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MongoDB and BI Woes Explained Through the Summer of Love

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MongoDB and BI Woes Explained Through the Summer of Love

MongoDB might be groovy and BI might be far out, but it still comes with some headaches. Look at these challenges through the lens of the Summer of Love in honor of its 50th anniversary.

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2017 marks is the 50th anniversary of the infamous Summer of Love: the moment that free-wheelin’, flower-powered, psychedelic, hippy culture exploded onto the scene as thousands flocked to the Monterey Pop Festival in California to see icons like Janis Joplin, Jimi Hendrix, The Beatles, The Who, Jefferson Airplane, and The Grateful Dead rock the stage. It was a major moment in music history… but let’s spare a thought for the plight of the organizers of that famous event.

These guys had picked out the most incredible bands and musicians of the day, but can you even imagine trying to manage them?

A free-and-easy, no-rules, stick-it-to-the-man mentality might be a seductive philosophy for life but harder to reconcile when it’s your job to track down a string of musical geniuses before their set and keep everything running according to schedule, several days in a row. To make it work, you’d have to intuitively understand the value of both ways of thinking, and be incredibly skilled at bridging the gap between them.

Freedom, Give It to Me…

Like Hendrix, MongoDB’s popularity soared thanks largely to an amazing blend of power and freedom. It doesn’t play by rigid, SQL-based database rules, and that’s what makes it so exciting.

Typically, businesses embrace this impressively flexible, open-source NoSQL database technology when they need to partition and shard data, are dealing with a high write load/location-based/massive data sets, are working with less-than-reliable environments (in which a data center could potentially fail), or when they need to make sure the volume of data they process won’t slow down any networked applications.

The trouble is, while MongoDB makes all this possible, it can present a major stumbling block to well-functioning BI.

Like a Ball and Chain

Unstructured schemas are superb for giving you unparalleled flexibility and freedom, but they can be a real pain when you actually want to use that data for analytics.

Squishing all that free and easy data into something coherent enough to draw useful insights from is tough. It takes an agile and intelligent renormalization process to make all that data consistent and measurable and to allow you to mash up data sources drawn from all over the place into a single pool you can work with.

What’s more, like much of Jefferson Airplane’s back catalog, MongoDB only really makes sense if you’re in the same headspace as its creators. Non-techie, non-development, business-focused types tend to view it with befuddlement, meaning you need a reliable way to translate its value into something they can use and understand.

Finally, MongoDB’s open-armed approach to new data is fantastic in theory: there’s room for every kind of data, no matter what type or in what volume, so your data pool can just keep growing and growing… if your physical infrastructure can keep up.

Unless you have extremely deep pockets or boundless capacity, you’ll need concrete ways to automate reporting and processes wherever possible, and to reduce pressure on your hardware, in order to make top BI available to everyone in the organization that needs it.

Stuck in the Middle With You

Who would benefit from more streamlined projects? Well, everyone in the organization, in different ways.

Let’s start with your C-Suite. These guys need a comprehensive overview of how the business is doing, which incorporates all data sources and doesn’t leave out anything that might be valuable.

Then there are your analysts. They need to be able to run ad hoc, tailored queries to answer vital business questions on the fly — and get the answers they need fast, with sufficient detail and context, in a language that makes sense to them.

Meanwhile, your developer team might be perfectly comfortable working with the back-end of your BI platform and MongoDB, but handling all of this manually for every query is hardly a sustainable way to work. They need a reliable way to bring together all the different strands of information, including data from MongoDB, to run rapid reports, and deliver powerful, accurate insights to their colleagues, in appropriate formats, without massively increasing their workload.

Come Together, Right Now…

Luckily, there is a way to make it work! Here at Sisense, we joined forces with Simba Technologies to build a Sisense-MongoDB connector that uses ODBC connectivity to allow you to tap into any MongoDB database.

This means that you now get to have the best of both worlds, making use of MongoDB’s liberated approach, while being able to query and analyze all the chaotically disorganized data in your MongoDB database using Sisense’s Business Intelligence tools.

This allows you to prepare data for use in the most effective way possible and eases much of the pressure on your IT team by providing direct access to the data to your non-technical team, allowing them to interpret, query, and visualize their data through self-service dashboards, without you having to help them through it every time.

Even better, you can run queries in SQL, even though MongoDB is built on NoSQL architecture. You can automate the download of MongoDB into CSV files and pull this into the Elasticube in a few simple steps. You don’t even have to learn MongoDB’s query language or API, and you can customize the schema in a simple, intuitive way using Sisense’s schema editor.

Essentially, we’ve set everything up for you to be completely compatible so that you can hook up your MongoDB database and start incorporating it into your data mix immediately. Or, in other words, we’ve figured out how to herd the hippies onto the stage and get them to perform. All you need to do is buy your ticket.

I Can See for Miles and Miles and Miles

MongoDB enriches your data projects substantially, but by itself, it certainly won’t streamline anything when it comes to analytics.

You absolutely need a reliable middleman to help you extract its full value, reconcile this with a wider BI platform, and help you to automatically normalize and prepare your data for deep-level analysis.

This applies no matter what your role in the broader organization. Striving to create a coherent, self-service system for BI that doesn’t compromise when it comes to the quality of your insights or limit creative control over what you can do with the data, is a major point of frustration.

This is precisely why Sisense’s connector is so important. It opens up an incredible wealth of insights, allowing you extensive visibility over the health and performance of the organization without sacrificing the incredibly granular detail that MongoDB facilitates.

By merging MongoDB and Sisense BI, you create a 360-degree view of the whole business based on a single version of the truth, that anyone in the organization can use to gain the answers they need — all by themselves.

Far out, man.

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Topics:
mongodb ,bi ,big data ,data analytics

Published at DZone with permission of Shelby Blitz, DZone MVB. See the original article here.

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