SQream

Mobile services subscribers use their connected devices for everything from voice, text, video, gaming, and more, creating enormous and fast-growing quantities of data. While this data carries great potential, Telecoms bear the difficult challenge of storing, navigating, and turning these huge data stores into actionable insights. Those telecom organizations that succeed in unlocking the intelligence gems hidden in these treasure troves of data often find themselves better poised to compete in an increasingly cluttered and competitive market.

The results are in from SQream's 2021 'The Challenges of Massive Data Analytics Report.' The big news? Even as organizations say they understand the need for prioritizing a data analytics strategy, 87% say they lack the budget to meet their data analytics needs. 

So how are industry professionals handling the explosion of data that was 2020? 'The 2021 'Challenges of Massive Data Analytics Report' was compiled with input from data professionals around the world to provide you with insights.

Hadoop came to prominence when the web exploded with unstructured data. The use of unstructured data is common for web analytics, where flexibility is required for unknown or compound fields (arrays, nested objects, or just unknown). The popularity of Hadoop for these use-cases led to its adoption, also for structured use-cases. For these cases, SQL query engines have been bolted on Hadoop, and convert relational operations into map/reduce style operations.

In the past, data was small, as were the number of data consumers. Most datasets were relatively simple, coming from a handful of ERP, CRM and other transactional sources. Traditional data warehouses were built to support this type of data. As computing hardware advanced, these databases got faster ‘for free’. However, they have by now become legacy technology incapable of utilizing new parallelized computing paradigms

Data is one of the most critical components of a telecom’s competitive arsenal, and the arrival of 5G will amplify its importance. Opportunities abound for telecoms who can rapidly analyze their massive stores of data. Yet industry estimates show that operators access and analyze only a small fraction of their data. Telecoms leave behind critical, game-changing business insights due to a reliance on legacy systems that cannot support their exponentially growing data.

This whitepaper looks at several leading mobile operators and the methods they use to overcome technological challenges and successfully uncover critical intelligence inside their massive data stores.

- Ad-Hoc 查詢

- 加速 ETL 及資料準備

- 整合或取代暨有 Data Warehouse 或 Hadoop 環境

- 資料庫結構優化