Why Hybrid NoSQL Architecture is Indispensable for IoT

Before we talk about hybrid architecture, let’s go over what IoT actually describes in case the term feels kind of fuzzy for you like it does for most people. 

The Internet of Things refers to the way everything is becoming connected. From your smartphone to your home to your car and beyond, all technology is moving toward a place of constant connection and interaction. 

To keep up in business, you have to think about more than just the user experience on your website or app. How will a user interact with your business on their commute to work? What about when they’re on the treadmill, or when they are cooking dinner or washing dishes?

The IoT exists in a perpetual state of evolution, meaning new use cases and scenarios pop up every single day. For your business to stay on top of the latest technological developments, and to be part of this endless cycle of connection, your apps, platforms, and operations must all work seamlessly together, and that is where a hybrid NoSQL Architecture comes in.

iot

What is a “Hybrid NoSQL Architecture”?

When it comes to database management, you generally have two options:

  • Relational Database (SQL)
  • Non-Relational Database (NoSQL)

Relational databases were the first to emerge and have been used across the past several decades to store and retrieve information, and to fuel various types of businesses. This type of database uses tables to maintain structured data in rows. Since it was designed before the IoT, the SQL database has struggled in many ways to maintain relevancy and is challenged in areas such as affordability, scalability, and flexibility.

Non-Relational databases were created to solve many of the shortcomings of relational databases. NoSQL databases are far more scalable and flexible, in addition to being much faster to the point of returning results from search queries in near real-time. NoSQL databases operate using less structured and unstructured data stores and are cloud-based to ensure they maintain 24/7 availability.

Hybrid architecture is a combination of different database models. Specifically, a hybrid architecture empowers you with the ability to work with SQL and NoSQL together within a single system. 

But why would you use a hybrid approach when NoSQL is better than SQL in virtually every possible way? Why do you need both?

Infrastructure Considerations

The most obvious reason for the necessity of a hybrid model is that many businesses have built their entire operation around relational database systems. 

In other words, it would be very difficult, extremely time-consuming, and cost-prohibitive to completely switch from one model to another. Yet, modern businesses must evolve with technology if they want to stay relevant.

Enter the hybrid NoSQL architecture. 

Hybrid NoSQL architectures are capable of managing many SQL applications, which makes them somewhat backward compatible. The system allows businesses to implement the features of NoSQL without sacrificing their relational database infrastructure.

By using a hybrid approach, your business can enjoy the best of both worlds. You can continue your operations uninterrupted while giving yourself a boost with the power of real-time analytics, data, and performance afforded by adding NoSQL.

What Does This Look Like in Practice?

Hybrid databases take advantage of a multi-faceted approach to data storage and retrieval. By storing and returning data with physical disks, and by leveraging in-memory data for active performance enhancement, hybrid database systems can support multiple operations with improved speed and efficiency.

On-Disk Database

The core benefit of leveraging on-disk systems is that physical disks have enormous storage space that can hold loads of data beyond the in-memory capacity. The one pitfall is that retrieving data from a physical disk is a much slower process than pulling it from in-memory.

In-Memory Database

Unlike physical disks, memory-based storage can rapidly recall data for retrieval. Unfortunately, the storage capacity for in-memory is much less than what a physical disk can hold. For this reason, a hybrid system that leverages both can create a powerful in-between solution.

Other Benefits of Hybrid NoSQL Architecture

In addition to speed and storage capacity enhancements, hybrid architecture also offers businesses the following advantages:

  • Affordability: Physical disk storage costs much less than in-memory storage, which means you can increase your storage capacity anytime without eating into your bottom line.
  • Flexibility: A hybrid NoSQL architecture gives you the ability to perform Hybrid Transactional and Analytical (HTAP) processing. This means you can simultaneously execute transactional and analytical processes without bogging down your database.
  • Multiple Data Stores: The biggest limitation of relational databases is found in the way they store and retrieve structured data using rows. With a hybrid database, you can manage your data in rows, columns, and other formats.
  • Resource Freedom: Since hybrid databases can be launched in the cloud, it means you can free up local resources. While you can still launch your database services locally, you don’t have to, and that gives you a lot of freedom when it comes to your in-house resources.

Why Is This Important for the IoT?

There are times when it doesn’t make sense to use a hybrid setup. Some businesses should stick with relational models while others should go all-in with non-relational models.

