Big Data - Tim Smith

Summary

Big data, though not a new concept, has transformed over decades, especially at CERN, where scientists have faced challenges in storing and analyzing expanding data. From the early days of mainframe computers to the global internet revolution and the rise of cloud computing, CERN's role in data management has been pivotal. Today, big data impacts many fields, from science to everyday life, helping inform decisions and predict trends. As mobile sensors and networks generate vast amounts of data, new tools and techniques are needed to extract valuable insights that will shape the future.

Takeaways

• 💾 Big data refers to digital information that is too large or complex to store, transport, or analyze using traditional technologies.
• 🏛️ CERN has been managing big data challenges for decades, starting from mainframe computers that occupied entire buildings.
• 🔗 In the 1970s, CERN distributed growing datasets across multiple computers connected by dedicated networks.
• 🌐 The adoption of internet protocols in the late 1980s enabled global remote access to CERN's large datasets.
• 🖥️ The World Wide Web, created in the early 1990s, simplified information sharing without requiring knowledge of where the data was stored.
• 🖱️ By the 2000s, CERN's data exceeded local computing capacity, prompting distribution of data to hundreds of partner institutions.
• 🔄 CERN developed a computing grid to orchestrate global computing resources, relying on trust and mutual exchange.
• ☁️ Cloud computing emerged as a business-friendly alternative for on-demand data analysis beyond scientific communities.
• ⚛️ Particle collisions at CERN generate massive data streams captured by detectors with 150 million sensors, producing up to 14 million events per second.
• 📊 Big data is now widely used across multiple fields, providing real-time, short-term, and predictive insights in areas like traffic, finance, medicine, weather, business, and crime analysis.
• 🛠️ Developing new tools and techniques to mine and analyze big data remains crucial for societal advancement and scientific discovery.
• 🔍 Combining large datasets to find correlations can reveal insights not possible when looking at data in isolation.

Q & A

What is big data, and why is it difficult to handle?

Big data refers to massive volumes of digital information that are challenging to store, transport, and analyze. Its size and complexity often overwhelm existing technologies, making it difficult to manage using traditional methods.

How was CERN involved in the development of big data technologies?

CERN played a critical role in the development of big data technologies by constantly dealing with expanding datasets in particle physics. The institution developed innovative solutions like CERNET, the internet, and grid computing to handle the growing volumes of data.

What was the initial way CERN handled its data in the 1970s?

In the 1970s, CERN's data was stored on a large mainframe computer that filled an entire building. Physicists would travel to CERN to connect to this machine and analyze the data.

What was CERNET, and why was it significant?

CERNET was a network developed to link together multiple independent networks at CERN. It allowed physicists to collaborate globally and access data distributed across different computers, overcoming the limitations of isolated systems.

How did the internet contribute to the growth of big data analysis?

In 1989, CERN adopted the emerging internet standards, which facilitated the global sharing of data. This allowed physicists to access big data remotely, speeding up data analysis and enabling worldwide collaboration.

How did CERN contribute to the creation of the World Wide Web?

In the early 1990s, CERN developed the World Wide Web to allow easy access to information stored at CERN without requiring users to know the data's location. This innovation helped expand the accessibility of big data beyond the scientific community.

What challenge did CERN face in the 2000s regarding data storage?

In the 2000s, CERN's data grew exponentially to petabytes, overwhelming local storage and computing capabilities. CERN had to distribute this data to partner institutions for remote processing and storage.

What is grid computing, and how did CERN use it?

Grid computing is a system that connects distributed computing resources across various institutions to share processing power and storage. CERN used this model to facilitate the global sharing of big data and computational resources for particle physics research.

How does cloud computing differ from grid computing, and why is it significant for big data?

Cloud computing provides on-demand access to computing resources, allowing users to scale up or down as needed. Unlike grid computing, which relies on shared resources within specific communities, cloud computing is more flexible and accessible to a broader range of organizations and industries.

Why is big data now considered relevant beyond the scientific community?

Big data is now crucial in various fields such as business, healthcare, meteorology, and more. By analyzing vast datasets, we can derive valuable insights to inform real-time decisions, predict trends, and improve services across many industries.