Big Data
Big Data as the name suggests is a huge amount of data. Adding to it, the data is increasing rapidly everyday so handling this much data becomes more and more difficult with the passing time.
Also as the data increases, the cost for data warehouses along with the cost for networking bandwidth and data analytics rise higher. Analysis takes longer time to complete leading to slower results. With the social websites traffic, the rate is higher than ever.
Hence, there is a need to optimize big data to manage data in a way that improves product quality, speeds up decision-making, aggressively exploits new analytical capabilities and optimize business processes along with reducing the overall cost associated with a traditional data warehouse.
Big Data Optimization
We need a system that works on following principles:
Scalability
The system has to be expanded with the increasing data. The expansion of system should not impact the existing system. So the system should be easily scalable.
Fault Tolerance
Hadoop cluster can have multiple machines in a cluster, even in thousands for huge businesses like Yahoo. There is a good chance that some of them fail one time or another.
Such possibilities need to be considered. The system should be capable of coping with such situations without any significant effects.
Data Distribution
The data distribution should be done in such a way that the same machine should process the data where it is stored. If data storage and processing happen in different machines, it will need extra cost and time for data transmission.
Here, Hadoop can serve as a building block of your analytics platform, as it is by far one of the best ways to handle fast-growing data processing, storage and analysis. The key to optimization is to trim down the data in such a way that it represents the whole data effectively implementing the principles discussed above.
One way is to grab the data but with the passing time reduce the older data but predicting what a user might need even after a year is not possible. Companies can leverage the cloud for targeted analytics like a sandbox environment to run analytics and identify the needed data and flush the unwanted ones.
The latter is the principle on which MapReduce works where user defines a map function to map data of one type with one key making a basket, which in turn is used as input for reduce function that processes the information to produce a relevant result for storage.
Challenges in Big Data Optimization
Preprocessing
Preprocessing the data is a very important, time-consuming and complicated task where the noise is filtered out from huge volumes of unstructured and structured data continuously and the data is compressed by understanding and capturing the context into which data has been generated.
Information Extraction
Extracting meaningful information from huge amounts of data of poor quality is one of the major challenges being faced in big data. So data cleaning and data quality verification are critical for its accuracy.
Data Integration, Aggregation and Representation
Data collected is not homogenous. It may have different metadata. Thus Data integration requires huge human efforts.
It is difficult to come up with aggregation logic for huge scale of big data manually, hence the requirement of newer and better approaches arises. Also different data aggregation and representation strategies may be needed for different data analysis tasks.
Query Processing, and Analysis
Methods suitable for big data need to be discovered and evaluated for efficiency so that they are able to deal with noisy, dynamic, heterogeneous, untrustworthy data.
Why AppPerfect?
AppPerfect offer you an optimized big data environment to manage your big data implementation properly. We help you to achieve your big data analytics needs with optimized algorithms and minimal resource utilization. We have experience in working with Hadoop and various other tools which can help in Big Data Optimization.
AppPerfect's Big Data Optimization Services help you with following:
- Big Data Applications with minimum costs and improved resource utilization.
- Improve the efficiency of analytics algorithm.