Enterprise Data Architecture Principles

Traditionally, enterprises have embraced data warehouses to store, process, and access large volumes of data. These warehouses are typically large RDBMS databases capable of storing a very-large-scale variety of datasets. As the data complexity, volume, and access patterns have increased, many enterprises have started adopting big data as a model to redesign their data organization and define the necessary policies around it.

This figure depicts how a typical data warehouse looks in an Enterprise:

As Enterprises have many different departments, organizations, and geographies, each one tends to own a warehouse of their own and presents a variety of challenges to the Enterprise as a whole. For example:

Multiple sources and destinations of data
Data duplication and redundancy
Data access regulatory issues
Non-standard data definitions across the Enterprise.
Software and hardware scalability and reliability issues
Data movement and auditing
Integration between various warehouses

It is becoming very easy to build very-large-scale systems at less costs compared to what it was a few decades ago due to several advancements in technology, such as:

Cost per terabyte
Computation power per nanometer
Gigabits of network bandwidth
Cloud

With globalization, markets have gone global and the consumers are also global. This has increased the reach manifold. These advancements also pose several challenges to the Enterprises in terms of:

Human capital management
Warehouse management
Logistics management
Data privacy and security
Sales and billing management
Understanding demand and supply

In order to stay on top of the demands of the market, Enterprises have started collecting more and more metrics about themselves; thereby, there is an increase in the dimensions data is playing with in the current situation.

In this chapter, we will learn:

Data architecture principles
The importance of metadata
Data governance
Data security
Data as a Service
Data architecture evolution with Hadoop

Data architecture principles

Data at the current state can be defined in the following four dimensions (four Vs).

Volume

The volume of data is an important measure needed to design a big data system. This is an important factor that decides the investment an Enterprise has to make to cater to the present and future storage requirements.

Different types of data in an enterprise need different capacities to store, archive, and process. Petabyte storage systems are a very common in the industry today, which was almost impossible to reach a few decades ago.

Velocity

This is another dimension of the data that decides the mobility of data. There exist varieties of data within organizations that fall under the following categories:

Streaming data:
- Real-time/near-real-time data
Data at rest:
- Immutable data
- Mutable data

This dimension has some impact on the network architecture that Enterprise uses to consume and process data.

Variety

This dimension talks about the form and shape of the data. We can further classify this into the following categories:

Streaming data:
- On-wire data format (for example, JSON, MPEG, and Avro)
Data At Rest:
- Immutable data (for example, media files and customer invoices)
- Mutable data (for example, customer details, product inventory, and employee data)
Application data:
- Configuration files, secrets, passwords, and so on

As an organization, it's very important to embrace very few technologies to reduce the variety of data. Having many different types of data poses a very big challenge to an Enterprise in terms of managing and consuming it all.

Veracity

This dimension talks about the accuracy of the data. Without having a solid understanding of the guarantee that each system within an Enterprise provides to keep the data safe, available, and reliable, it becomes very difficult to understand the Analytics generated out of this data and to further generate insights.

Necessary auditing should be in place to make sure that the data that flows through the system passes all the quality checks and finally goes through the big data system.

Let's see how a typical big data system looks:

As you can see, many different types of applications are interacting with the big data system to store, process, and generate analytics.

The importance of metadata

Before we try to understand the importance of Metadata, let's try to understand what metadata is. Metadata is simply data about data. This sounds confusing as we are defining the definition in a recursive way.

In a typical big data system, we have these three levels of verticals:

Applications writing data to a big data system
Organizing data within the big data system
Applications consuming data from the big data system

This brings up a few challenges as we are talking about millions (even billions) of data files/segments that are stored in the big data system. We should be able to correctly identify the ownership, usage of these data files across the Enterprise.

Let's take an example of a TV broadcasting company that owns a TV channel; it creates television shows and broadcasts it to all the target audience over wired cable networks, satellite networks, the internet, and so on. If we look carefully, the source of content is only one. But it's traveling through all possible mediums and finally reaching the user’s Location for viewing on TV, mobile phone, tablets, and so on.

