Leveraging Real-Time Big Data analytics in a Modern Telecom environment
Hyppönen, Jyri (2016)
Haaga-Helia ammattikorkeakoulu
2016
Creative Commons Attribution-NonCommercial-NoDerivs 1.0 Suomi
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:amk-2016121921097
https://urn.fi/URN:NBN:fi:amk-2016121921097
Tiivistelmä
Big Data is not a new challenge, and nowadays the focus has shifted from getting results to getting results fast.
For analytics, faster is always better because faster reaction time improves many situations, such as detecting network faults. Faster speed of detecting allows for more time to minimize the impact of the incident.
However, reaching real-time analytics is not as simple due to many Big Data technologies just were not designed with speed in mind. Thankfully the value of faster Big Data applications has not gone unnoticed and there are currently multiple interesting applications that can help with faster processing or straight out streaming of data.
Difficulty can be selecting the right technology for the use case. Especially since different enterprises often have different business and technical requirements and platforms that they use.
The challenges were mapped by interviewing few key people in the organization.
For this case company Apache Spark seemed to be most suitable application for real-time analytics as it offers fast processing speed, streaming, is supported by the Hadoop stack they use and uses Java.
For analytics, faster is always better because faster reaction time improves many situations, such as detecting network faults. Faster speed of detecting allows for more time to minimize the impact of the incident.
However, reaching real-time analytics is not as simple due to many Big Data technologies just were not designed with speed in mind. Thankfully the value of faster Big Data applications has not gone unnoticed and there are currently multiple interesting applications that can help with faster processing or straight out streaming of data.
Difficulty can be selecting the right technology for the use case. Especially since different enterprises often have different business and technical requirements and platforms that they use.
The challenges were mapped by interviewing few key people in the organization.
For this case company Apache Spark seemed to be most suitable application for real-time analytics as it offers fast processing speed, streaming, is supported by the Hadoop stack they use and uses Java.