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May 4-6 at Taipei Nangang Exhibition Center, Hall 2
SQream
Founded in 2010, SQream Technologies has redefined big data analytics with SQream DB – a complementary SQL database harnessing the power of GPU to enable fast, flexible, and cost-efficient analysis of massive datasets of hundreds of terabytes or more.
SQream DB integrates seamlessly into enterprises’ MPP ecosystems, reducing query time from hours to minutes, eliminating bottlenecks, and enabling complex queries that were previously infeasible. SQream’s proprietary technology analyzes raw data directly, enabling data scientists and BI analysts to ask more questions about more data from a variety of perspectives without the need for arduous preparation.
Organizations around the world use SQream DB to drastically improve their workflow, accelerate business intelligence and gain access to a world of never-before-seen insights. Customers include leading companies in telecom, retail, ad-tech, healthcare and other industries across the US, EMEA and APAC.
For more information visit: https://sqream.com/
White Paper
How Telecoms Overcome Big Data Challenges
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 Challenges of Massive Data Analytics 2021 Report
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.
Making The Most Of Your Investment In Hadoop
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.
A look Inside SQream DB
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.
How Telecoms Can Maximize Their Competitive Edge Using 5G Data
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.
Video
Introducing SQream DB - The GPU-accelerated data warehouse for massive data
SQream DB is a fully-featured GPU-accelerated data warehouse, capable of handling the most complex queries. SQream DB has all of the features you expect from a relational database system, like comprehensive ANSI SQL support. Anyone can use SQream DB to load, store, and analyze data up to 100x faster than any other data warehouse.
SQream DB Demo of Clickstream Analysis
Clickstream analysis on hundreds of millions of rows with SQream usingTableau visualizer - Insights in under 2 seconds.
Accelerating Analytics in a New Era of Data - Arnon Shimoni, SQream
Organizations today produce exponentially more data than they did just a few years ago, but their databases weren’t built to handle these new volumes. As a result, reporting takes way too long, and some complex analytics simply cannot be done. The Era of Massive Data is upon us, and a new approach is required to overcome the limitations of traditional CPU-based data stores.
NEW！
SQream
SQream easily ingests and analyzes an organization’s largest datasets. Combining the throughput-oriented GPU with some best-of-breed data techniques, SQream DB scales from terabytes to petabytes with ease. Together with the formidable JOIN, which SQream DB runs entirely and effectively in-GPU from its inception, GPU-acceleration is the backbone of SQream’s massive data revolution.
For more information visit: https://sqream.com/
Product Category
- Security Analytics
