Customer message analysis – predictive & streaming


To implement the use case “customer message analysis in a predictive and streaming manner”, you can use the following resources

  • code, files, data
  • Video of the demo/use case
  • BangDB binaries


Users and customers are sending their messages or reviews from their devices. Several such messages are streaming from different users into the system. We must first be able to ingest these messages in a real-time manner. Further, we should be able to process every single message and take corrective action as needed in order to perform customer message analysis.

The processes of Customer Message Analysis would include the following:

  1. set the streams and sliding window and ingest the data in these streams in a continuous manner
  2. find out the sentiment of the message [ positive, negative ] using IE (information extraction) (NOTE: we can find out many different sentiments/ emotions as we want, the demo deals with only two). We need to train a model here for this
  3. filter messages with negative sentiment and put them in a separate stream for further action/processing
  4. find out a definitive pattern and send such events matching the pattern to another stream for further review/action. The pattern is as follows;
    1. Any particular product that gets minimum 3 consecutive negative sentiment messages
    2. from different users in 1000 sec, find this pattern in a continuous sliding manner
  5. store few triples in graph store like (user, POSTS_REVIEW, prod) and (prod, HAS_REVIEWS, revid), revised id review id and prod is product
  6. set running stats for different attributes in the event such as unique count for users or min/max/avg/SDTV/sum/Kurt for the amount spent etc.
  7. set up the reverse index for messages such that it can be used for text search by the user
  8. set up secondary indexes for several attributes that could be helpful in query and also internal stream joins/ filter etc.

Relevant application areas

  • E-Commerce
  • Payment and banking
  • Ridesharing and cabs on hire, ex-Uber, Ola
  • Home delivery entities (food, etc)

Complexities in Customer Message Analysis

There are several challenges here, some of them could be

  1. Volume and Velocity. The number of messages could be very high, as these could be several users sending messages per second across the geographical areas. Hence data ingestion in real-time is critical
  2. The messages could be in English or in other vernacular languages, hence we need to extract sentiment from unstructured data, and keep improving or updating the models in real-time
  3. Extracting patterns from the streaming set of events in a continuous manner, this requires CEP on the streaming data which is very hard to implement on SQL or regular NoSQL databases
  4. Storing certain triples (sub, obj, predicate) in a graph that is continuously updated as events arrive, helps link data and/or events
  5. Different database queries along with text search which requires many secondary and reverses indexes
  6. Infrastructure deployment and maintenance if too many silos are used. Further automation is difficult to achieve in typical deployment models

Benefits of BangDB in Customer Message Analysis

  1. Use lightweight high performance BangDB agents or another messaging framework to stream data into the BangDB. BangDB is a high-performance database with an ingestion speed of over 5K+ events per second per server leading to half a billion events processing per commodity server in a day
  2. Integrated stream processing within BangDB allows users to simply start the process with a simple JSON schema definition. There are no extra silos set up for streaming infrastructure
  3. Integrated AI within BangDB allows users to simply train, deploy and predict incoming data without having to set up separate infra and then exporting data/ importing model, etc. The entire process can be automated within BangDB
  4. BangDB is a multi-model database and it also allows Graph to be integrated with streams such that the graph is updated on streaming data with triples
  5. BangDB supports many kinds of indexes including reverse indexes, hence running rich queries along with searches on BangDB is quite simple
  6. Integrated with Grafana for visualization of time-series data

Overview of the solution

  1. We have a stream schema ecomm_schema. Here in these streams, we will be ingesting data from various sources
  2. Ingestion of data happens as and when data is created. Therefore agent monitors a set of files here and as we write data into these files agent will parse the data and send it to banged server. We could directly write data using CLI or using a program that uses the bangda client etc…
  3. We have 3 different data sources here;
    • product data – this is rathe non-streaming data, but still, we could ingest these using agent
    • order data – as and when order is placed
    • customer or user reviews/ messages. This should be high volume streaming data
  4. Sample data is provided here, however, you may add more data to run it at a larger scale, etc.

Steps to run the demo on your own

  • Set up BangDB on your machine

                       Note: If you have got the BangDB already, you may skip the step

                       a. Get the BangDB, please check out or

                       b. Follow the read me file available in the folder and install the DB

Check out or clone this repo to get the files for the use case, and go to customer_reviews dir

Note: It will be good to have several shell terminals for the server, agent, client, and mon folder

> cd customer_reviews

copy the binaries (server, agent, cli) to the folders here in this folder (note: it’s not required but simple to run the demo)

  • copy bangda-server-2.0 binary to the server/ folder
  • copy bangda-agent-2.0 binary to the agent/ folder
  • copy bangda-CLI-2.0 binary to the client/ folder
  • Before running the demo from scratch, you should simply clean the database, and ensure the agent. the conf file is reset, and the files which are being monitored by the agent are also reset. To do this run the file in the base folder Also, please ensure the file and dir attributes are pointing to the right file and folder respectively in the agent/agent.conf file


  • Run the server, agent, and client

Run the server

> cd server

> ./bangdb-server-2.0 -c hybrid -w 18080

> cd ..

