Jesse Anderson
DECEMBER 12, 2022
Google looked over the expanse of the growing internet and realized they’d need scalable systems. Doug Cutting took those papers and created Apache Hadoop in 2005. They were the first companies to commercialize open source big data technologies and pushed the marketing and commercialization of Hadoop.
Knowledge Hut
MAY 2, 2024
To store and process even only a fraction of this amount of data, we need Big Data frameworks as traditional Databases would not be able to store so much data nor traditional processing systems would be able to process this data quickly. Compatibility MapReduce is also compatible with all data sources and file formats Hadoop supports.
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Monte Carlo
MAY 14, 2024
Let’s revisit how several of those key table formats have emerged and developed over time: Apache Avro : Developed as part of the Hadoop project and released in 2009, Apache Avro provides efficient data serialization with a schema-based structure.
ProjectPro
FEBRUARY 10, 2016
It is difficult to believe that the first Hadoop cluster was put into production at Yahoo, 10 years ago, on January 28 th , 2006. Ten years ago nobody was aware that an open source technology, like Apache Hadoop will fire a revolution in the world of big data. Happy Birthday Hadoop With more than 1.7
Data Science Blog: Data Engineering
JULY 4, 2022
Apache Spark began as a research project at UC Berkeley’s AMPLab, a student, researcher, and faculty collaboration centered on data-intensive application domains, in 2009. Spark outperforms Hadoop in many ways, reaching performance levels that are nearly 100 times higher in some cases.
ProjectPro
MARCH 23, 2015
In one of our previous articles we had discussed about Hadoop 2.0 YARN framework and how the responsibility of managing the Hadoop cluster is shifting from MapReduce towards YARN. In one of our previous articles we had discussed about Hadoop 2.0 Here we will highlight the feature - high availability in Hadoop 2.0
Knowledge Hut
JANUARY 18, 2024
Data science is the application of scientific methods, processes, algorithms, and systems to analyze and interpret data in various forms. The role requires extensive knowledge of data science languages like Python or R and tools like Hadoop, Spark, or SAS. It came out in 2009 when Google introduced it to the world.
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