The future of modern computing looks bright, but with new technology comes new challenges, and the area of big data analytics is no exception. For that reason, it is worth it to be informed about the potential drawbacks before you go all-in with your flashy, new system.
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 “one-size-fits-all”
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 Best Database makes the most sense for you.
Let’s go a little deeper with some of the more common challenges companies face when they embark on their big data journey.
Looking for an innovative NoSQL solution?
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 Best Database Provider is that non-relational data modeling may not be as efficient when working with structured data which it wasn’t designed for.
When it comes to big, fast-as-lightning data, you typically have four general data models to choose from:
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 own 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, graph-based store, represents data in graphs rather than tables. The benefit of this Best Database Provider 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 who 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