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Mar 2014


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Live webinar on Big Data Technology and Use Cases

Online Event


Overview: Big Data can be defined as data that will be stored in data stores categorized as "NoSQL" due to their lack of compatibility with the SQL language that is so ubiquitous in the relational database world. Typically, though not always, these solutions will exceed a size threshold that causes the user to willingly "give up" the mature capabilities of a relational database for the ability to cost-effectively store, and lightly access, the volume of data. NoSQL is sometimes referred to as "not only SQL" because of the need for both SQL and NoSQL solutions at almost any company. 

Most NoSQL is open source and most of the well over one hundred NoSQL open source projects are not data stores. However, there are a significant numbers, perhaps dozens, of them that are. 
NoSQL solutions originated out of a need by data-oriented companies like Google, Facebook, eBay and Yahoo to store the massive amounts of information their systems generate. The fate of these companies lies in creating outstanding personal experiences and they are able to utilize every click and every aspect of a page render in their analysis of customer behavior. Commercial software proved too expensive, papers were published and companies took up development of solutions, which soon spread to the community in the open source software development model. 

It is important to select the correct category of NoSQL database management system to store the data. Data will be typically stored only once and according to the most important use case due to the high volume. There are five categories: 

  • Hadoop
  • Key-Value Stores (non-Hadoop)
  • Column Stores (sometimes referred to as wide column stores)
  • Graph Databases
  • Document Stores

Certain aspects of NoSQL are common across all the categories and projects:

  • The Open Source nature of most of the tools, mostly Apache Open Source
  • Use of Map Reduce as the data access paradigm, which is batch and keeps processing as close to the data as is possible
  • Data model implemented as JavaScript Object Notation (JSON)
  • Use of Sharing (horizontal partitioning of data across file systems)
  • Very Near Linear Scaling into petabytes
  • Not strictly ACID compliant (atomicity, consistency, isolation and durability; not meant for transaction processing)
  • Highly Fault Tolerant (but not with RAID)

As companies increasingly begin to understand that no matter what business they are in, they are in the business of information and they need to develop a competency for Information Development to take control of their data. This includes data that is best suited for NoSQL solutions. Some of the data that exceeds the volume, variety and complexity thresholds that make NoSQL attractive include:

  • Sensor-read data
  • Detailed clickstream data
  • Full transaction logs
  • Heavy-relationship-based data (for Graph Databases)
  • Web-crawled data

The need to manage Big Data and the benefits of mastering it will trump the inertia currently at work upholding the paradigm of loading all data into a database using ETL and then querying that data. BigData/NoSQL solutions have a chasm to cross, but it will need to happen soon. The investment in BigData by the large IT vendors and the investment community certainly supposes adoption well beyond the initial group of Silicon Valley "new economy" companies like eBay, Amazon, Yahoo and Google - companies who, incidentally, wrote many of the solutions. The Big Data landscape could radically change if the adoption does not continue through to the big insurance companies, car manufacturers, healthcare companies and big retailers.

Why should you attend: Big Data Technology and Use Cases provides an approach for storing, managing and accessing data of very high volumes, variety or complexity. Storing large volumes of data from a large variety of data sources in traditional relational data stores is cost-prohibitive. And regular data modeling approaches and statistical tools cannot handle data structures with such high complexity. This seminar discusses use cases and new types of data management systems based on NoSQL database management systems and MapReduce as the typical programming model and access method. 

Areas Covered in the Session:

  • The main characterisics of Hadoop and NoSQL databases
  • Differences between a distributed NoSQL database and relational databases
  • Use cases for big data, with real-world examples from organizations in-production today
  • The integration of Hadoop with the Data Warehouse
  • The placement of big data in information architecture
  • Scale up versus scale out
  • MapReduce
  • Graph Stores: All About Relationships
  • Enablers for big data in the enterprise

Who Will Benefit:

  • CIO
  • Information Architects
  • Data Warehouse Architects, Designers
  • Developers, and Administrators
  • Data Management Staff
  • Program and Project Managers
  • Center of Excellence Staff
  • Application Developers
  • IT architects
  • Managers who want to understand the purchases their staff are recommending
  • DBAs




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