When your business is limited in size, and you don’t have plans to add apps or real-time features, and when constant database upkeep isn’t that important, then you might be able to save yourself time and money by using a SQL database. This is also true if you only deal with structured data and if your operations only involve minimal online interactions.

If your business is on a different trajectory – say you are on an exponential growth path, whether that is inventory, user management, or some other aspect, and if you will constantly be engaging with users and need the power of real-time analytics constantly, then a NoSQL-only database could be the right solution for you. 

A hybrid NoSQL architecture comes in when you need the best of BOTH worlds. When you have offline operations or you have structured data or when you’ve built your entire business around a SQL database, but now you are ready to expand into the IoT to offer newer, faster apps, advertisements, inventory management, and personalization without losing everything you’ve built, then a hybrid database makes the most sense.

IoT

Making the Best Choice for Your Business

The best option always depends on your current and future business goals. How have you built yourself up so far? Where are you going in three years? Five years? Ten years?

If you think you will need a combination of SQL and NoSQL solutions, then a hybrid architecture will be the right choice for you. This is especially true if you’ve already been using SQL and you want to give yourself a solid foundation as the world of IoT continues to evolve.

However, if you only deal in structured, low-volume data, then you will save time and money by sticking with your trusty old relational system.

Finally, if you’re all-in on technology, real-time data, scalability, flexibility, and your plan is for exponential growth, then a cloud-based NoSQL database is the absolute best choice for you. 

To get the most comprehensive NoSQL solution in existence, start here with BangDB completely free, and give yourself the biggest IoT advantage right now.

Further Reading:

Use Cases and Scenarios Suitable for a NoSQL Database Technology

Wherever you are starting from, you have to consider which type of database technology makes the most sense for the needs of your business. 

The NoSQL database is a powerhouse of a management solution best suited for businesses working with a lot of unstructured data in huge volume and in real-time.

If you’re a small business with a low volume of structured data, and if you don’t need the ability to manipulate data in real-time, then you’ll find relational databases (or even just an excel spreadsheet) can be a better option, although not always.

To explore other types of databases, check out this article where we cover both spreadsheets and relational database solutions. Now, let’s dive into some of the different scenarios that might call for the nearly limitless power of a NoSQL database.

NoSQL Database Use Case Number One: Scaling Up

Some businesses always remain small. Others grow to the moon and beyond. Imagine what Amazon was like when it started. They had a limited inventory because their operation was tiny. Over time, Amazon grew much larger.

Today, Amazon has warehouses all around the world. Their inventory is massive, and they operate with so many moving pieces and at such a scale, that trying to accommodate wide-scale inventory adjustments using a relational system would be virtually impossible. Thankfully, Amazon is a smart business. The company is built on a cloud computing solution that scales with them as they grow.  

With NoSQL, businesses can easily manage inventory systems at scale regardless of how large and complex they become. 

But what if you don’t have inventory? Even if you only have a large user base, a challenge could arise as your business grows. If you operate a platform that manages many users daily, then over time, you will find you need a solution to easily call up specific user data quickly.

Managing hundreds of thousands or even millions of users with a relational database would take light years. Relational systems have to read every entry line by line to find and return the requested data, so by the time the system finds the targeted dataset, users are usually long gone.

NoSQL on the other hand takes an entirely different approach by working with unstructured data so that queries run fast, and data can be retrieved quickly no matter how much data is contained within the system.

NOSQL Database

NoSQL Database Use Case Number Two: Real-Time Data

Building from the scenario above, imagine if Amazon couldn’t provide order data to customers instantaneously. In a world where people demand instant results, using a relational database leads to an absolute catastrophe.

The good news is, that non-relational systems operate with such speed and efficiency, that user data can be searched, retrieved, and returned to the end-user almost as fast as they can request it.

If you expect to provide information to consumers regularly, and if you plan to have a lot of people using your platform or service, then you absolutely must use some form of NoSQL to manage your data because the alternative leads to the downfall of your business.

Since there are different types of NoSQL databases, we’ve broken down the storage options to give you an idea of how each type works and when you might want to use one over another. Some are faster than others, so you’ll want to explore your options before choosing a service provider.

Returning user data instantly is one use case. Another scenario occurs when you think about the overarching consumer experience.

