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Kafka vs RabbitMQ - A Head-to-Head Comparison for 2025

ProjectPro

Kafka vs. RabbitMQ -Source language Kafka, written in Java and Scala , was first released in 2011 and is an open-source technology, while RabbitMQ was built in Erlang in 2007 Kafka vs. RabbitMQ - Push/Pull - Smart/Dumb Kafka employs a pull mechanism where clients/consumers can pull data from the broker in batches.

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Going from Developer to CEO: Chronosphere

The Pragmatic Engineer

Then, I joined a startup that tried to beat Facebook, and become the #1 social media company in Australia, around 2007. After all, we already  had open source technology to use – M3 – and we also had a clear path on what we wanted to build, similar to the roadmap the observability team at Uber used to have. 

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Data News — Week 24.16

Christophe Blefari

AI News 🤖 When do models get the same hype as 2007 iPhone release? WAP with dbt, Icerberg or Nessie — Julien showcase how you can achieve WAP pattern with different technologies. But don't worry it gonna be out soon. I did not get the memo. Llama the Third is online.

MySQL 130
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Snowflake and the Pursuit Of Precision Medicine

Snowflake

Data cloud technology can accelerate FAIRification of the world’s biomedical patient data. Next-generation sequencing (NGS) technology has dramatically dropped the price of genomic sequencing, from about $1 million in 2007 to $600 today per whole genome sequencing (WGS).

Metadata 117
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10 Real World Data Science Case Studies Projects with Example

ProjectPro

The Walmart Labs team heavily invests in building and managing technologies like cloud, data, DevOps , infrastructure, and security. Get Closer To Your Dream of Becoming a Data Scientist with 70+ Solved End-to-End ML Projects 2) Amazon Amazon is an American multinational technology-based company based in Seattle, USA. million drivers.

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Telecom Network Analytics: Transformation, Innovation, Automation

Cloudera

The Dawn of Telco Big Data: 2007-2012. Advanced predictive analytics technologies were scaling up, and streaming analytics was allowing on-the-fly or data-in-motion analysis that created more options for the data architect. Let’s examine how we got here. First, AI was on the rise, and demanding consistent, large data sets.

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Back to the Financial Regulatory Future

Cloudera

It’s hard to believe it’s been 15 years since the global financial crisis of 2007/2008. Cultural shift and technology adoption: Traditional banks and insurance companies must adapt to the emergence of fintech firms and changing business models.