The Five Money-Saving Tricks MongoDB Doesn’t Want You To Know
MongoDB continues to grow rapidly and is the go-to solution for companies large and small that need a powerful and flexible modern database. However, if you listen to your friendly MongoDB sales rep, it’s easy to think they are a one-stop shop for all things MongoDB.
However, a bit of quick research quickly reveals there are alternatives for completing your MongoDB-based infrastructure that will save you money and yield better results.
Here are 5 tips that will save you tons of time and money!
1. Ditch MongoDB Cloud Manager
MongoDB Cloud Manager provides automated management and backups for your environment. While this can be a key element of any application infrastructure, you have other options. Companies like ScaleGrid offer similar features as Cloud Manager for less cost. You can use MongoDB Community with a service like ScaleGrid and have the best of both worlds.
2. Get Support from a Third-Party
Users need to upgrade to MongoDB Professional or Enterprise in order to get support for the database. If you have a production environment, support is critical to insure minimal downtime, and getting support from the vendor isn’t a bad idea. Unfortunately, the support comes with a super high price tag.
An excellent alternative is Percona. Percona is a veteran technology company that has provided support and services for open source MySQL for years. They have added support for MongoDB in recent years, and the reviews are glowing. Percona offers more flexible support options at better prices, and if you happen to have other open source databases like MySQL in production, then you have one-stop support shopping.
3. Swap MongoDB Compass for Open Source
MongoDB expects users to upgrade from Community Edition to Professional Edition to gain access to the Compass tool for data exploration and schema validation. Compass is useful, and the GUI is decent, but it is limited in what it can actually do from an analytics perspective.
An open source alternative is the SlamData project. SlamData is the most popular native tool for exploring and analyzing data in MongoDB, and allows users to discover, search, query and visualize any data stored in MongoDB.
SlamData has a powerful and flexible UI that makes it super simple to create reports and dashboards in minutes. SlamData works natively on the data stored in MongoDB, no ETL, data mapping or extraction of any kind. It pushes 100% of the computation down to the live data, so as your data changes so do your analytics, in real-time.
4. Kiss BI Connector Goodbye
MongoDB requires users to upgrade from Community to Enterprise Advanced in order to gain access to the MongoDB BI Connector (MBIC). This tool allows users to connect their MongoDB database to popular BI tools by leveraging the PostgreSQL Foreign Data Wrapper (FDW).
There is an open source alternative, the Quasar BI Connector for MongoDB (QBIC) that provides similar functionality, and in several cases, better performance than MBIC.
No need to pay for Enterprise Advanced to get MBIC. The Quasar BI Connector uses the popular Quasar NoSQL analytics engine in conjunction with the PostgreSQL FDW to make it possible for any BI tool to connect to data stored in MongoDB.
5. Exploit the Server Loophole
If you feel compelled to upgrade to MongoDB Professional or Enterprise Advanced, then keep it small, since MongoDB charges by the server!
However, there’s a loophole you can exploit. MongoDB defines a “server” as 512 GB of memory, regardless of how many physical or virtual devices share this memory. So you can spread the 512GB across many servers and get more bang for your license buck!
In summary, the MongoDB open source ecosystem continues to grow, and like most situations in life, it pays to do your homework when building out your solution environment.
There is a tremendous amount of innovation occurring and users can benefit from this both technically with better solutions and financially.
One-stop shopping does not get you the best solution!
Latest posts by John De Goes (see all)
- Battle of Open Source Analytics: Spark vs Drill vs Quasar - July 20, 2016
- The Five Money-Saving Tricks MongoDB Doesn’t Want You To Know - July 19, 2016
- 5 Killer Reasons to Upgrade to SlamData 3.0 - July 14, 2016
Native Analytics On MongoDb, Couchbase, MarkLogic and Hadoop.
No mapping. No ETL.
Recent News & Blogs
Boulder, Colo., February 23, 2017 – SlamData Inc., the leading open source analytics company for modern unstructured data today announced that it has raised a $6.7M Series A funding round, led by Shasta Ventures. The investment will drive further development of the firm’s breakthrough analytics solution: a single application for natively exploring, visualizing and embedding analytics against unstructured data sources including NoSQL, Hadoop, and cloud API’s.read more
SlamData just released its first update of 2017, SlamData 4.1.1. It delivers a number of new UI enhancements, performance improvements, new charts, as well as commercial releases for the Couchbase, MarkLogic and Spark/Hadoop connectors.read more
Welcome to the SlamData getting started video. Let’s jump right in. By default, SlamData runs on port 20223. You can change the port it runs on by modifying the quasar-config.json file. By default, this file is located in the following directories on Windows, Mac and Linux.read more
To help shed light on where customers can go to address their data-driven challenges, Database Trends and Applications magazine assembles an annual list of solutions…read more
The following is an interview I conducted with Jeff Carr, CEO and Founder of SlamData regarding the trends in enterprise business intelligence.read more
Enterprise Business Intelligence solutions are failing, and the reasons are very obvious. Leading analyst firms including Gartner and G2 have published rankings which show virtually no leadership and scant challengers in the market.read more
Who Is Using SlamData?
Whitepaper: The Characteristics of NoSQL Analytics Systems
by John De Goes, CTO and Co-Founder of SlamData
Semistructured data, called NoSQL data in this paper, is growing at an unprecedented rate. This growth is fueled, in part, by the proliferation of web and mobile applications, APIs, event-oriented data, sensor data, machine learning, and the Internet of Things, all of which are disproportionately powered by NoSQL technologies and data models.
This paper carves out a single concern, by focusing on the system-level capabilities required to derive maximum analytic value from a generalized model of NoSQL data. This approach leads to eight well-defined, objective characteristics, which collectively form a precise capabilities-based definition of a NoSQL analytics system.
These capabilities are inextricably motivated by use cases, but other considerations are explicitly ignored. They are ignored not because they are unimportant (quite the contrary), but because they are orthogonal to the raw capabilities a system must possess to be capable of deriving analytic value from NoSQL data.
Table of Contents
- The Nature of NoSQL Data
- NoSQL Databases
- Big Data
- A Generic Data Model for NoSQL
- Approaches to NoSQL Analytics
- Coding & ETL
- Real-Time Analytics
- Relational Model Virtualization
- First-Class NoSQL Analytics
- Characteristics of NoSQL Analytics Systems
- Generic Data Model
- Isomorphic Data Model
- Unified Schema/Data
- Polymorphic Queries
- Dynamic Type Discovery & Conversion
- Structural Patterns