Relational databases aren’t particularly useful for creating personalized experiences because they are too slow. When you want to offer personalized advertisements or other engaging and interactive platform elements, then you’re looking for a power found only within NoSQL.

NoSQL Database Use Case Number Three: Affordability

Probably the most sensible reason why businesses turn to NoSQL is affordability. Just because you’re growing an enormous company doesn’t mean you have to settle for enormous costs. Unfortunately, that’s exactly what happens when you work with relational databases. 

Relational Databases Are Expensive

Some companies worry that upgrading their database will come with endless expenses. The truth is, outdated relational systems cost far more to manage than what you can save by migrating to NoSQL. This happens because relational databases weren’t designed for the cloud. They were built for a different times and to handle different needs.

It’s kind of like how you wouldn’t expect a computer from the early 90s to play a modern PC game. Not only would the older computer not handle the game out of the box, but you would have to rebuild the old computer from the ground up to make it possible.

NoSQL Was Built for the Cloud

Non-Relational databases were created for the internet of things (IoT). Their design works within cloud computing systems which makes them extremely flexible, scalable, dependable, and therefore, affordable. 

When you upgrade to NoSQL, you are making the move to a modern solution that isn’t just designed to handle the challenges of today but also is adaptable for the needs of tomorrow. 

Instead of rebuilding from the ground up, your team can quickly institute solutions while minimizing costs, so even if you invest cash during the migration phase, you end up saving a lot more over time.

That said, if you were to use BangDB’s open-source NoSQL technology, then you could start completely free, and you would save a lot more.

NoSQL Database

Is NoSQL Right for You?

What do you value in business and within your operations? If you need to move fast, provide users with an extremely reliable, engaging, or personalized experience, or if your business has large-scale operations or is likely to grow quickly, then NoSQL is probably the right choice for you. 

If you are a small business working with a limited amount of data that is mostly structured, and if you don’t need 24/7 availability or quick recall of information and datasets, then relational technology is still a viable option. 

However, even small businesses can benefit from NoSQL solutions when they want to bring the power of cloud computing into their services. For instance, if you wanted to offer an app for your customers, part of your business could remain on a relational system while your app is developed using a non-relational solution to maximize speed, minimize costs, and deliver the best possible user experience.

After considering your options, if you find that NoSQL is the right choice for you, then BangDB has a powerful, affordable solution with a range of storage options that extend beyond other providers. Click here to explore our NoSQL technology for your business completely free.

Further Reading:

The Evolution of Databases from SQL to NoSQL

The amount of data the world is producing grows day after day. And as it grows, technology experts are looking for ways to store and access this data to improve the customer experience and analyze activity. 

This growing data volume required changes in database options. Relational databases are expensive and challenging to scale since developers can only scale them horizontally. Yet, SQL has always been the go-to data structure up until about a decade ago.

Relational databases date back to 1970 when Edgar F. Codd introduced the concept of storing data in rows and columns with a specific key that showed the relationship between the data. The majority of relational databases used structured query language (SQL) but with time, they have become too rigid and restrictive to handle complex and unstructured data.

We’ll take a look at the evolution of databases and what’s fueling the need to move from SQL to NoSQL in the big data era.

Databases from SQL to NOSQL

Unstructured Data Boom

Until recently, all data fit perfectly into a relational SQL database because the data was all structured. But then came the unstructured data boom, which led to SQL databases being insufficient to meet the needs of many companies. 

The unstructured data boom began when access to the internet became commonplace. And with greater access to the internet, social media platforms began to spring up as users shared simple updates to keep their friends and network informed about their daily activities. 

According to research from 2021, 7 in every 10 Americans use social media. That’s up from just 5 percent in 2005 when Pew Research began tracking social media usage. With that ever-increasing demand for social media, updates have come the need for storing and delivering unstructured data at incredibly rapid rates.

Not only is the need for storing unstructured data rising, but the data also includes various types, such as images, video, audio, and text. These large files put enormous strains on limited storage capacities within SQL databases.

Before NoSQL joined the marketplace, IT professionals relied solely on relational database management systems (RDBMS) to handle storing all data. From website data to business application data, SQL databases were able to handle storage needs.

