O'Reilly Media - Data
SEPTEMBER 11, 2018
It has become much more feasible to run high-performance data platforms directly inside Kubernetes. That’s great to have because you can use that storage platform to build a data fabric that extends from your on-premises systems into multiple cloud systems to get access to data at a performance level and with an API that you want.
O'Reilly Media - Data
MARCH 8, 2018
Big data is often called one of the most important skill sets in the 21st century, and it’s experiencing enormous demand in the job market. Hiring data scientists and other big data professionals is a major challenge for large enterprises, leading many to shift their efforts to training existing staff. Statistics.
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O'Reilly Media - Data
AUGUST 16, 2018
The deployment of big data tools is being held back by the lack of standards in a number of growth areas. Technologies for streaming, storing, and querying big data have matured to the point where the computer industry can usefully establish standards. The main standard with some applicability to big data is ANSI SQL.
TechCrunch
SEPTEMBER 21, 2021
Farming sustainably and efficiently has gone from a big tractor problem to a big data problem over the last few decades, and startup EarthOptics believes the next frontier of precision agriculture lies deep in the soil. Drive it along the fields and it goes only as deep as it needs to.
CIO
JUNE 14, 2023
Data and big data analytics are the lifeblood of any successful business. Getting the technology right can be challenging but building the right team with the right skills to undertake data initiatives can be even harder — a challenge reflected in the rising demand for big data and analytics skills and certifications.
O'Reilly Media - Data
FEBRUARY 28, 2018
A comparison of the accuracy and performance of Spark-NLP vs. spaCy, and some use case recommendations. In the previous two parts, we walked through the code for training tokenization and part-of-speech models, running them on a benchmark data set, and evaluating the results. Performance. Runtime performance comparison.
O'Reilly Media - Data
APRIL 11, 2018
It’s important to understand the differences between a data engineer and a data scientist. Misunderstanding or not knowing these differences are making teams fail or underperform with big data. I think some of these misconceptions come from the diagrams that are used to describe data scientists and data engineers.
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