Note: we are running banged in hybrid listening mode (TCP and HTTP) both, HTTP port is 18080. This will come in handy in ingesting data using agents, interactions using CLI, etc. and at the same time visualization using Grafana

Run the agent

> cd  agent

> ./bangdb-agent-2.0

> cd ..

Run the client

> cd cli

> ./bangdb-CLI-2.0

you will see something like this on the prompt

server [ : 10101 ] is master with repl = OFF

 __     _    _   _   ____   ___    ___
|   \  / \  | \ | | | ___\ |   \  |   \
|   / /   \ |  \| | | | __ |    | |   /
|   \/ ___ \| | \ | | |__|||    | |   \
|___/_/   \_|_| |_| |_____||___/  |___/

command line tool for db+stream+ai+graph

please type 'help' to get more info, 'quit' or 'exit' to return


  • Register the schema (set of streams)

Let’s first register the stream schema into which we will be receiving the data

bangdb> register schema ecomm_schema.txt

now let’s ingest a few data into the streaming product.

NOTE: to help users ingest some events, there is a simple script “” which takes the following arguments;

bash sendline <fromfile> <tofile> <numrec> <stopsec>

send <fromfile> to <tofile> with <numrec> num of events <stopsec> per sec

In a real scenario, the application or program will write these events into some log file and the agent will keep sending data to the server. For demo purposes, we will simulate the application/program by using sending. sh

  • Send product data to the server

> cd mon/

> bash ../data/ecomm_product.txt prod.txt 1 1

Note: you can send 1000’s of events per second by using

> bash ../data/ecomm_product.txt prod.txt 1000 1

  • Send order data to the server

bash ../data/ecomm_order.txt order.txt 1 1

Now come back to the CLI shell terminal and train a model for sentiment analysis.

BangDB will keep using this sentiment model for adding the “sentiment” attribute for every event as it arrives

  • Train sentiment model

When you train a model from CLI, here is what you will see, you may follow as it’s shown here or simply follow the workflow as cli will keep asking questions.

NOTE: The sentiment model requires a knowledge base for the context. It’s always a good idea to train a KB for the context/ area in which we work in order to perform customer message analysis. Therefore for better accuracy and performance, we should ideally train a model. However, for the demo purpose, we have a sample kb (which is trained on minimal data), which can be used but is not sufficient. If you want proper KB for sentiment analysis for customer reviews/comments/messages then please send me a mail (, and I will forward the link to you. For production, we must use properly trained KB file

bangdb> train model user_sentiment
what's the name of the schema for which you wish to train the model?: ecomm
do you wish to read earlier saved ml schema for editing/adding? [ yes |  no ]: 

	BangDB supports following algorithm, pls select from these
	Classification (1) | Regression (2) | Lin-regression/Classification (3) | Kmeans (4) | Custom (5)
	| IE - ontology (6) | IE - NER (7) | IE - Sentiment (8) | IE - KB (9) | TS - Forecast (10) 
	| DL - resnet (11) | DL - lenet (12) | DL - face detection (13) | DL - shape detection (14) | SL - object detection (15)

what's the algo would you like to use (or Enter for default (1)): 8
what's the input (training data) source? [ local file (1) | file on BRS (2) | stream (3) ] (press enter for default (1)): 1
enter the training file name for upload (along with full path): ../data/review_train.txt

	we need to do the mapping so it can be used on streams later
	This means we need to provide attr name and its position in the training file

need to add mapping for [ 2 ] attributes as we have so many dimensions
enable attr name: sentiment
enable attr position: 0
enable attr name: msg
enable attr position: 1

we also need to provide the labels for which the model will be trained
    enter the label name: positive
do you wish to add more labels? [ yes |  no ]: yes
    enter the label name: negative
do you wish to add more labels? [ yes |  no ]: 
    enter the name of the KB model file (full path)(for ex; /mydir/total_word_feature_extractor.dat): total_word_feature_extractor.dat
    Do you wish to upload the file? [ yes |  no ]: yes
training request : 
   "training_details" : {
      "train_action" : 0,
      "training_source_type" : 1,
      "training_source" : "review_train.txt",
      "file_size_mb" : 1
   "model_name" : "user_sentiment",
   "algo_type" : "IE_SENT",
   "labels" : [
   "schema-name" : "ecomm",
   "total_feature_ex" : "total_word_feature_extractor.dat",
   "attr_list" : [
	 "position" : 0,
	 "name" : "sentiment"
	 "name" : "msg",
	 "position" : 1
do you wish to start training now? [ yes |  no ]: yes
model [ user_sentiment ] scheduled successfully for training
you may check the train status by using 'show train status' command
do you wish to save the schema (locally) for later reference? [ yes |  no ]: 

Now you can see the status of the model training

bangdb> show models
|key                 |model name    |   algo|train status|schema name|train start time        |train end time          |
|ecomm:user_sentiment|user_sentiment|IE_SENT|passed      |ecomm      |Sat Oct 16 00:07:11 2021|Sat Oct 16 00:07:12 2021|