Why SQL Was Suited for Storing Structured Data

Relational databases were well-suited for storing all structured data because they are ACID compliant. ACID stands for:

  • Atomic: transactions are “all or nothing,” which means that if one part of the transaction fails, the entire transaction will fail. If the transaction does fail, the database state is unchanged. Relational databases guaranteed atomicity in every situation, which allowed developers to use the database for crucial transactions, such as banking.
  • Consistency: whether a transaction is successful or not, the database remains consistent. Before, during, and after a transaction, developers could rely on the database to be consistent.
  • Isolation: data modification transactions are independent of one another.
  • Durability: once the system notifies a user that their transaction was successful, the action cannot and will not be undone. That transaction will persist within the database.

The consistent experience of working with SQL was one reason why developers enjoyed it. You could count on the following characteristics being present with any relational database.

  • Table format data storage
  • Data represent relationships
  • You can join tables using relational links
  • Normalization reduces duplicate data in the database
  • The databases are flexible and efficient

Discovering SQL Shortcomings

Although developers relied on SQL databases to store their data and power applications, they also recognized their shortcomings, which grew as the need for big data and unstructured data grew.

While SQL is great for storing structured data, it cannot store unstructured data. And because data needs are constantly changing, SQL databases are also challenging because developers must know and understand their data and create its structure before beginning a project. And if that data changes, it can require application downtime or large expenses to adapt the SQL database accordingly. 

Social media data has no structural boundary. But that’s just one example of schema-less data. With rising needs for creating, reading, updating, and deleting (CRUD) all types of data, relational databases are becoming more challenging to use and more expensive to operate. Maintaining relationships between data has become a big job and in some cases, impossible. 

That’s what led technology specialists to look for a new solution. Great minds in technology, such as Google and Facebook, have worked to develop new databases that don’t require schema and data relationships to store and retrieve unstructured data.

Looking for an innovative NoSQL solution?

The Dawn of NoSQL

For nearly 30 years, SQL databases and other types of relational databases were the only option and met the needs of most developers. In 1998 Carlo Strozzi introduced the concept of NoSQL. But it took more than a decade for the concept to catch on.

We didn’t see much about NoSQL until 2009 when Eric Evans and Johan Oskarsson described non-relational databases using the term NoSQL. While NoSQL is often thought to mean it does not use SQL at all, it actually means not only SQL because these systems can engage in SQL-like queries.

Developers created NoSQL options to respond to the growing need to store and process web data, which is generally unstructured. The system allows for a distributed database system allowing developers to rely on multiple computers and servers.

The ad-hoc approach to data storage is incredibly fast and appropriate for storing various kinds of data and in large volumes. Slowly, these databases are becoming the database of choice for large, unstructured data sets because they are far more flexible, fast, and economical.

Enormous companies like Twitter, Facebook, and Google that process incredible volumes of data have turned to NoSQL to power their experiences. 

Big data is not a new term. It became official in 2005 but was something that many companies were grappling with before this. NoSQL has been the answer to dealing with big data and helping applications with CRUD operations.

NoSQL

NoSQL Database Flexibility

One reason why developers appreciate NoSQL databases so much is because they allow for various types of data storage. There are four formats NoSQL can store data in.

  1. Key-value
  2. Document
  3. Column
  4. Graph

Over the last decade, some databases are now multi-model, which means they can store data in more than one of these formats or even all four. And some databases merge SQL and NoSQL to provide the benefits of each type of database by using SQL-like language with a NoSQL database.

The future of NoSQL databases is strong. Given the ever-growing need for managing additional data, developers continue to rely more heavily on NoSQL and the industry doesn’t foresee that trend changing. 

For a multi-model NoSQL database that includes artificial intelligence and SQL-like queries through a command-line interface (CLI), download BangDB. The advanced NoSQL database is incredibly flexible and offers some of the most modern technology available for a database. 

Further Reading:

The 5 Fastest NoSQL Databases Every Data Science Professional Should Know About

Fast NoSQL Databases: Data is all around us and extremely plentiful. Just about everything that a consumer does generates data now. From posting to social media to purchasing groceries online for in-store pick up, modern-day databases need to be prepared to cope with these extreme data volumes.

The best way to cope with large data volumes is to use a distributed database capable of operating different nodes to partition data accordingly. That way, if one node goes down or is overloaded, the system can continue to run using other nodes with no problems. Fast NoSQL databases are also needed in order to keep up with the speed of the data streaming from multiple sources and also for real time processing of the data

Answering these challenges means using a fast NoSQL database that can handle partition tolerance seamlessly to create a great experience for customers while housing the information data scientists want.

