Basic concepts introduced here will be helpful when we go through some
examples later in this article for better understandings.

Database design has several goals:

To understand the basic concepts behind Data normalization, let’s take an example.

Example:

Suppose we are given a table in which information is being stored in two attributes viz. student name and roll numbers. Suppose it is required to create a table of Roll Numbers in which both the attributes i.e., student name and roll Number are present so that whenever required this data can be accessed with high efficiency by performing necessary operations on these tables. Without any doubt, it will be better if the above-mentioned tables are combined into one single table called StudentRollNumber with just two columns i.e., student Name and roll Number where all students’ records appear once for each entry in the roll number column. This way data redundancy has been eliminated and efficient querying can be performed on these tables as and when required.

Data Normalization is the process of converting relations into relations that follow certain basic rules called normal forms, viz., 1st Normal Form (1NF), 2nd Normal Form (2NF), and 3rd Normal Form (3NF). Also, we cannot directly use relations that violate any one of the normal forms and must first convert them to a form that does not violate the particular normal form we are working with. To understand this better let’s look at what exactly we mean by data redundancy?

Let us take an example:

Suppose there is a relation R (A1, A2, An) where n > 1 such that for each possible value of Ai some value(s) appear for Aj. This is called a tuple, and it is said to be in 1NF if and only if the whole tuple represents some valid relation, i.e., R(A1, A2, An).

Example:

R (A1, A2) –> (a student with roll number 20205 has the name “John”)

Valid Relation

Invalid Relation or Data Redundancy

TL; DR – Any instance where there are two or more identical attributes that are related to each other violates the First Normal Form (1NF), which means you’re not deduping your data properly.

First Normal Form (1NF)

A relation schema R is said to be in 1NF if and only if the following conditions are satisfied:

All attributes are of atomic or group type. E.g.: College (Name, Address, Head of the Department Name, no. of faculty) instead of College (Name, Address, Head of the Department Name, Faculty’s name).

In this case no. of faculty is a Group attributes not an atomic one so it violates 1st Normal form.

Attributes are not repeating groups.

There are no multi-valued dependencies among the attributes that are not necessary to define the purpose of the relation.

TL; DR – Make sure every attribute in a relational database is uniquely identified by its column name and a primary key.

Second Normal Form (2NF)

A relation schema R is said to be in 2NF if and only if the following conditions are satisfied:

If attributes A1, A2, A have domains D1, D2, Dn respectively then Ri must be a subset of Dj for all 1 <= I <= n where Ri represents some valid subset of Dj. In other words, no partial dependencies exist between attributes within the same domain. Again there’s another term called transitive dependency which means transitivity of whole normal form not partial or otherwise 2nd normal form will not be satisfied.

TL; DR – All multi-valued dependencies must be removed, and the table should follow domain integrity.

What is domain integrity?

Domain Integrity requires that all tuples in a relation/table belong to the same domain (list of values). Tables that don’t exhibit this property contain what are called ‘domain anomalies.’ Example: City (StateCodeNo, Name)

This violates Domain Integrity because for example (‘CA’, ‘Los Angeles’) are not allowed in the City table. So this has to be normalized into 2NF by splitting into two tables i.e., one for states and another for Cities like below:

State (StateCodeNo, Name)

City (StateCodeNo, Name, Population)

Normal Form 2NF

This “refers to the application of general normalization procedures to a table so that each attribute corresponds directly to an identifiable attribute of the original relation” i.e., not repeating groups corresponding atomic columns.

Conclusion:

As you can see that by merely following these two rules your data is not necessarily normalized. There are various levels of normalization, which depend on how deep you want to go with respect to its normal form. It all depends upon what type of queries you run on the table, how it’s designed etc.

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How Data Is Disrupting Old Industries and Fueling New Business Models

I recently had the opportunity to talk with Matt Turck, partner at FirstMark Capital and the moderator of this week’s Structure Intelligence conference in San Francisco. Matt wanted to discuss how data is changing our lives and businesses in such a fundamental way.

He asked about my thoughts on big trends we’re seeing in artificial
intelligence (AI), visualization technology, and blockchain, all areas where
I’ve spent a significant amount of time.

AI: The Next Wave of Computing:

As 2017 gets underway, many investors believe AI has arrived as the next wave in computing that will change our lives, just as mobile computing changed our lives when smartphones proliferated over the past decade. To understand this latest wave of computing, I suggest watching Andrew Ng’s video which has been viewed almost 2 million times. MBA students in AI and machine learning. For that reason, we’re seeing a big increase in demand for scientists and engineers who specialize in machine learning and deep learning all areas where GPUs excel due to their throughput advantages. Two universities focusing on AI are Stanford University’s Artificial Intelligence Lab led by Fei-Fei Li, a brilliant scientist whose work encompasses both research and product development-and Carnegie Mellon University (CMU), where my friend Byron from NVIDIA sits on the faculty.

Visual: The Most Important Tool you’re not using today:

We have spent the past 20 years building tools for helping people communicate visually. It has been an incredible journey watching customers use Tableau to help them discover amazing stories in their data, oftentimes with big business impacts from helping doctors discover that a specific drug is generating unexpected side effects, to seeing how different products are selling across different regions, to understanding the market price sensitivity of different geographies. We’ve built a very happy and engaged community of more than 55,000 members on LinkedIn alone. I see this growing larger every year as more people look at their data visualizations day-in and day-out. The New York Times recently featured Tableau in its “Data Is Beautiful” section which shows off some amazing visualizations. Together with Tableau, we’re also seeing increasing demand for the tools that help people create their own stories from workbooks that show how to use different functions in Tableau, to books showing how to visualize data with specific tips and tricks on visualization best practices for Blockchain, all the way to self-service courses where students learn at their own pace by solving real problems in Tableau. Everyone is looking for new ways of getting better at telling stories visually.

Blockchain: Now Going Mainstream:

I started my career working with banks and financial institutions back when I was a doctoral student at CMU. In many ways, it’s been exciting watching blockchain grow from an open-source project into a powerful industry-transforming finance and business tool. Today, any industry in which a trusted intermediary is required for transactions to occur and where parties prefer to keep their transactions private from other participants in the ecosystem can benefit from blockchain technology. The bitcoin blockchain lets people send value to each other without having to trust that third party, while Ethereum lets people run applications without trusting an intermediary. In both cases, the parties participating in a transaction feel more confident due to the visibility and transparency of these blockchains, and because their records cannot be altered once written. I’m excited about how this will lead to new business models and transform nearly every industry over time from finance and healthcare to government services and insurance by removing middlemen from processes that have been built around them for hundreds of years.

The Age of AI is upon Us

AI, machine learning, deep learning, and neural networks-whatever you want to call it is the wave of the future. This technology isn’t science fiction anymore. We are seeing it make an increasing impact on our lives. Right now, this means that companies need people who understand how to build these systems and apply them in useful ways. I think that anyone interested in this field should start building things today if they’re not already doing so. There are countless sources of information available for free online about these topics. If you’re just starting out with programming or data visualization, there’s no better time than now to learn!

Conclusion:

In closing, I’d like to thank those who have been part of our journey so far. We’ve come a long way from the early days in San Francisco. To everyone reading this article-I look forward to continuing this journey with you as we explore new ways of bringing data and analytics into more people’s lives.

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