The Morning Paper
DECEMBER 13, 2020
An overview of end-to-end entity resolution for big data , Christophides et al., It’s an important part of many modern data workflows, and an area I’ve been wrestling with in one of my own projects. Therefore we only have to do more detailed comparisons within blocks, but not across blocks. “ ACM Computing Surveys, Dec.
The Morning Paper
SEPTEMBER 8, 2019
Experiences with approximating queries in Microsoft’s production big-data clusters Kandula et al., I’ve been excited about the potential for approximate query processing in analytic clusters for some time, and this paper describes its use at scale in production. VLDB’19. Approximate query support.
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Dynatrace
AUGUST 14, 2023
As user experiences become increasingly important to bottom-line growth, organizations are turning to behavior analytics tools to understand the user experience across their digital properties. In doing so, organizations are maximizing the strategic value of their customer data and gaining a competitive advantage.
Highly Scalable
JUNE 5, 2012
At GridDynamics, we recently worked on a custom database for realtime web analytics where fast intersection of very large lists of IDs was a must for good performance. In this example, we need 5 comparisons to process two lists of 12 elements each. return count; }.
Scalegrid
MARCH 28, 2024
In this comparison of Redis vs Memcached, we strip away the complexity, focusing on each in-memory data store’s performance, scalability, and unique features. Performance Comparison: Redis vs Memcached Although Redis and Memcached are high-performance in-memory data stores, their performance characteristics are distinct.
Highly Scalable
MAY 1, 2012
Statistical analysis and mining of huge multi-terabyte data sets is a common task nowadays, especially in the areas like web analytics and Internet advertising. Analysis of such large data sets often requires powerful distributed data stores like Hadoop and heavy data processing with techniques like MapReduce.
Smashing Magazine
JULY 29, 2019
These countries generally have a combination of poor technical infrastructure and low adoption, meaning data is both costly to deliver and doesn’t have the economy of scale to drive costs down. Data is expensive in parts of Europe too. A gigabyte of data in Greece will set you back $32.71; in Switzerland, $20.22. in the USA.
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