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Big Data Imperatives, focuses on resolving the key questions on everyone’s mind: Which data matters? Do you have enough data volume to justify the usage? How you want to process this amount of data? How long do you really need to keep it active for your analysis, marketing, and BI applications?
Big data is emerging from the realm of one-off projects to mainstream business adoption; however, the real value of big data is not in the overwhelming size of it, but more in its effective use.
This book addresses the following big data characteristics:
- Very large, distributed aggregations of loosely structured data – often incomplete and inaccessible
- Petabytes/Exabytes of data
- Millions/billions of people providing/contributing to the context behind the data
- Flat schema's with few complex interrelationships
- Involves time-stamped events
- Made up of incomplete data
- Includes connections between data elements that must be probabilistically inferred
Big Data Imperatives explains 'what big data can do'. It can batch process millions and billions of records both unstructured and structured much faster and cheaper. Big data analytics provide a platform to merge all analysis which enables data analysis to be more accurate, well-rounded, reliable and focused on a specific business capability.
Big Data Imperatives describes the complementary nature of traditional data warehouses and big-data analytics platforms and how they feed each other. This book aims to bring the big data and analytics realms together with a greater focus on architectures that leverage the scale and power of big data and the ability to integrate and apply analytics principles to data which earlier was not accessible.
This book can also be used as a handbook for practitioners; helping them on methodology,technical architecture, analytics techniques and best practices. At the same time, this book intends to hold the interest of those new to big data and analytics by giving them a deep insight into the realm of big data.
- Sales Rank: #1060029 in Books
- Published on: 2013-06-24
- Released on: 2013-06-27
- Original language: English
- Number of items: 1
- Dimensions: 9.02" h x .73" w x 5.98" l, .95 pounds
- Binding: Paperback
- 320 pages
About the Author
Soumendra Mohanty? is a Partner and leads Accenture?s Global Information Management Services practice. He is an expert within the Information Management area, focusing primarily on BI architectures, data warehouse, CRM/Customer Insight, MDM, Analytics and PCM solutions.? He is experienced in leading project teams through the lifecycle of a project, and has successfully helped sell and delivered BI and DW projects in multiple industries, including products, CPG, brokerage, banking, telecommunications, and retail.Soumendra has authored several books on data warehousing and Analytics and published numerous journals in DM Review. He has also presented in numerous international forums. His functional expertise ranges from Big Data Analytics, BI Architectures, Data Warehouse, CRM/Customer Insight, Supply Chain Analytics, Marketing Insights, & MDM.
Most helpful customer reviews
3 of 3 people found the following review helpful.
Seminal Book on Big Data
By Ashwin Desikan
A seminal book which takes the reader beyond the fancy term of "Big Data" and clearly elicits how big data can transforms today's enterprises to better serve its customers. The book provides food for thought for readers from different backgrounds. Business leaders and analyst would find Use cases provided in Chapter 3 interesting as it suggests applications for big data in various industries. Architecture patterns in chapter 5 along with modelling and implementation techniques using NoSql, specific solutions like Hadoop, in-memory grids etc would appeal to technologist. Chapter 9 discusses in depth about the skills which needs to be acquired to become a data scientist.
Having worked and consulted in BFSI, I was able to correlate with the specific use cases provided for the financial industry. The book made me rethink how I would have implemented some of the solutions we had developed years back if we had know about these techniques/ technologies.
Introduction to visualization techniques, in chapter 9, highlights when a particular visualization (bar chart, scatter plot etc) can be used.
The book has personally given me a different perspective on how to view / structure our business data. By applying the techniques suggested we might be able to; solve our customer needs better; build simple and more effective solutions.
-Ashwin Desikan, founder & CTO, Aasan Software
1 of 1 people found the following review helpful.
A long long talk about Big Data opportunities for the non tech savvy.
By Jascha Casadio
I did not like the book, I'm honest. And I did not like it for a couple of reasons. First, this book targets those interested in the business aspects of Big Data, and I'm not among them; second, even if I were, I don't really feel that enlighten by this title. It's a big long talk about the opportunities Big Data offers and its implications on the enterprise budget.
It's a big talk.
As the authors suggest, Big Data Imperatives can be broadly divided into three parts:
Part 1, chapters one to three, is a speech about the traditional approaches to data management and their failure to handle Big Data. The discussion then moves to the opportunities and costs it presents to the enterprise. Here the authors provide real use cases, such as retail, insurance and telecommunications.
Chapters four to six included represent instead the second part of the book. Here the focus is to find which technologies better suit the needs of the company. The talk starts with the evolution of databases. Concepts like read/write ratio, consistency and sharding make their appearance. Their impact on data analysis is also explored.
The last chapters of the book, seven to nine, cover the technologies available for developing and implementing Big Data analytics. The discussion is again centered around the inefficiency of RDBMs to satisfy the stakeholders needs. The talk quickly moves to noSQL, Hadoop and Map/Reduce.
The figure of the data scientist is the subject of the last chapter. The magic the business can expect from this divine person is described with exhaustive details.
Finally, a note about the many schemas present in the book. I must admit that the authors dedicated much time to the details of each image in the book. Kudos to their quality. Still, each comes with lots of text. When I say lots I mean they are really overly populated with text, which decreases their added value.
I don't personally suggest the book.
As usual, you can find more reviews on my personal blog: http://books.lostinmalloc.com Feel free to pass by and share your thoughts!
1 of 1 people found the following review helpful.
Content is good, but the quality of the printing could be better.
By Calvin Professor
The content strikes a good balance between the business and technical perspective. Unfortunately, the print quality is poor, and don't be fooled by the color graphics in the Amazon preview. When this arrived on the book shelves at the school as a text book (regular, full price), the graphics were B&W and not always clear due to the smaller than usual page size. What gives, Apress? My students were not happy campers.
See all 5 customer reviews...
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