Since the viewers are accessing this TV content on a variety of devices, the applications running on these devices can generate several messages to indicate various user actions and preferences, and send them back to the application server. This data is pretty huge and is stored in a big data system.

Depending on how the data is organized within the big data system, it's almost impossible for outside applications or peer applications to know about the different types of data being stored within the system. In order to make this process easier, we need to describe and define how data organization takes place within the big data system. This will help us better understand the data organization and access within the big data system.

Let's extend this example even further and say there is another application that reads from the big data system to understand the best times to advertise in a given TV series. This application should have a better understanding of all other data that is available within the big data system. So, without having a well-defined metadata system, it's very difficult to do the following things:

Understand the diversity of data that is stored, accessed, and processed
Build interfaces across different types of datasets
Correctly tag the data from a security perspective as highly sensitive or insensitive data
Connect the dots between the given sets of systems in the big data ecosystem
Audit and troubleshoot issues that might arise because of data inconsistency

Data governance

Having very large volumes of data is not enough to make very good decisions that have a positive impact on the success of a business. It's very important to make sure that only quality data should be collected, preserved, and maintained. The data collection process also goes through evolution as new types of data are required to be collected. During this process, we might break a few interfaces that read from the previous generation of data. Without having a well-defined process and people, handling data becomes a big challenge for all sizes of organization.

To excel in managing data, we should consider the following qualities:

Good policies and processes
Accountability
Formal decision structures
Enforcement of rules in management

The implementation of these types of qualities is called data governance. At a high level, we'll define data governance as data that is managed well. This definition also helps us to clarify that data management and data governance are not the same thing. Managing data is concerned with the use of data to make good business decisions and ultimately run organizations. Data governance is concerned with the degree to which we use disciplined behavior across our entire organization in how we manage that data.

It's an important distinction. So what's the bottom line? Most organizations manage data, but far fewer govern those management techniques well.

Fundamentals of data governance

Let's try to understand the fundamentals of data governance:

Accountability
Standardization
Transparency

Transparency ensures that all the employees within an organization and outside the organization understand their role when interacting with the data that is related to the organization. This will ensure the following things:

Building trust
Avoiding surprises

Accountability makes sure that teams and employees who have access to data describe what they can do and cannot do with the data.

Standardization deals with how the data is properly labeled, describe, and categorized. One example is how to generate email address to the employees within the organization. One way is to use firstname-lastname@company.com, or any other combination of these. This will ensure that everyone who has access to these email address understands which one is first and which one is last, without anybody explaining those in person.

Standardization improves the quality of data and brings order to multiple data dimensions.

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Devendra Dharm May 28, 2018

This is a highly practical, no-nonsense and to-the-point kind of a book which is the kind of books I like where the author gets to the meat of the content without wasting a lot of time in build up to the climax in every topic being discussed! It provides a broad coverage of Big Data Topics with just the right amount of detail to help one jumpstart on their big data journey without missing the big picture or the key pieces of the puzzle. The book systematically talks about Big Data all the way from the basic installation of Hadoop through data ingestion tools and techniques, commonly used big data stores and real-time data streams to building an enterprise search. The simplicity of the book makes it very easy to understand and the examples provided make it relatively easier to practically implement those concepts.

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Sagar Jadhav Jun 03, 2018

In a long time this is one of the most pragmatic and most well explained book written in tune with the current and future industry practices. Not only will this book help beginners but also give a clear insight for experienced professionals as well working in the data engineering field. I would highly recommend this book for in premise and cloud deployments as well. As an engineer myself it helped me adopt few best practices from this book. Happy leary folks.

Girish Sherikar Apr 19, 2018

I found this book to be a great resource due to it's simple to understand language, emphasis on hands on and practical information. Discussion of concepts is concise and clear, so none of the topics become a drag on staying focused. A must have reference for beginners and practitioners alike.

Joe G Dec 15, 2020

It provides good content.

Player#30 May 01, 2018

Being new to the Big data topic I was looking for a book which not only covers basics but also provides examples and details in the implementation areas including data life cycle, data visualization and such. I found this book an easy to read with lot of practical examples. I must read for those new to this area and also for those who would like to expand their big data horizons.

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