Now let’s ingest the customer reviews and see the output

> cd mon

> bash ../data/user_msg.txt reviews.txt 1 1

come back to cli terminal and select a few events from the stream “reviews” in the “comm”schema

bangdb> select * from
|key             |val                                                                                                                             |
|1634329532924119|{"uid":"sal","prod":"ipad","msg":"finally the order arrived but i am returning it due to delay","tag":"return","revid":"rev13","|
|                |_pk":1634329532924119,"sentiment":"negative","_v":1}                                                                            |
|1634329531921928|{"uid":"raman","prod":"guitar","msg":"finally order is placed, delivery date is still ok do it's fine","tag":"order","revid":"re|
|                |v12","_pk":1634329531921928,"sentiment":"positive","_v":1}                                                                      |
|1634329530919064|{"uid":"sal","prod":"iphone","msg":"just ordered for p3 and i got a call that the delivery is delayed","tag":"order","revid":"re|
|                |v11","_pk":1634329530919064,"sentiment":"positive","_v":1}                                                                      |
|1634329529916681|{"uid":"raman","prod":"guitar","msg":"the product is in cart, i want to order but it's not going","tag":"cart","revid":"rev10","|
|                |_pk":1634329529916681,"sentiment":"negative","_v":1}                                                                            |
|1634329528914003|{"uid":"mike","prod":"football","msg":"how amazing to get the packet before time, great work xyz","tag":"order","revid":"rev9","|
|                |_pk":1634329528914003,"sentiment":"positive","_v":1}                                                                            |
|1634329527911595|{"uid":"sal","prod":"ipad","msg":"not sure why the product is not yet delivered, it said it will be done 3 days ago","tag":"orde|
|                |r","revid":"rev8","_pk":1634329527911595,"sentiment":"negative","_v":1}                                                         |
|1634329526909432|{"uid":"rose","prod":"guitar","msg":"not sure if this site works or not, frustating","tag":"order","revid":"rev7","_pk":16343295|
|                |26909432,"sentiment":"negative","_v":1}                                                                                         |
|1634329525906102|{"uid":"hema","prod":"p3","msg":"the tabla got set very smoothly, thanks for the quality service","tag":"order","revid":"rev6","|
|                |_pk":1634329525906102,"sentiment":"positive","_v":1}                                                                            |
|1634329524902468|{"uid":"hema","prod":"tabla","msg":"i received the product, it looks awesome","tag":"order","revid":"rev5","_pk":163432952490246|
|                |8,"sentiment":"positive","_v":1}                                                                                                |
|1634329523899985|{"uid":"rose","prod":"guitar","msg":"order placed, money debited but status is still pending","tag":"order","revid":"rev4","_pk"|
|                |:1634329523899985,"sentiment":"negative","_v":1}                                                                                |
total rows retrieved = 10 (10)
more data to come, continue .... [y/n]: 

As you see, the attribute “sentiment” is added with the value predicted by the model user_sentiment

Now let’s check out the events in the filter stream. We see that all negative events are also available in the stream negtative_reviews

bangdb> select * from ecomm.negative_reviews
|key             |val                                                                                                                             |
|1634329532924119|{"uid":"sal","prod":"ipad","msg":"finally the order arrived but i am returning it due to delay","tag":"return","revid":"rev13","|
|                |_pk":1634329532924119,"_v":1}                                                                                                   |
|1634329529916681|{"uid":"raman","prod":"guitar","msg":"the product is in cart, i want to order but it's not going","tag":"cart","revid":"rev10","|
|                |_pk":1634329529916681,"_v":1}                                                                                                   |
|1634329527911595|{"uid":"sal","prod":"ipad","msg":"not sure why the product is not yet delivered, it said it will be done 3 days ago","tag":"orde|
|                |r","revid":"rev8","_pk":1634329527911595,"_v":1}                                                                                |
|1634329526909432|{"uid":"rose","prod":"guitar","msg":"not sure if this site works or not, frustating","tag":"order","revid":"rev7","_pk":16343295|
|                |26909432,"_v":1}                                                                                                                |
|1634329523899985|{"uid":"rose","prod":"guitar","msg":"order placed, money debited but status is still pending","tag":"order","revid":"rev4","_pk"|
|                |:1634329523899985,"_v":1}                                                                                                       |
|1634329522897451|{"uid":"sal","prod":"ipad","msg":"even after contacting customer care, we have no update yet","tag":"order","revid":"rev3","_pk"|
|                |:1634329522897451,"_v":1}                                                                                                       |
|1634329521895545|{"uid":"sal","prod":"ipad","msg":"the order 2 was placed 4 days ago, still there is no response, i am still waiting for any conf|
|                |irmation","tag":"order","revid":"rev2","_pk":1634329521895545,"_v":1}                                                           |
|1634329520891590|{"uid":"sachin","prod":"cello","msg":"even after calling 20 times, the customer care is not responding at all","tag":"order","re|
|                |vid":"rev1","_pk":1634329520891590,"_v":1}                                                                                      |
total rows retrieved = 8 (8)

As you see, the events got automatically collected in this stream, we can further set the notification as well which will allow the server to take actions / sends notifications in an automated manner

But if you see, we don’t have any event in the negative_reviews_pattern stream. This is because we haven’t sent the events which could have formed the pattern. To remind, the pattern is defined as “at least 3 consecutive negative events for the same product but from different users within 1000 sec”. We would like to extract these patterns in a continuous manner and store these events in the negative_reviews_pattern stream

Let’s now add two events that are negative (as you note, the last event is predicted as negative so another three more negative events should trigger a pattern)

bangdb> insert into values null {"uid":"alan","prod":"ipad","msg":"finally the order arrived but i am returning it due to delay","tag":"return","revid":"rev14"}

bangdb> insert into values null {"uid":"john","prod":"ipad","msg":"frustating that product is not delievered yet","tag":"return","revid":"rev15"}

bangdb> insert into values null {"uid":"johny","prod":"ipad","msg":"frustating and disappointing that product is not delievered yet","tag":"return","revid":"rev16"}

Now select from the pattern stream

bangdb> select * from ecomm.negative_reviews_pattern
|key             |val                                                                                                                             |
|                |329669688454,"_v":1}                                                                                                            |
total rows retrieved = 1 (1)

As you see it has two kids (since we select both as per schema definition – see the ecomm_schema.txt). The first one was where the pattern started and the one where it got completed.

You can play with this and see how it works. If another negative event comes for the product which forms the pattern then it will get collected. else if broken then the next time when the pattern is seen, the server will send that event to the stream, etc.

Now, let’s see the triples as stored by the server in a graph structure, we will run Cypher queries

bangdb> USE GRAPH ecomm_graph
USE GRAPH ecomm_graph successful

bangdb> S=>(@u uid:*)-[POSTS_REVIEWS]->(@p prod:guitar)
|sub      |pred         |        obj|
|uid:rose |POSTS_REVIEWS|prod:guitar|
|uid:rose |POSTS_REVIEWS|prod:guitar|

bangdb>  S1=>(@u uid:hema)-[POSTS_REVIEWS]->(@p prod:*)-[HAS_REVIEWS]->(@r revid:*)
|sub       |pred       |       obj|
|prod:p3   |HAS_REVIEWS|revid:rev6|

And so on. Please see the documentation for more info on stream, graph, ml, etc… you may get help from CLI, forex; help on graph, type “help graph”, for ml type “help ml” etc…

Further, you can run for higher volume with high speed and high volume implementation of the use case. You can train more models, add more triples, etc. as required.

Get started with BangDB

Does NoSQL Mean Cloud? Best NoSQL Cloud Database Services in 2022

Just like traditional databases, not all NoSQL databases are cloud databases. A cloud database runs on a cloud virtual machine. This machine can be on a public, private or hybrid cloud. Or, it can be part of a database-as-a-service (DBaaS) offering.  

DBaaS is the equivalent of software-as-a-service (SaaS) where you subscribe and pay as you go. The platform manages the provisioning, maintenance, and performance. The service provider makes it simple to use the database. 

However, if you choose to host the database yourself on a private, public, or hybrid cloud, you’ll be responsible for handling the upkeep, security, and performance of the database. 

Want to learn more about NoSQL cloud options? This article will explain the difference between DBaaS, and self-hosting and provide an overview of the best NoSQL cloud database options.

Is NoSQL a Cloud Database?

No, not necessarily. Some NoSQL databases include all the structure and code that you need but they do not host it on the cloud for you. While you can then use host databases on public, private, or hybrid cloud servers, they are not necessarily natively cloud databases.

To be a cloud database, you’re looking for a database-as-a-service provider which means you don’t have to host the database on your own local or cloud servers.

Benefits of DBaaS

A NoSQL cloud database service has many great benefits. Here’s a look at a few of those top benefits.

  • Easy, yet controlled access from anywhere
  • Agility to manage the data and software development process
  • Scalability to meet your ongoing growth needs
  • Outstanding performance 
  • Reduction in manual labor for your team
  • Reliability thanks to regular backups
  • Disaster recovery
nosql cloud database

NoSQL Cloud Database Service Use Cases

There are several NoSQL cloud database use cases where you’ll appreciate having a database-as-a-service provider. Here are a few examples.

  • Projects that require large data volume
  • Cloud-native applications
  • You’re planning to handle high scale traffic
  • The traffic will be distributed geographically
  • It requires real-time transaction processing
  • You’re migrating from a legacy database
  • The project includes a mobile application
  • You’re using the database to power internet of things application
  • The application requires caching
  • You’ll be relying heavily on analytics

Key Considerations for Cloud Databases

As you prepare for finding the best NoSQL cloud database, review these considerations to make the best selection possible.

1. Provider Options

Some databases can only run on a specific cloud provider, such as Amazon Web Services (AWS) or Google. If you want to have cloud provider options, have that discussion with a representative from the database provider you’re considering. 

Some of the most influential aspects of deciding on a provider are based on your existing relationships, compatibility with other technology, etc.

