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DATA ARCHITECTURE A PRIMER FOR THE DATA SCIENTIST PDF

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3 days ago Vault - [Free] Data Architecture A Primer For The Data Scientist Big Data Data Vault [PDF] [EPUB] William H. (Bill) Inmon (born ) is an. Editorial Reviews. About the Author. Best known as the “Father of Data Warehousing,” Bill Inmon has become the most prolific and well-known author worldwide. Purchase Data Architecture: A Primer for the Data Scientist - 1st Edition. Print Book & E-Book. DRM-free (EPub, PDF, Mobi). × DRM-Free.


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Download this large ebook and read on the Data Architecture A Primer For A Primer For The Data Scientist Big Data Data Warehouse And Data Vault PDF. Read "Data Architecture: A Primer for the Data Scientist Big Data, Data Warehouse and Data Vault" by W.H. Inmon available from Rakuten Kobo. Sign up today. Since the task of a Data Scientist at any company can be vastly different ORYX - Lambda Architecture Framework using Apache Spark and Apache .. Machine Learning Module - Class on machine w/ PDF,lectures,code.

Consider the data as shown in Figure 1. Structured data is typically found as a by-product of transactions. Every time a sale is made, every time a bank account encounters a withdrawal, every time someone transacts an ATM activity, and every time a bill is sent a record of the transaction is made.

The record of the transaction ends up as a structured record.

Data Architecture: A Primer for the Data Scientist (eBook)

Unstructured repetitive data is quite different. Unstructured repetitive records are typically records of machine interactions, such as the analog verification of product coming off a manufacturing process or the metering of energy usage by a consumer.

Consider metering.

There is great repetition of records in both form and substance that are created when looking at metered readings. Unstructured nonrepetitive information is fundamentally different than unstructured repetitive records.

With unstructured nonrepetitive records there is little or no repetition of either form or content from one record to the next.

Some examples of unstructured nonrepetitive information include email, call center conversations, and market research. When you look at one email, the odds are very good that the next email in the database will be different than the previous email.

The same is true for call center information, warranty claims, market research, and so forth. Unstructured repetitive data and unstructured nonrepetitive data have very different characteristics, in many different ways.

One of the ways that these two types of data are different is in terms of business relevancy.

Why is there an increased demand for data scientists?

In unstructured repetitive data, there often are very few records that are of real business interest. With unstructured nonrepetitive data, however, there is a very large percentage of business-relevant data. As an example of a small percentage of repetitive unstructured data being business relevant, consider the millions of phone calls that are made each day.

The government is only interested in a very few phone calls out of the millions that have been made.

Or consider manufacturing control information. Nearly all manufacturing records are not of interest. Only a very few records — usually where the parameters being measured exceed a threshold — are of interest.

Oftentimes with unstructured repetitive records, there are records that are not directly or immediately of interest but are potentially of interest in this category. There are not too many records that are not of interest when it comes to unstructured nonrepetitive data.

There is spam and there are stop words. But other than those two categories of information, nearly all unstructured nonrepetitive data is of interest.

It is of interest to note that Big Data consists of the unstructured repetitive and the unstructured nonrepetitive data in the corporation, as seen in Figure 1. At first it may seem that the differences between the two types of unstructured data — unstructured repetitive and unstructured nonrepetitive data — are almost whimsical or trivial.

In fact the differences between the two types of unstructured data are anything but trivial. Because of the profound differences between the two types of data, there is a great divide that separates the two types of unstructured data.

The great divide that separates the two types of unstructured data occurs because data on one side of the divide is handled one way and data on the other side of the divide is handled in an entirely different manner. For all practical purposes the data found on the different sides of the great divide might as well exist on different planets.

The division in the way that data is handled is such that unstructured repetitive data is almost entirely consumed with a fixation on managing Hadoop. For unstructured repetitive data the emphasis is entirely on accessing, monitoring, displaying, analyzing, and visualizing data residing on a Big Data manager such as Hadoop. The emphasis on unstructured nonrepetitive data is almost entirely centered on textual disambiguation.

The emphasis here is on the types of disambiguation, the reformatting of the output, the contextualization of the data, the standardization of the data, and so forth.

The remarkable thing about the great divide is that the disciplines surrounding the data are so diametrically different. Textual disambiguation is a very different subject than the access and analysis of data stored on Hadoop. It is because of the extreme differences between these two worlds that it is said that the two worlds live in different planets.

Dividing Unstructured Data Unstructured data can further be divided into two basic forms of data — repetitive unstructured data and nonrepetitive unstructured data. As is the case with the division of corporate data, there are many ways to subdivide unstructured data.

The method shown here is but one of many ways to subdivide unstructured data. This simple subdivision of unstructured data is shown in Figure 1. Typically, repetitive data occurs many, many times. The structure of repetitive data looks exactly the same or substantially the same as the previous record.

There is no massive and elaborate infrastructure managing the content of repetitive unstructured data. Nonrepetitive unstructured data is data where the records are substantially different from each other. In general each nonrepetitive record is markedly different from each other record. The division of data types in the corporation has many different embodiments. Consider the data as shown in Figure 1.

Every time a sale is made, every time a bank account encounters a withdrawal, every time someone transacts an ATM activity, and every time a bill is sent a record of the transaction is made.

The record of the transaction ends up as a structured record. Unstructured repetitive data is quite different. Unstructured repetitive records are typically records of machine interactions, such as the analog verification of product coming off a manufacturing process or the metering of energy usage by a consumer. Consider metering.

There is great repetition of records in both form and substance that are created when looking at metered readings. Unstructured nonrepetitive information is fundamentally different than unstructured repetitive records. With unstructured nonrepetitive records there is little or no repetition of either form or content from one record to the next.

Some examples of unstructured nonrepetitive information include email, call center conversations, and market research. Inmon was born July 20, in San Diego, California. In he founded Pine Cone Systems, which was renamed Ambeo later on.

In , he created a corporate information factory web site for his consulting business. Inmon promotes building, usage, and maintenance of data warehouses and related topics.

Data Architecture: A Primer for the Data Scientist (eBook)

In July , Inmon was named by Computerworld as one of the ten people that most influenced the first 40 years of the computer industry. In , Inmon developed and made public technology known as "textual disambiguation".

Textual disambiguation applies context to raw text and reformats the raw text and context into a standard data base format. Once raw text is passed through textual disambiguation, it can easily and efficiently be accessed and analyzed by standard business intelligence technology.

Textual disambiguation is accomplished through the execution of textual ETL. Publications[ edit ] Inmon has published more than 55 books and 2, articles on data warehousing and data management.

A selection: See if you have enough points for this item. One such way but hardly the only way to subdivide the data found in the corporation is to divide the totality of data into structured data and unstructured data, as seen in Figure 1. Unstructured repetitive data is quite different. Handy book for everyone working or studying data management or - data analytics.

Most references on Big Data look at only one tiny part of a much larger whole. But no one is looking at the larger architectural picture of how Big Data needs to fit within the existing systems data warehousing systems.