Diversity in Management: Brings differentiation in product offering.
Diversity in Social Science: Brings Stability and Wholesome view of social paradigm hence sustainability in long term.
Diversity in healthcare: Brings new comprehensive approach towards solving health issues like from pathology, physiology , to use other speciality and engineering speciality now like we see data science usage.
Diversity in Technology: In Tech domain diversity is more important than anything else like you see What brings disruptive innovations ? its complete new technology from ground up. First thing is cleaning slate of old technology with learning outcomes from old yet start fresh with new approach. Can diversity bring that ? you can answer urself.
Diversity Business process thinking implementation : Diversity approach can bring BPR Business process Re-engineering.
Datawarehouses maintain data loaded from operational databases using Extract Transform Load ETL tools like informatica, datastage, Teradata ETL utilities etc…
Data is extracted from operational store (contains daily operational tactical information) in regular intervals defined by load cycles. Delta or Incremental load or full load is taken to datwarehouse containing Fact and dimension tables which are modeled on STAR (around 3NF )or SNOWFLAKE schema.
During business Analysis we come to know what is granularity at which we need to maintain data. Like (Country,product, month) may be one granularity and (State,product group,day) may be requirement for different client. It depends on key drivers what level do we need to analyse business.
There many databases which are specially made for datawarehouse requirement of low level indexing, bit map indexes, high parallel load using multiple partition clause for Select(during Analysis), insert( during load). data warehouses are optimized for those requirements.
For Analyticwe require data should be at lowest level of granularity.But for normal DataWarehouses its maintained at a level of granularity as desired by business requirements as discussed above.
for Data characterized by 3V volume, velocity and variety of cloud traditional datawarehouses are not able to accommodate high volume of suppose video traffic, social networking data. RDBMS engine can load limited data to do analysis.. even if it does with large not of programs like triggers, constraints, relations etc many background processes running in background makes it slow also sometime formalizing in strict table format may be difficult that’s when data is dumped as blog in column of table. But all this slows up data read and writes. even is data is partitioned.
Since advent of Hadoop distributed data file system. data can be inserted into files and maintained using unlimited Hadoop clusters which are working parallel and execution is controlled byMap Reduce algorithm . Hence cloud file based distributed cluster databases proprietary to social networking needs like Cassandra used by facebook etc have mushroomed.Apache hadoop ecosystem have created Hive (datawarehouse) https://sandyclassic.wordpress.com/2011/11/22/bigtable-of-google-or-dynamo-of-amazon-or-both-using-cassandra/
With Apache Hadoop Mahout Analytic Engine for real time data with high 3V data Analysis is made possible. Ecosystem has evolved to full circle Pig: data flow language,Zookeeper coordination services, Hama for massive scientific computation,
HIPI: Hadoop Image processing Interface library made large scale image processing using hadoop clusters possible. http://hipi.cs.virginia.edu/
with innovation in hadoop ecosystem spanning every direction.. Even changes started happening in other side of cloud stack of vmware acquiring nicira. With huge peta byte of data being generated there is no way but to exponentially parallelism data processing using map reduce algorithms.
There is huge data out yet to generated with IPV6 making possible array of devices to unique IP addresses. Machine to Machine (M2M) interactions log and huge growth in video . image data from vast array of camera lying every nuke and corner of world. Data with a such epic proportions cannot be loaded and kept in RDBMS engine even for structured data and for unstructured data. Only Analytic can be used to predict behavior or agents oriented computing directing you towards your target search. Bigdata which technology like Apache Hadoop,Hive,HBase,Mahout, Pig, Cassandra, etc…as discussed above will make huge difference.
kindly answer this poll:
Some of the technology to some extent remain Vendor Locked, proprietory but Hadoop is actually completely open leading the the utilization across multiple projects. Every product have data Analysis have support to Hadoop. New libraries are added almost everyday. Map and reduce cycles are turning product architecture upside down. 3V (variety, volume,velocity) of data is increasing each day. Each day a new variety comes up, and new speed or velocity of data level broken, records of volume is broken.
The intuitive interfaces to analyse the data for business Intelligencesystem is changing to adjust such dynamism since we cannot look at every bit of data not even every changing data we need to our attention directed to more critical bit of data out of heap of peta-byte data generated by huge array of devices , sensors and social media. What directs us to critical bit ? As given example https://sandyclassic.wordpress.com/2013/06/18/gini-coefficient-of-economics-and-roc-curve-machine-learning/
for Hedge funds use hedgehog language provided by : http://www.palantir.com/library/ such processing can be achieved using Hadoop or map-reduce algorithm. There are plethora of tools and technology which are make development process fast. New companies are coming from ecosystem which are developing tools and IDE to make transition to this new development easy and fast.
When market gets commodatizatied as it hits plateu of marginal gains of first mover advantage the ability to execute becomes critical. What Big data changes is cross Analysis kind of first mover validation before actually moving. Here speed of execution will become more critical. As production function Innovation gives returns in multiple. so the differentiate or die or Analyse and Execute feedback as quick and move faster is market…
This will make cloud computing development tools faster to develop with crowd sourcing, big data and social Analytic feedback.
As cloud adoption picks up it will stir up networking stack..not only that telecom stack…precisely reason the great visionary bill gate picked up skpe for acquisition…and unified computing is at play again.
Here are Cisco back up plan how its affected.: Cisco’s imediate threat from software driven networking
Through compute virtualization decoupling the operating system from the underlying hardware, compute virtualization provides an extremely flexible operational model for virtual machines that lets IT treat a collection of physical servers as a generic pool of resources. All data center infrastructure, including networking, should provide the same properties as compute virtualization.it will unlock a new era of computing more significant than the previous transformation from mainframe to client-server.
The advantages of this shift bear repeating: once infrastructure is fully virtualized,any application can run anywhere, allowing for operational savings through automation, capital savings through consolidation and hardware independence, and market efficiencies through infrastructure outsourcing models such as hosting and“bursting.”But, data center infrastructure is not yet fully virtualized. While the industry has decoupled operating systems from servers, it has not yet decoupled the operating system from the physical network. Anyone who has built a multi-tenant cloud is aware of the practical limitations of traditional networking in virtualized environments. These include high operational and capital burdens on the data center operators, who run fewer workloads, operate less efficiently, and have fewer vendor
choices than they would if their network was virtualized.
Problems with non virtualized network stack in data centre:
#1. Hardware Provisioning: Although VM provisioning is automated to run on any server.But creation of isolated network (and its network policy) is done manually by configuring hardware often through vendor specific APIs.effectively data centre operations are tied to vendor hardware and manual configuration.So upgrades are difficult.
#2. Address Space virtualization:
VM’s next hop is Physical router in network.There are 2 problems which arise
i) VM share same switch or L2 network limiting there mobility and VM placements.In multi-tenant environment it leads to downtime
ii) Sharing of same forwarding tables in L2 or L3 so no overlapping IP address space.In multi tenant IP adresses should be as desired by customer.virtual routing and forwarding (VRF) table limits and the need to manage NAT configuration make it cumbersome to support overlapping IP addresses or impossible to do at scale.
#3. Network services tightly coupled with hardware design cycle:
Due to long ASIC design development times,so organizations that operate the largest virtual data centers don’t rely on the physical hardware for virtual network for provisioning or virtual network services. Instead they are using software-based services at the edge, which allows them to take advantage of faster software development cycles for offering new services