2. Technology

Make sure that a NoSQL database will work for your application. Some databases are transactional while others are not. Some integrate AI, others use outside AI products and still, others have no AI capabilities. Learn the technology limitations and options for the database before subscribing.  

If your in-house resources are only comfortable working in SQL, also consider what training and preparation moving to NoSQL will require. Consider your team’s skills with different programming languages to choose a provider that best fits those skills.

nosql cloud databse

3. Database management

Before selecting a self-managed database, consider your in-house resources. If you don’t have the skills to oversee a database, you’ll need to ensure you select a fully managed database. Otherwise, you might find that you end up with more overhead to hire team members capable of self-management, which could cost more.

4. Pricing

Some database services base their pricing on usage. This makes new projects more affordable since you’ll only need to carve out a small amount of space on a server to house your application. Before signing up, be sure you understand the ins and outs of the pricing structure so you know if it is license-based or usage-based.

5. Security

While a fully managed database-as-a-service has many great benefits, you want to be sure it isn’t opening your organization up to security risks. DBaaS providers must meet stringent regulatory requirements to secure customer data. This can be one of the greatest benefits of DBaaS is that you don’t have to assume the risk of security concerns.

6. Added features

Many database-as-a-service providers add-in features you might not get if you hosted the database yourself. These can include additional reporting, automatic connections to other services, and more.

Best NoSQL Cloud Database

With a greater understanding of how a NoSQL database can be a cloud database, you’re ready to start reviewing the options available to you.

1. BangDB

BangDB is a multi-model NoSQL database that enables you to store all types of data. It offers stream processing for real-time continuous data and has native AI capabilities so you can train and predict within the database. 

  • Double the performance of other leading databases
  • Native AI offers faster machine learning
  • Complex event processing helps you find real-time data patterns
  • Incredible statistics for fast queries
  • ACID-compliant for transactional needs
  • Supports rich query

BangDB is not a fully managed database-as-a-service offering. Be sure to learn more about the licensing before selecting BangDB.

2. MongoDB

Is MongoDB a cloud? Yes, MongoDB Atlas is a fully managed cloud database designed for modern applications. It is a key-value NoSQL database that stores and retrieves data as JSON documents. Some of the benefits of MongoDB include:

  • Built-in intelligence
  • Strong querying capabilities
  • Good analytics
  • Flexible document schemas
  • Uptime is excellent

Despite being an excellent option for a cloud-based NoSQL database, MongoDB does have some limitations.

3. Azure Cosmos DB

This NoSQL database has open APIs so you can easily scale your applications. Azure Cosmos DB is one of the best NoSQL cloud database options The free version allows for up to 25BG of storage and 1,000 request units per second. The database’s benefits include:

  • Nearly perfect availability
  • Low latency
  • Globally distributed database
  • SSD backed storage
  • Reserved throughput model
  • Ideal for IoT, social applications, mobile apps, and gaming
  • Database-as-a-service from a well-known and respected name in the industry

4. Oracle NoSQL Cloud Database

Oracle’s NoSQL cloud database offers the ability to store data in columns or key-value format. The database has a free option to help you get started and test out whether the database is right for you. You’ll also experience these great benefits.

  • Single-digit millisecond response times
  • High availability
  • ACID-compliant
  • Pay-per-use pricing
  • Compatible with on-premises Oracle NoSQL database

5. Amazon DynamoDB

Amazon Dynamo DB is a key-value and document NoSQL database. It started as a solution to Amazon’s need to handle larger traffic volumes to its service during heavier shopping seasons, such as during the holidays. Then it went public for others to use starting in 2012. It is a popular option for public cloud databases. 

  • Integrates well with other Amazon technology
  • Fully managed cloud NoSQL database
  • Able to handle 10 trillion requests per day or even 20 million requests per second

Although DynamoDB fits well into the family of Amazon technology services, it isn’t as ideal if you use other services and technology. It also lacks ACID compliance, which means it is not a transactional database.

Further Reading:


Moving From SQL to NoSQL Database: An In-Depth Handbook

Relational databases were created back in the ’70s. Imagine trying to play a 4k movie on 70’s television, or checking your favorite news app on a phone from the 70s… 

Impossible, obviously.

For almost every area of modern life, you use modern technology because it just makes sense. The same is true for your business tech. 

So we thought it was time to show you what it takes to migrate from your outdated SQL system to something more scalable, flexible, and technologically sound.

Steps to Switch Database Systems:

  1. Choose a NoSQL database provider
  2. Familiarize with the new system
  3. Conceptualize how you will represent your data
  4. Make the leap from SQL to NoSQL
  5. Rewrite your application code for NoSQL

The process can seem daunting at first, but many major companies, such as Marriott, Ryanair, Gannett, Art. sy, Foursquare, and more started with relational systems and later upgraded to support their exponential growth. 

In the rest of this article, I’ll walk you step-by-step through the process so you know exactly how it works.