What Is a NoSQL Database?

A NoSQL database does not mean that there is no relationship between data at all. NoSQL actually stands for “Not Only SQL.” This means that information stored in this type of database is not divided into various tables. That way, you can see all related data without specific restrictions.

NoSQL databases allow data scientists to view data in one structure. This enables the data processing to experience greater speeds and fewer performance lags. Completing multiple queries at once with these databases should be no problem and you won’t need to run joins.

Using a NoSQL database continues to grow in popularity because they scale extremely well and are ideal for distributed environments. Your team can rely on these databases to perform well under heavy workloads time and time again. 

Are NoSQL Databases Faster?

nosql databases are faster
NoSQL databases are faster and were designed for performance out of the box.

Yes, NoSQL databases are faster and designed for high-performance data processing. Developers created these non-relational databases out of a need for greater agility and performance while also scaling daily to meet the needs of ever-increasing data processing and storage.

NoSQL databases can help with real-time predictive analytics and meet the needs of billions of users.

As you go about your daily routines of surfing the internet and using mobile applications, you’re probably engaging with these lightning-fast databases. Some common uses for NoSQL databases include:

  • Social applications
  • Online ads
  • Data archiving

Why Are NoSQL Databases Faster?

The biggest reason that these databases are faster is that they “focus on using a very small set of database functionality,” according to Cameron Purdy, who used to work at Oracle.

Ultimately, the speed of your database will depend on how you’re using and querying that data. Some software engineers develop SQL applications that can act and function similarly to NoSQL, but it still leaves the question of the scalability of such ad hoc creations. And you’ll need to plan for longer development timelines to accommodate such engineering.

5 Fast NoSQL Databases

If you’re looking to increase the speed, reliability, and scalability of your database solutions, here’s a look at the nine fastest NoSQL databases available.

1. MongoDB

MongoDB is an excellent database for storing documents in JSON objects. Large companies like Uber and eBay use their services. Its ideal use cases include the following.

  1. Integration of hundreds of data sources with a unified data view
  2. The need for large read and write operations
  3. Storing clickstream data to analyze customer behavior

The company offers extensive online training and certification programs to help its users learn the database and use it to its fullest. 

2. Cassandra

Cassandra is an open-source NoSQL database. Facebook initially developed Cassandra but now it’s widely available and many companies use it because of its scalability. 

Cassandra is well-known for its ability to handle petabytes of information. It is also a great product for responding to thousands of requests at once.

Data scientists often choose Cassandra in the following scenarios.

  • Situations where they have more write operations than reading operations
  • Greater availability needs than consistency needs. Facebook built it to meet the needs of social networking, but the application would not do as well for banking
  • Fewer joins and aggregation database queries
  • Some examples of applications that lend themselves well to using Cassandra include weather data, order tracking, health tracking

3. Elasticsearch

Elasticsearch offers one of the best full-text search databases. It is open-source and highly scalable. You can use Elasticsearch when you need fuzzy matching.

Some major companies like Slack and Medium use Elasticsearch. The database is ideal if you’re looking to accomplish the following functionality.

  • Full-text search use cases
  • Chatbots to resolve queries, especially using fuzzy matching in the case of misspellings or poor syntax
  • Storing and analyzing log data
fast nosql databases
Choosing the right database will have an enormous impact on the speed of your project.

4. Amazon DynamoDB

Amazon’s product is not open source, but it is still highly scalable even with up to 10 trillion daily requests. It’s no surprise that many large companies like Snapchat and Samsung use Amazon DynamoDB.

This NoSQL database has two major use cases.

  1. Simple key-value queries with high volumes
  2. OLTP workloads that require highly consistent data, such as online banking or ticket booking

5. BangDB

BangDB is a very high-performance database in the world. It has been designed and developed from the ground up to deal with modern and emerging fast-moving data in real-time. It implements core features of a database like transaction, concurrency, WAL, indexing, etc. and at the same time implements AI, Graph processing, and Stream processing natively within the database for modern user cases

BangDB is ideal for the following use cases.

  1. Real-time data processing and analysis
  2. Random and real-time data access
  3. Predictive and enhanced data science with Graph

Scalable, Reliable Database Solutions

NoSQL databases offer some of the fastest, most reliable, and scalable solutions. Start building your modern data app today.