Non-relational database systems are growing in popularity. They’re scalable, accessible, virtually always available, and they solve many of the problems and limitations of relational database models in real-time.
That’s why companies around the globe are turning to these modern technological wonders to power their businesses with big data insights.
Although amazing breakthroughs happen every day, there is a dark side to managing big data analytics with NoSQL, and it’s something we wanted to shed light on so you can make informed decisions for your own business.
Challenges of Big Data Analytics with NoSQL
- It may not be the right solution for your data
- It is not a “one-size-fits-all”
- A smaller field of experts for your in-house team
- New technology can lack support in the early years
The ability to work with unstructured data in real-time applications is a major bonus for most companies. Nonetheless, it would be unwise to overlook the potential drawbacks as you decide which kind of database makes the most sense for you.
Let’s go a little deeper into some of the more common challenges companies face when they embark on their big data journey.
Data Modeling is Never Done
The world of data modeling is rapidly expanding. That’s great, but it comes with a challenge. Data modeling in NoSQL is an ongoing process.
It’s not one of those things where you set it and forget it. You need to constantly toy with your data modeling to figure out what setup works best for what you want to accomplish right now.
That’s because data modeling in NoSQL doesn’t work the same way as in relational systems. Instead of leveraging structured schemas, non-relational databases are flexible so that they are fast, scalable, and design-friendly.
The drawback is that non-relational data modeling may not be as efficient when working with structured data which it wasn’t designed for.
NoSQL is Not a One-Size-Fits-All
When it comes to big, fast-as-lightning data, you typically have four general data models to choose from:
- Key-value store
- Document-based store
- Column-based store
- Graph-based store
Each of these data models operates differently from the others. The challenge here is that you have to figure out which data model makes the most sense for the types of data you work with, and for what you want to accomplish with your analytics.
Furthermore, each model has its benefits and drawbacks to consider.
For instance, the key-value store matches unique key pairs to store data. This setup could be useful for storing online retail information such as product details, pricing, categories, and more. Companies like Oracle and Redis use key-value pairs within their systems. A key-value store is a valid structure, but performance issues can crop up when keys are either too long or too short and this could be an issue for some.
In a document store, records are stored in a single document, which makes the model semi-structured. The information within this model must be encoded in JSON or XML, and it is never stored in a table (as you’d find in a relational database). The benefit is that complex structures can be contained in a single record. At the same time, this can be a drawback since it opens up programmers to the potential of accidentally adding incorrect data into a table.
The column-based store collects data in columns, hence the name, “column-based.” Again, this is similar to relational databases, except that relational databases store data in rows rather than columns. The core difference is that by storing data in columns, it is contained as a single, ongoing entry which speeds up the retrieval process. Unfortunately, the data entry process can be much slower with column stores when compared with relational systems, especially when dealing with large volumes of data.
The last of the main data models, the graph-based store, represents data in graphs rather than tables. The benefit of this model is that it is highly flexible and the analytics captured can easily be extended with attributes. This model also benefits from rapid search returns and faster indexing. On the downside, graph stores are much less efficient when it comes to high volumes of transactions. Likewise, they are also inefficient when working with queries across entire databases. This can be a major drawback for companies that deal in data warehousing.
Growing Base of Expertise
Let’s say you’ve reviewed your value store options, and you’ve decided that the drawbacks are worth the payoff. Not only is NoSQL more flexible and scalable, but the ability to call data up in real-time is just too good not to use.
Now that you’ve decided to go in this direction, you have to figure out who will handle your database administration.
Yes, NoSQL tends to be friendlier from a design standpoint, but because the model is much newer than relational models, there are far fewer experts available.
Although NoSQL is rapidly making headway in removing the necessity of skilled maintenance personnel, there is still room to grow. For now, there remains a need for trained experts who can install and administer the system to ensure it operates smoothly 24/7.
Until more professionals migrate into big data systems, this could be a factor that you need to consider before leaping.
The final area worth mentioning expands on the previous area. In addition to a lack of experts in the field, many NoSQL databases are built upon open-sourced projects where quality support for the development of the database isn’t always available.
Lacking in-house expertise, your NoSQL provider must be capable of delivering premium support promptly to keep your analytics flowing.
Depending on the provider you choose, you may not have the support you need when you need it most, and this is possibly the single biggest reason why some companies continue using outdated, tedious models even when faster and flashier options have arrived.
All of that said, if you choose a strong database provider, such as BangDB, many of these problems can be mitigated.
Not only does BangDB offer additional value stores beyond the limited options listed in this article, but it provides both free and enterprise versions of its database, a deep support library, and backend support for enterprise clients to ensure your transition into big data analytics is as seamless as possible.
Do the NoSQL Drawbacks Outweigh the Benefits When it Comes to Big Data?
Truthfully, no. As with any emerging technology, there will be challenges to overcome. But when it comes to building scalable systems that provide real-time analytics that you can use without spending countless hours on upkeep, NoSQL is the victor.
The key to how well NoSQL performs for your company depends largely on choosing the best database provider for your business and ensuring that whoever you choose offers helpful resources, support, and ongoing services to keep you running like a well-oiled machine.
At BangDB, we pride ourselves on delivering all of that, and we even have an option to get started for free. If that sounds fair to you, then go here to download BangDB now and start building your big data analytics system today.
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