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BI in The Big Data Era
Transcript of BI in The Big Data Era
BI in the Big Data era
BI &בBig Data
"Business intelligence (BI) is a set of theories, methodologies, processes, architectures, and technologies that transform raw data into meaningful and useful information for business purposes. BI can handle large amounts of information to help identify and develop new opportunities".
From raw data to BI
"Business intelligence systems combine operational data with analytical tools to present complex and competitive information to planners and decision makers. The objective is to improve the timelines and quality of inputs to the decision process".
"Big data is the term for a collection of data sets so large and complex that it becomes difficult to process using on-hand database management tools or traditional data processing applications.
Communications of the Association for Information Systems
"Big data is high-volume, high velocity and high-variety information assets that demand cost-effective, innovative forms of information processing for enhanced insight and decision making"
Scaling up VS scaling out
Single software solution.
Better Drill Down capabilities.
So what is the big problem?!?
1. Key Words
3. BI Process
Loss of data
5. key words
9. The problem
10. Case Study
11. Future Steps
Data streams from many sources such as: Retail entities, Transactions, Business entities etc.
The massive amount of data loads into targeted databases to farther aggregation
During a long data processing period, the massive amount of data reduced into a valuable business information.
From this moment on the BI task take place:
The business information is processed by the relationships between the various tables
The final step is the visualization of the analyzed data using reports
Extract Transfer Load
On-Line Analytical Processing
Provides immediate access to right information.
loads into memory instead of hard disks.
can be as large as data mart or small data warehouse.
Easy navigation and ability to modify queries.
Allows access to real time data and create reports.
Minimizes the need for IT staff.
40 million monthly players and more than 3 billion games played per month globally.
2 billion new rows are produced each day.
Customer behaviors: number of players, the number of games played, and time played instantly.
Process: ETL, Aggregate.
Technologies: parallel computing(14).
Language: java, Pig\Hive, NoSQL.
process: ETL, Aggregate.
Technologies: Database, Data Warehouse.
not only SQL
"volume, velocity, variety"
Lost in translation...
BI & Big Data
Based on a survey of over 4,000 information technology (IT) professionals from 93 countries and 25 industries - business analytics is one of the four major technology trends in the 2010's.
IBM Tech Trends
97 percent of companies with revenues exceeding $100 million were found to use some form of business anallytics.
By 2018, the United States alone will face a shortage of 140,000 to 190,000 people with deep analytical skills.
IBM Tech Trends
McKinsey Global Institute
Big data sizes are a constantly moving target, as of 2012 ranging from a few dozen terabytes to many petabytes of data in a single data set.
In 2012, every day 2.5 quintillion bytes of data (1 followed by 18 zeros) are created, with 90% of the world’s data created in the last two years alone.
144.8 billion E-mail messages.
340 million tweets.
684,000 bytes of Facebook content.
72 hours (259,200 seconds) of new video to YouTube.
Google receives over 2 million search queries.
Apple receives around 47,000 app downloads a minute.
1 Bit = Binary Digit
8 Bits = 1 Byte
1024 Bytes = 1 Kilobyte
1024 Kilobytes = 1 Megabyte
1024 Megabytes = 1 Gigabyte
1024 Gigabytes = 1 Terabyte
1024 Terabytes = 1 Petabyte
1024 Petabytes = 1 Exabyte
1024 Exabytes = 1 Zettabyte
1024 Zettabytes = 1 Yottabyte
1024 Yottabytes = 1 Brontobyte
1024 Brontobytes = 1 Geopbyte
MapReduce - the algorithm behind the hadoop
Cloud "On Demand" computers
Scale when need
Answering multi-dimensional analytical queries
allowing complex analytical and ad hoc queries with a rapid execution time
OLAP consists of three basic analytical operations:
1. consolidation (roll-up)
3. slicing and dicing
Here we can see an how it really works: