Question

Define big data and describe the technologies for managing and analyzing it. What technologies you have personally utilized or experienced in previous jobs or projects. How would or have you used big data to obtain key information? If you have used technologies to obtain key information, what information did you collect?

Answer 1

Big data is an essential part of any organisation which has different layers. It has different stages which passes through raw statistics or unstructured data to an actionable structured data.

Layers are necessary to build a system which moves from raw data material to the specific actionable data inside.

Layers which are implemented into the big data are as follows

Data source layer

Data storage layer

Data analysis layer

Data output layer

Data source layer

This is specifically here is the first layer where the data arrives. Data which arise at this specific layer is unstructured as well as contains multiple sales record customer database is a feedback. It is one of the first Strategies and steps in setting up the data strategy for assessing the specific data which is entered into the organisation. Determination of the new sources or available data is possible.

Data storage layer

This specific layer has all the big data which arise at your organisation. Once the big data enters your organisation it is stored into this specifically her. Some specifically Design Tool have been developed to store this kind of data and as well as Apache Hadoop dfs system or Google file system has widely been used for scoring the big data. Every database has its own understanding of Hadoop has its own HBase and all of them are based on the noSQL architecture which are very popular.

Data processing layer

In this layer data is processed and analysed. Most common for analysing and processing data is mapreduce tool. This is specific tool uses this big data to analyse the specific and process the specific terms which has been implemented into the big data. Larger organisations have invested in their own Data Analytics tool as well as teams which are part of their big data. Automatic patterns as well as different algorithms have also been implemented into the organisation for better big data mining. Drawing the conclusions regarding the manual data analysis is also present.

Data output layer

This layer contains the passed data which comes out from the organisation for the specific benefit of the teams as well as people. Clear and concise communicate is very essential in this is specific stage and the output can be in form of charts reports, figures or recommendations. Big data analysiss final part is this specific stage and the measurable improvement in at least one KPI has to be achieved by the specification taken on the basis of the analysis which has been carried out.

Predictive modelling is a process in which data Mining or big data is used to create a prediction of the specific model which is later used as a prototype. Number of forecast items and predictors are used to obtain the specific model which is Virtually assign. When the data has been collected from relevant predictors statistical. Complex software algorithm as well as statistical analysis model is design with the help of the specific softwares and they are validated or specifically revised.

Offshore data warehousing is very widely being used for organisations for improving the overall data warehousing needs as well as reducing the overall cost involved in data warehousing. Offshore development of the data warehousing provides extensive support to the stopping as well as it also increase the overall productivity by lowering and maintaining the cost which are heavily invested in on shore data warehousing systems. Offshore data warehousing systems are very flexible in terms of acquiring band with. If a project is specifically determined with required details,Offshore model is the most appropriate way to use with this specific situation as it provides intensive support to the overall structure of organisation as of your data warehousing is very effective in terms of directly affecting the total bandwidth captured by the organisation.

Basic limitation of the Offshore data warehousing is that it is very stressful to the changes. For making any kind of change in the of your data warehouse, intensive efforts are need to be done which is directly implemented into the chain management system.

Development of the Offshore models are widely developed as per the need of extreme data warehousing for most of the organisation which is creating problem on land. Pirate using the overall cost and development of the Offshore data warehousing systems, overall implementation of multiple requirements for your organisation which include intensive amount of big data is stored in data warehouse is which is build Offshore for the benefit of the organisation.

Offshore data warehousing systems also have different risk involved as the maintenance level of the Offshore warehousing system is very high and any kind of repair which is done on the system is very costly. Once the link is damaged between the data warehouse which is located Offshore and the line centre, mitigating the problem and finding the issue in the connections is one of the biggest issues which arrived in this type of data warehousing.

All in all we can say that Offshore data warehousing has been very effective in implementing better Strategies for the organisation to reduce their overall expenses in big data storage. By lowering the overall expenses in the of big data storage by doing Offshore development of the data warehousing systems, organisations are directly focusing on creating better Strategies for managing the data and improving their overall market situation. It also increases the flexibility which makes it more efficient for the Change management of the organisations as it is a requirement of today's business.

P.S.- P.S.- Please use separate threads to ask multiple questions at a time and leave a comment ig any explanation is needed.