Big
Data is much of a buzzword in the technical industry. However, many people are
still confused about the differences between Big Data and the traditional forms
of Data Warehousing. Through the expansion of several technology innovations
the old separation, which used to exist between the online and offline world is
slowing eroding away. Two big factors are driving this. One is the huge
expansion in mobility and the fact that people are increasingly using their
mobile devices in the real world. The other factor is the Internet of Things. The
simple devices have turned much more effective today and can be connected to
the Internet. This is leading to generation of huge amounts of data.
Big
Data is much more than just data itself. It is characterized by volume,
velocity, variety and veracity; each of which is much more in size we have ever
handled. The management of Big Data, thus, involves different methods for
storing and processing data. With the amount of data volume involved, there is
a high likelihood of bad data embedded in the mix. This increases the
challenges of quality assurance testing.
Big
Data actually talks about the questions that can be found in one's business.
The question can be about the future investments and their viability. Big Data
gives us answers to such questions before making decisions. Hence, testing Big data
is not about analyzing data, but also to pose queries about future business.
Some of the biggest challenges of Big
Data are as follows:
- Seamless Integration: It is a great challenge to integrate the BI framework
with Big Data components, because the search algorithm may not be in a format
as per the valid data warehousing standards. This runs the risk of poor BI
Integration. Consequently, the quality of the overall system cannot be assured
because of the operational disparity of the BI and the Big Data Systems.
- Data standards and data cleanup: Big Data systems lack the precise
rules for filtering out or scrubbing out bad data. Its data sources are usually
unheard of and are usually unconventional sources such as sensors, social media
inputs, surveillance cameras, etc.
- Real Time Data processing: Big Data solutions need accuracy in
high density data volumes and make them BI friendly. The Big Data set
processing frameworks execute them in smaller clusters or nodes. This is done
in batches and not exactly in real-time mode.
Considering
the challenges in putting an appropriate strategy into practice for assuring
quality, the following seven features need to be considered for an ideal
end-to-end Big Data Assurance Framework.
- Data Validation: The ecosystems for Big Data need to be able to read data
of any size, from any source and at any speed. The framework should be able to
validate the data drawn from a variety of unconstrained range of sources,
structured data such as text files, spreadsheets, xmls, etc. and unstructured
data such as audio, video, image, Web and GIS.
- Initial data Processing: The Big Data system should be able
to validate initial unstructured data processing and use an appropriate
processing script for real time data analysis and communication.
- Processing logic validation: The data processing language defined
in the query language should be able to clean and organize the huge
unstructured data into clusters. The framework should be able to validate the
raw data with the processed data in order to establish data integrity and
prevent data loss.
- Data processing speed testing: The Big Data Assurance framework
should integrate seamlessly with tools like ETL validator, Query Surge, etc.
Such tools help to validate data from source files and other databases, compare
with the required data and rapidly formulate reports after analysis. It should
also enable high speed processing of bulk data, as speed is very crucial in
real-time digital data analysis.
- Distributed file system validation: Validation is the key for Big Data based
frameworks. This validation feature helps the framework in flowing parameters
such as velocity, volume and variety of data in each node.
- Job Profiling validation: Job profiling is required in Big Data frameworks prior
to the processing of unstructured data algorithms, as errors in such algorithms
raise the chances for job failure.
- Field to field map testing: Massive architectural makeovers take
place while migrating from legacy to Big Data systems. The solution-specific integration testing is
carried out with a field-to-field mapping and schema validation as a very
important part.
Conclusion
For
leveraging the full patenting of Big Data, the main key is to work out an
effective validation strategy. The success of a business is directly
proportional to the effectiveness of the testing strategy of validating
structured and unstructured data of large volume, variety and velocity. Such
solutions not only will help in building
a framework for data validation over an existing data warehouse, but But also
empower them to adopt new approaches for faster and more meaningful analysis of
bulk data.
