- Implementation of Agile
- Creating better scene with agile & data science
- Improving Prediction Performance
- Fixing Prediction Problem
- Agile Data Science - SparkML
- Deploying a predictive system
- Building a Regression Model
- Extracting features with PySpark
- Role of Predictions
- Working with Reports
- Data Enrichment
- Data Visualization
- Collecting & Displaying Records
- NoSQL & Dataflow programming
- SQL versus NoSQL
- Data Processing in Agile
- Agile Tools & Installation
- Agile Data Science - Process
- Methodology Concepts
- Agile Data Science - Introduction
- Agile Data Science - Home
Agile Data Science Useful Resources
Selected Reading
- Who is Who
- Computer Glossary
- HR Interview Questions
- Effective Resume Writing
- Questions and Answers
- UPSC IAS Exams Notes
NoSQL and Dataflow programming
There are times when the data is unavailable in relational format and we need to keep it transactional with the help of NoSQL databases.
In this chapter, we will focus on the dataflow of NoSQL. We will also learn how it is operational with a combination of agile and data science.
One of the major reasons to use NoSQL with agile is to increase the speed with market competition. The following reasons show how NoSQL is a best fit to agile software methodology −
Fewer Barriers
Changing the model, which at present is going through mid-stream has some real costs even in case of agile development. With NoSQL, the users work with aggregate data instead of wasting time in normapzing data. The main point is to get something done and working with the goal of making model perfect data.
Increased Scalabipty
Whenever an organization is creating product, it lays more focus on its scalabipty. NoSQL is always known for its scalabipty but it works better when it is designed with horizontal scalabipty.
Abipty to leverage data
NoSQL is a schema-less data model that allows the user to readily use volumes of data, which includes several parameters of variabipty and velocity. When considering a choice of technology, you should always consider the one, which leverages the data to a greater scale.
Dataflow of NoSQL
Let us consider the following example wherein, we have shown how a data model is focused on creating the RDBMS schema.
Following are the different requirements of schema −
User Identification should be psted.
Every user should have mandatory at least one skill.
The details of every user’s experience should be maintained properly.
The user table is normapzed with 3 separate tables −
Users
User skills
User experience
The complexity increases while querying the database and time consumption is noted with increased normapzation which is not good for Agile methodology. The same schema can be designed with the NoSQL database as mentioned below −
NoSQL maintains the structure in JSON format, which is pght- weight in structure. With JSON, apppcations can store objects with nested data as single documents.
Advertisements