Cloudera
JUNE 7, 2022
In the past decade, the amount of structured data created, captured, copied, and consumed globally has grown from less than 1 ZB in 2011 to nearly 14 ZB in 2020. Impressive, but dwarfed by the amount of unstructured data, cloud data, and machine data – another 50 ZB.
Knowledge Hut
DECEMBER 22, 2023
Power BI, originally called Project Crescent, was launched in July 2011, bundled with SQL Server. It was released as a standalone product in July 2015 after adding more features including enterprise-level data connectivity and security options, apart from its original Excel features like Power Query, Power Pivot, and Power View.
ProjectPro
AUGUST 26, 2015
1997 -The term “BIG DATA” was used for the first time- A paper on Visualization published by David Ellsworth and Michael Cox of NASA’s Ames Research Centre mentioned about the challenges in working with large unstructured data sets with the existing computing systems. In 2011, it took only 2 days to generate 1.8
Towards Data Science
FEBRUARY 9, 2024
Data Store Another significant change from 2021 to 2024 lies in the shift from “Data Warehouse” to “Data Store,” acknowledging the expanding database horizon, including the rise of Data Lakes. Stream transformations represent a less mature area in comparison.
ProjectPro
APRIL 14, 2015
Table of Contents Statistics on Online Dating Big Data Analytics in Online Dating How Online Dating Alogirthms work? According to Juniper Research, the market for dating through mobile apps is expected to rise from $1 billion in 2011 to $2.3 billion by 2016. since 2008 and the Canadian dating industry amounts to $153 million.
Monte Carlo
JUNE 14, 2023
5 Data pipeline architecture designs and their evolution The Hadoop era , roughly 2011 to 2017, arguably ushered in big data processing capabilities to mainstream organizations. Data then, and even today for some organizations, was primarily hosted in on-premises databases with non-scalable storage.
ProjectPro
APRIL 2, 2018
Using the company’s iWay DataMigrator, Information Builders customers can pull data into Jethro irrespective of where the data is stored. Then through the IB’s connector, users can index, build dynamic cubes and query for structured as well unstructured data resulting faster response time.
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