Step 1: Choose a NoSQL Database Provider

Before you can move anything, you’ll need a service provider. You can learn more about some of the fastest NoSQL providers here. If you just want to see an overview of some of our top choices, here are our favorites:

  • BangDB
  • MongoDB
  • Cassandra
  • ElasticSearch
  • Amazon DynamoDB

The database provider you choose should be based on what you need to accomplish with your application; however, BangDB is considered one of the fastest, easiest, and most reliable providers in existence, so it is always a good choice in our opinion.

Step 2: Familiarize with the New Database System

Once you’ve chosen a service provider, you need to understand a little about their solution. You don’t have to be an expert in NoSQL, but it helps to have an idea of what that service is capable of and how you can implement it for your application.

  • Download the database system if possible
  • Read some of the manuals and online tutorials
  • Use the system for one or more test projects

Start by downloading the system if it has a free version. BangDB (mentioned above) has a completely free, open-source option that can be upgraded for Enterprise use and additional functionality later on. That means you can download it, learn it, and test it before going all-in.

Most service providers offer online tutorials that are really insightful and can help you avoid common challenges as you make your move. You can read these yourself or have your developers review them to make sure they *get it* before diving into the deep end of the pool.

We also recommend putting your new database service to use on small test projects before migrating your entire application. Hands-on experience will help you and your developers get a feel for the power you’re about to have in the palm of your hands, and as you’ve probably heard… 

“With great power comes great responsibility.”


Even though migrating data from one system to another is actually pretty simple, it is important to get familiar with the new system before you make the move.

Looking for an innovative NoSQL solution?

Step 3: Conceptualize How You Will Represent Your Data

Another pre-move consideration is how you plan to represent your data in the new system. You have several options, and the right one for your business will depend on the capabilities you need. 

Common NoSQL Data Store Options:

  • Key-Value Pair
  • Document
  • Column
  • Graph

Key-Value Pair
In this database setup, you will store key-value pairs with a record. A key can be a numeric or string value and needs to be unique within its record.

A document store is used for keeping semi-structured data. Data in a document store is encoded in standard formats such as XML, JSON, YAML, and BSON. 

Instead of storing data in rows, this database type stores information in columns which simplifies the aggregation process to make it easier to analyze information quickly. Columns can be unlimited in number, and can also be grouped into logical “families” with reading and writing carried out in columns rather than rows.

The graph is useful for applications that represent data in graph format where information is interconnected. This type of database implements nodes, edges, and properties. 

Start to think about what you need your application to do because life is much easier when you migrate to a database solution that makes natural sense for the goals of your app.

Step 4: Make the Leap from SQL to NoSQL

You’re finally ready to migrate from your old relational system to your new, improved NoSQL. For many applications, this process is relatively easy.

For example, most migrations can make use of SELECT * FROM statements against the original database. After that, they can then be loaded into the NoSQL database using whatever language you choose.

That said, each type of NoSQL is somewhat different, so your migration process may vary. To understand the exact steps to load your data into your new system, you will need to consult the service company’s tutorials or documentation.

For information on BangDB’s migration process, see the Developer’s Manual page here.

Step 5: Rewrite Your Application Code for NoSQL

Once your data has been moved from SQL to NoSQL, then the final step is to rewrite your application code so that it can query the NoSQL database with statements like insert() or find().

You will want to test your application before launch to ensure everything functions as expected, and you will also want to stage your application launch following similar due diligence steps as you would normally take with your old relational system.

In addition, you will also want to spend time learning your new database administration tools so you have a strong understanding of the different options available to you, and how everything works.

Migration Challenges to Expect

Before you start your SQL to NoSQL data migration, it is helpful to have an idea of some of the common challenges others have faced.

Moving Large Volumes of Users

If you’ve been in business a while, you may already have a large number of users. Sometimes that can cause hesitation and reluctance when it comes to moving from one place to another. While the new system may work flawlessly, that doesn’t mean problems never occur. 

The Solution? Start with a phased migration where you move a small number of users such as 3-5% and then 10-15%, and then 30-40%. Once you’ve done this several times and you feel comfortable with the process, then you can remove all of the remaining users at once with confidence.

Data Optimization

Optimizing data is another potential challenge companies sometimes face. Migrating from a SQL to NoSQL database isn’t particularly difficult; however, the new system needs to be optimized for your application. To ensure your new system is optimized for your application, you will need to know which queries your app runs and which queries you want to optimize the data store for so you can avoid any speed problems.

Choosing the Right Database Design

If you’ve never worked with NoSQL before, then it can be challenging to conceptualize how your data might be represented in the new system. Solving this problem requires spending some time understanding the different data models by reading through tutorials or working with a developer.

Choosing the Best Service Provider

There are many options to choose from. We’ve discussed quite a few of them on our blog, and the right one for your business will depend on your needs. 

That said, BangDB is considered one of the most flexible, easiest to use, open-source database service providers. Not only that, but it comes with a completely free version that can be downloaded and tested before migrating any data at all.

For those reasons, we recommend having a look at BangDB to find out if it might be right for you. If it turns out to be the exact right solution, then you can download BangDB here for free.

