The volume of data that organizations collect is massive, and it is continuously accelerating; by 2025, IDC predicts that 175 zettabytes of data will be created globally each day. That’s about 21,000 GB per person on Earth, and represents a CAGR Of 61%.
These staggering numbers mean that processing efficiency will increasingly become a major competitive differentiator. Companies that optimize their DataOps will be able to process data more quickly-allowing them to get more value in less time from the data they collect-but also more cost-effectively, since every data manipulation translates into compute cycles that come with a cost.
Snowflake and Apache Spark are two popular data processing engines that take different approaches to managing DataOps efficiency. In this technical whitepaper we explore how the capabilities of the two platforms differ, and report on a series of benchmark experiments to compare the efficiency of Snowflake versus Spark using Spectra by Fosfor, our DataOps platform.
Download your free copy now to learn more.
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Artificial intelligence (AI) and machine learning (ML) technologies are expanding rapidly as organizations seek to capitalize on the value of their data. Half of the companies surveyed in a 2020 Mckinsey study have already adopted AI in at least one business function.
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