Quản trị dữ liệu và trực quan hóa

THE DATA ANALYTICS PIPELINE
Data does not arrive nicely formatted and ready to be consumed.
The data pipeline is paramount to understanding any analytics solution.

We Hold These Truths…
• A database has a schema
• We transform the data into that schema
• Data conforms to the schema we define
• The schema defines the business

Data Lake| Tài liệu tham khảo môn quản trị dữ liệu và trực quan hóa| Trường Đại học Bách Khoa Hà Nội. Tài liệu gồm 24 trang giúp bạn ôn tập và đạt kết quả cao trong kỳ thi sắp tới. Mời bạn đọc đón xem.

Objective:
Understand how the traditional data landscape is changing,
what opportunities big data presents and what architectures
allow you to maximize the benefits to your organization.

Alex Gorelik
‣ Founder and CEO, Waterline Data
‣ 3rd start up: Acta (SAP), Exeros (IBM), Waterline
‣ 25 years of building data related technology (Sybase, IBM, Informatica)
‣ 5 years of hands on IT consulting (BAE, Unilever, IBM, Jysk, Stanford Hospital, Kaiser, etc.)
‣ Last 5 years focused on Big Data (Waterline, Informatica, Menlo Ventures, IBM)
‣ IBM Distinguished Engineer
‣ BS CS Columbia, MS CS Stanford

Visual Explanations| Giáo trình quản trị dữ liệu và trực quan hóa| Trường Đại học Bách Khoa Hà Nội. Tài liệu gồm 150 trang giúp bạn ôn tập và đạt kết quả cao trong kỳ thi sắp tới. Mời bạn đọc đón xem.

HUMAN perception plays an important role in the area of
visualization. An understanding of perception can
significantly improve both the quality and the quantity of
information being displayed [1]. The importance of percep-tion was cited by the NSF panel on graphics and image
processing that proposed the term “scientific visualization”
[2]. The need for perception was again emphasized during
recent DOE-NSF and DHS panels on directions for future
research in visualization [3].

We wrote The Big Book of Dashboards for anyone tasked with building or overseeing the development of business dashboards. Over the past decade, count-less people have approached us after training sessions, seminars, or consultations, shown us their data, and asked: “What would be a really good way to show this?”

I encounter a lot of less‐than‐stellar visuals in my work (and in my life—once you get a discerning eye for this stuff, it’s hard to turn it off). Nobody sets out to make a bad graph. But it happens. Again and again. At every company throughout all industries and by all types of people. It happens in the media. It happens in places where you would expect people to know better. Why is that?

Data integration has been the information systems profession’s most enduring challenge.

It is almost four decades since Richard Nolan nominated data administration as the penultimate stage of his data processing maturity model, recognizing that the development of applications to support business processes would, unless prop-erly managed, create masses of duplicated and uncoordinated data.