Good-bye Big Data. Hello, Massive Data!
Join the Massive Data Revolution with SQream. Shorten query times from days to hours or minutes, and speed up data preparation with - analyze the raw data directly.
Big Data used to be a big buzzword a few years ago, but it is no longer a useful term to describe the currently technology challenges. SQream, an exciting startup, is offering a new approach which can shorten query times from days to hours or minutes and enables data scientists to analyze the raw data directly. Join the Massive Data Revolution - learn more here.
Sponsored Post.
Data is growing exponentially. This growth is driven by government and enterprise led digitalization, and the onslaught of IoT connected devices. Estimates put the total data load for this year at 35 zettabytes, growing to 175 zettabytes by 2025. When the first "big data" started hitting the market over a decade ago, organizations created data lakes using Hadoop, or they piled on server after server of legacy data warehouse technology and storage. This may have solved the initial issue of bringing the data into the organization, but most organizations were challenged with accessing, managing, and analyzing these big data stores.
Fast forward just a few years, and "big data" has been left in the dust with the emergence of the Massive Data Era.
Enterprises who only recently were struggling to tackle terabytes of data, are now faced with hundreds of terabytes and petabytes. So the situation as it exists hasn't changed much — analytic queries take forever, can only be executed on a subset of data with few dimensions, leaving behind critical business insights that could be the difference between propelling the company forward or leaving it well behind the competition.
Massive Data Era requires new ways of thinking about data management and analytics. Analysts and data scientists should not have to put up with long data preparation cycles and query development, which makes their data practically irrelevant by the time it reaches the business stakeholders.
The first step in addressing the challenge of massive data lies in understanding that it is so much more than big data. Continuing to throw resources at legacy technology and systems to access and analyze this data is not feasible for an organization that wants to grow and stay ahead of the competition.
Enterprises should embrace innovative GPU-based data analytics acceleration platforms built specifically for analyzing massive data. They give organizations powerful parallel processing capabilities of thousands of cores per processor. They can ingest, process, and analyze significantly more data, much more rapidly on more dimensions, and with the support of multiple applications and frameworks. And most importantly, they can easily scale as their data grows, at a fraction of the cost. The Massive Data Revolution is upon us. Organizations who embrace the revolution will come out as the winners as they become truly data driven, benefiting from their most important asset - their data. If you'd like learn to how you can take control of your growing data stores, join the Massive Data Revolution today.
Sponsored Post.
Data is growing exponentially. This growth is driven by government and enterprise led digitalization, and the onslaught of IoT connected devices. Estimates put the total data load for this year at 35 zettabytes, growing to 175 zettabytes by 2025. When the first "big data" started hitting the market over a decade ago, organizations created data lakes using Hadoop, or they piled on server after server of legacy data warehouse technology and storage. This may have solved the initial issue of bringing the data into the organization, but most organizations were challenged with accessing, managing, and analyzing these big data stores.
Fast forward just a few years, and "big data" has been left in the dust with the emergence of the Massive Data Era.
Enterprises who only recently were struggling to tackle terabytes of data, are now faced with hundreds of terabytes and petabytes. So the situation as it exists hasn't changed much — analytic queries take forever, can only be executed on a subset of data with few dimensions, leaving behind critical business insights that could be the difference between propelling the company forward or leaving it well behind the competition.
Massive Data Era requires new ways of thinking about data management and analytics. Analysts and data scientists should not have to put up with long data preparation cycles and query development, which makes their data practically irrelevant by the time it reaches the business stakeholders.
The first step in addressing the challenge of massive data lies in understanding that it is so much more than big data. Continuing to throw resources at legacy technology and systems to access and analyze this data is not feasible for an organization that wants to grow and stay ahead of the competition.
Enterprises should embrace innovative GPU-based data analytics acceleration platforms built specifically for analyzing massive data. They give organizations powerful parallel processing capabilities of thousands of cores per processor. They can ingest, process, and analyze significantly more data, much more rapidly on more dimensions, and with the support of multiple applications and frameworks. And most importantly, they can easily scale as their data grows, at a fraction of the cost. The Massive Data Revolution is upon us. Organizations who embrace the revolution will come out as the winners as they become truly data driven, benefiting from their most important asset - their data. If you'd like learn to how you can take control of your growing data stores, join the Massive Data Revolution today.