Further Reading:

The Hidden Benefits of Real Time NoSQL Database Architecture for Applications

The NoSQL database architecture provides many benefits over other, more traditional options like relational SQL databases. Not only does NoSQL handle larger volumes of information, but it is scalable, easier to update, friendlier for developers, and can use cloud infrastructure for zero downtime.

The primary benefits of NoSQL are numerous, but what about the hidden benefits that nobody really talks about? That’s what we’ll cover in the rest of this article.

Real-Time NoSQL Database Hidden Benefits:

  • Support huge volumes of users (in the tens of thousands and beyond)
  • Improve user experience with extremely responsive data points
  • Always ready for rapid updates and fast feature additions
  • Synchronize data with cloud platforms for mobile support

When you work with real-time applications, each of these points becomes a huge advantage that improves the user experience as well as the speed and efficiency of backend support.

Even improving just one area can make NoSQL the better choice over other options depending on the size of your business and its goals. 

Now, let’s take a closer look at how each of these points equates to more productivity, increased platform engagement, lower costs, and higher profits.

Why Scalability Matters for Application Development

At the start of an application’s lifecycle, scalability may not seem that important. The aim of a minimum viable product (MVP) is to move fast, enter the market early, test your idea, and get valuable feedback from users. But what happens when that feedback is positive?

Suddenly, you’ve got proof of concept and room to grow. That means your application can be scaled up without reservation since you have confirmation that the people it was designed for like your offer and want more.

If you developed your app with NoSQL from the start, then you can probably dive in and scale up with ease. But if you chose a more constrained option like a relational database, then you may discover you run into limitations since these systems weren’t designed for the kind of exponential expansion that happens in modern software applications.

Relational Databases – It’s Like Chiseling Notes on an Ancient Stone Tablet

Don’t get me wrong, relational databases have their uses, but they were designed before the IoT existed, and they were never meant to scale up for hundreds of thousands of users who want access to real-time information. 

For that reason, trying to get a relational database to function how you want is like loading Fortnite on a 90’s Windows OS (it’s not happening).

Relational Databases Are Best For:

  • Simplicity
  • Data Accuracy
  • High Security
  • Standardization

Although relational options are considered the “standard” when it comes to managing data, they’re starting to show their age as faster, more scalable, and more reliable options such as NoSQL start to emerge.

Looking for an innovative NoSQL solution?

The NoSQL Database was Designed with Exponential Growth in Mind

NoSQL stands for “not only SQL,” meaning you can empower your app with the capabilities of an SQL database, and with functionality that extends beyond relational limitations.

Today, having the power to manipulate and retrieve data at almost instantaneous speeds is the expectation rather than the exception. 

Once an app goes live, it might be used by millions of people, all of which expect a flawless and smooth experience. To make that happen, it makes sense to start from a scalable infrastructure rather than trying to upgrade an outdated system later on.

How Exactly Does Cloud Infrastructure Improve User Experience

In addition to scalability, NoSQL solutions are empowered by cloud services that add a wealth of benefits to the current developmental environment. 

Cloud Infrastructure:

  • On-demand scaling to support higher user volume
  • Globally operational apps for a worldwide customer base
  • 24-7 availability and virtually zero downtime
  • Synchronize seamlessly to support mobile users
  • Minimizes the cost of infrastructure
  • Dramatically speeds up a time to market

When combined with the ability to rapidly return real-time data at scale, a cloud infrastructure ensures the smoothest user experience possible. 

Instead of frustrating customers due to downtime or disconnects, or limiting your app to specific locations, cloud capacity transforms you from the Flintstones into the Jetsons. Suddenly, you can engage users almost anywhere on the planet.

Additional Hidden Benefits of a Real-Time NoSQL Database

Beyond the obvious benefits of leveraging modern systems to operate at scale and serve more people faster, there are a few other hidden gems worth noting.

  • Eases the workload burden for developers
  • Improves the testing and iteration process
  • Results in greater overall profit potential

When developers have the tools they need to build effortless solutions and make quick changes, then productivity increases and their overall workload decreases.  This results in more satisfaction with their ability to manipulate data and work with real-time feedback. In return, developers work harder for your business because they can reap the rewards of an unobstructed workflow.

In addition, simplified feature implementation and fast turnaround on upgrades speed up the overall testing and iteration process for rolling out new app concepts. Faster iterations decrease time-to-market, which opens up pathways to higher profits going forward. 

Finally, as a result of faster time-to-market, and faster testing, the end-user experience improves substantially. This leads to a steady flow of new and returning customers who are happy to make purchases at higher prices, therefore, increasing total profit potential.

How to Get Started With NoSQL

real time nosql databases

When you’re ready to develop your real-time application and you want to start from a future-proof NoSQL foundation, then you have several options available to you. 

Here are four of the best NoSQL databases around:


Powered by native artificial intelligence (AI), BangDB’s real time NoSQL database solutions are exceedingly useful for a variety of modern apps. Not only is it among the fastest, simplest, and most scalable choices available, but it is free with up to three licenses, and can be upgraded as you become more profitable and as your business grows.


