. MongoDB supports this popular framework that is loved by data scientists, engineers, & analysts. MongoDB provides powerful large-scale analytics features. These allow users to perform analysis within the platform by converting data into visualizations along with a parallel query execution engine to boost performance.
MongoDB also supports a SQL-based BI connector that allows users to explore their MongoDB data using different business intelligence tools such as Looker, Microsoft Power BI, and others.
For further information on the BI connector for MongoDB, you can check the official site here.
What are the Advantages Of MongoDB Real Time Analytics?
- Ad-hoc Queries: MongoDB supports ad-hoc querying. It is very flexible and supports all different kinds of data.
- Powerful Analytics: MongoDB supports real-time analytics with a wide variety of data. It allows for performing analysis on geospatial data, secondary data, and even on text searches. It has strong integrations with aggregation frameworks & the MapReduce paradigm.
- Speed: MongoDB being a document-oriented database, allows you to query data quickly. Its rich indexing capabilities allow it to perform way faster than a relational database.
- Easy Setup: MongoDB can be set up easily on any system.
- Data Adaptability: A NoSQL system like MongoDB supports a wide variety of data such as text data, geospatial data, etc. It provides an ultra-flexible data model making it easier to incorporate data and make adjustments for better performance.
- Scalability: NoSQL databases are built to scale. MongoDB’s sharding capability allows it to distribute data across datasets, servers, etc. This gives it an unlimited growth capability and a higher production rate than a relational database.
- Real-Time: With MongoDB, you can analyze data of any structure within the database and get real-time results without costly data warehouse loads.
What are the Disadvantages Of MongoDB Real Time Analytics?
- Memory Constraints: MongoDB leads to unnecessary usage of memory. It stores every key-value pair and hence suffers from duplication of values.
- No Support For Joins: MongoDB doesn’t support joins. Joins are implemented using programming languages such as Java, however, this makes the querying complex & hampers the performance.
- No Referential Integrity (RI): These are the defined and validated relations between different pieces of data. RI helps to keep the information consistent and adds another layer of validation underneath the programmatic one.
Conclusion
This article introduces you to the various methods used to perform MongoDB real-time analytics. It also provides you with in-depth knowledge about the factors to consider before selecting a SQL or NoSQL environment for your analysis work. These methods, however, can be challenging especially for a beginner & this is where Hevo saves the day.
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Skilled in freelance writing within the data industry, Nicholas is passionate about unraveling the complexities of data integration and data analysis through informative content for those delving deeper into these subjects. He has written more than 150+ blogs on databases, processes, and tutorials that help data practitioners solve their day-to-day problems.