Jesse Anderson

DECEMBER 12, 2022

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. Hadoop was hard to program, and Apache Hive came along in 2010 to add SQL. We lacked a scalable pub/sub system.

Data Engineering 130

Data Engineering Data Engineer Engineering Hadoop 130

Ascend.io

FEBRUARY 15, 2023

Back in 2004, I got to work with MapReduce at Google years before Apache Hadoop was even released, using it on a nearly daily basis to analyze user activity on web search and analyze the efficacy of user experiments. So in this piece, I’ll give my take on the evolution of the cloud data platform, starting way back from my days at Google.

Cloud 52

Cloud Amazon Web Services Hadoop Telecommunication 52

Ascend.io

FEBRUARY 15, 2023

Back in 2004, I got to work with MapReduce at Google years before Apache Hadoop was even released, using it on a nearly daily basis to analyze user activity on web search and analyze the efficacy of user experiments. So in this piece, I’ll give my take on the evolution of the cloud data platform, starting way back from my days at Google.

Cloud 52

Cloud Amazon Web Services Hadoop Telecommunication 52

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

Hadoop 40

Hadoop Big Data Programming SQL 40

ProjectPro

JULY 21, 2021

As a big data architect or a big data developer, when working with Microservices-based systems, you might often end up in a dilemma whether to use Apache Kafka or RabbitMQ for messaging. Rabbit MQ vs. Kafka - Which one is a better message broker? Table of Contents Kafka vs. RabbitMQ - An Overview What is RabbitMQ?

Kafka 52

Kafka Big Data Java Architecture 52

Rockset

JANUARY 23, 2020

While traditional RDBMS databases served well the data storage and data processing needs of the enterprise world from their commercial inception in the late 1970s until the dotcom era, the large amounts of data processed by the new applications—and the speed at which this data needs to be processed—required a new approach.

Database 40

Database MySQL Kafka NoSQL 40