Another solid option, MongoDB is a good choice when you need to store documents in JSON. While not as flexible or capable as BangDB, MongoDB offers a variety of training and certification programs to help users get the most out of their services.


If you’re looking for an open-source solution, then RavenDB could be the right option for you. RavenDB offers a fast NoSQL database that can be set up for on-premise use or cloud-based applications.


Last but not least, HBase is another open-source option with a high potential for scalability. Because it is written in JAVA, and because of its capability to store data for billions of users with rapid access, HBase has been used by large social platforms such as Pinterest and HubSpot.

The Best Real-Time NoSQL Database for Modern Applications

Quite a few options exist, and there are benefits and drawbacks to each. BangDB is currently considered the fastest, easiest, and most powerful machine-learning, deep-insight NoSQL database around.

If you are looking for the best overall option, then you will want to tap into the power of the BangDB architecture; however, any of the solutions mentioned above can help your business grow depending on your needs. 

To start your app development with BangDB for free (with unlimited use), go here to Download BangDB now.

Further reading:

Best NoSQL Databases for IoT Applications: Commercial and Open Source

The Internet of Things (IoT) requires certain database characteristics. IoT encompasses a wide range of technology from smart objects to empowering RFID systems. Astoundingly, the IoT worldwide revenue is $34.8 billion and growing. The challenges of using a NoSQL database for IoT come more from the development process than from the database itself.

Some developers get lulled into thinking they’ll put all the necessary data into a NoSQL database and figure out the schema later. But if you don’t create some form of structure for your data even within NoSQL, you risk the following challenges.

  1. Data loss
  2. Poor data readability
  3. Pipeline inefficiencies

The extreme flexibility of NoSQL is a great advantage, but it can become a disadvantage if you don’t plan how to use the data. 

Key Considerations When Selecting an IoT Database

When building an IoT application, you should consider these important factors in the database you select.

  1. Size, scale, and indexing capabilities
  2. Stream processing
  3. Flexible schema
  4. Running querying support
  5. Sliding window
  6. Cost

But you’ll also want to think about the types of data you’ll be dealing with. Some examples of data types include.

  1. Log data
  2. RFID, geologic
  3. Identifiers or addresses
  4. Sensor data
  5. Much, much more

Seeking the Best NoSQL Database for IoT?


InfluxDB is another option that launched within the last decade. It was published in 2013 and is a key-value database. It uses the Go programming language and is optimized to handle time-series data. It has many IoT benefits, including:

  • Indexable series
  • Built-in linear interpolation for missing data
  • Calculates aggregates based on continuous queries
  • SQL-like query language to help automate data downsampling


MongoDB is another NoSQL database option. It is free and open-source and is a document-based database. You can store all types of data and analyze it in real-time. Additionally, developers enjoy how they can change the schema as needed.

Experience BangDB for IoT Applications

Thousands of users have downloaded BangDB and many reports it is excellent for IoT applications. Learn more about it by downloading the NoSQL database today.


Beyond NoSQL Database: Why AI Is Needed within NoSQL for Modern Use Cases

Those familiar with traditional NoSQL databases know that scalability, flexibility, and speed are primary concerns. More data retrieved faster leads to actionable insights for developers and a better end-user experience.

Despite their growing popularity, NoSQL databases are challenged in four core competencies that limit their performance and function. afgsdfgsdf sasasas vfvfv

NoSQL Database Core Competency Limitations:

  • Complexity Limitations
  • Scalability Limitations
  • Rigidity Limitations
  • Cost Limitations

As long as these limitations continue, NoSQL databases cannot achieve their full performance capacity. Unfortunately, as an emerging technology, few solutions exist to overcome these problems today.

It is for this reason that artificial intelligence within NoSQL databases is needed for modern use cases. More on that in a moment. 

First, let’s look at how NoSQL databases work together with artificial intelligence for modern use cases right now. This will give you a broader picture of how AI convergence changes everything.

What is an AI Database?

AI Database Solution

The elimination of silos and the convergence of AI within the database means there is no need to integrate heterogeneous items individually. Instead, an AI database uses a single distributed layer to free up resources and empower the database to rapidly scale where scaling was nearly impossible before.

AI Databases Are Self-Serving

One of the biggest limitations of traditional NoSQL databases is the necessity of developers and coding. With AI databases, data is streamed in real-time which removes the need for an added analytic layer. In addition, the AI’s machine learning capacities train and deploy fast, and leverage abstractions for reuse to decrease build times. This means that adding features no longer requires an extensive backlog of coding. Instead, regular people can operate the database quickly and easily.

AI Databases Are Affordable

Dealing with big iron appliances or consultants can add hundreds of thousands, if not millions to startup costs. AI databases, on the other hand, can be cloud-based and allow you to start small and pay as you grow. Since AI databases are based on commodity (off-the-shelf) hardware, and because they do not require expert

Today, BangDB offers one of the most reliable and scalable AI database solutions available anywhere. To get started for free with unlimited use, go here to Download BangDB now and build your AI-powered app today.