Big data
Big data refers to extremely large and diverse collections of structured, unstructured, and semi-structured data that continues to grow exponentially over time. These datasets are so huge and complex in volume, velocity, and variety, that traditional data management systems cannot store, process, and analyze them.
The amount and availability of data is growing rapidly, spurred on by digital technology advancements, such as connectivity, mobility, the Internet of Things (IoT), and artificial intelligence (AI). As data continues to expand and proliferate, new big data tools are emerging to help companies collect, process, and analyze data at the speed needed to gain the most value from it.
Big data describes large and diverse datasets that are huge in volume and also rapidly grow in size over time. Big data is used in machine learning, predictive modeling, and other advanced analytics to solve business problems and make informed decisions.
Read on to learn the definition of big data, some of the advantages of big data solutions, common big data challenges, and how Google Cloud is helping organizations build their data clouds to get more value from their data.
Developing a solid data strategy starts with understanding what you want to achieve, identifying specific use cases, and the data you currently have available to use. You will also need to evaluate what additional data might be needed to meet your business goals and the new systems or tools you will need to support those.
Unlike traditional data management solutions, big data technologies and tools are made to help you deal with large and complex datasets to extract value from them. Tools for big data can help with the volume of the data collected, the speed at which that data becomes available to an organization for analysis, and the complexity or varieties of that data.
For example, data lakes ingest, process, and store structured, unstructured, and semi-structured data at any scale in its native format. Data lakes act as a foundation to run different types of smart analytics, including visualizations, real-time analytics, and machine learning.
It’s important to keep in mind that when it comes to big data—there is no one-size-fits-all strategy. What works for one company may not be the right approach for your organization’s specific needs.
Here are four key concepts that our Google Cloud customers have taught us about shaping a winning approach to big data:
Open
Today, organizations need the freedom to build what they want using the tools and solutions they want. As data sources continue to grow and new technology innovations become available, the reality of big data is one that contains multiple interfaces, open source technology stacks, and clouds. Big data environments will need to be architected to be both open and adaptable to allow for companies to build the solutions and get the data it needs to win.
Intelligent
Big data requires data capabilities that will allow them to leverage smart analytics and AI and ML technologies to save time and effort delivering insights that improve business decisions and managing your overall big data infrastructure. For example, you should consider automating processes or enabling self-service analytics so that people can work with data on their own, with minimal support from other teams.
Flexible
Big data analytics need to support innovation, not hinder it. This requires building a data foundation that will offer on-demand access to compute and storage resources and unify data so that it can be easily discovered and accessed. It’s also important to be able to choose technologies and solutions that can be easily combined and used in tandem to create the perfect data toolsets that fit the workload and use case.
Trusted
For big data to be useful, it must be trusted. That means it’s imperative to build trust into your data—trust that it’s accurate, relevant, and protected. No matter where data comes from, it should be secure by default and your strategy will also need to consider what security capabilities will be necessary to ensure compliance, redundancy, and reliability
The amount and availability of data is growing rapidly, spurred on by digital technology advancements, such as connectivity, mobility, the Internet of Things (IoT), and artificial intelligence (AI). As data continues to expand and proliferate, new big data tools are emerging to help companies collect, process, and analyze data at the speed needed to gain the most value from it.
Big data describes large and diverse datasets that are huge in volume and also rapidly grow in size over time. Big data is used in machine learning, predictive modeling, and other advanced analytics to solve business problems and make informed decisions.
Read on to learn the definition of big data, some of the advantages of big data solutions, common big data challenges, and how Google Cloud is helping organizations build their data clouds to get more value from their data.
Developing a solid data strategy starts with understanding what you want to achieve, identifying specific use cases, and the data you currently have available to use. You will also need to evaluate what additional data might be needed to meet your business goals and the new systems or tools you will need to support those.
Unlike traditional data management solutions, big data technologies and tools are made to help you deal with large and complex datasets to extract value from them. Tools for big data can help with the volume of the data collected, the speed at which that data becomes available to an organization for analysis, and the complexity or varieties of that data.
For example, data lakes ingest, process, and store structured, unstructured, and semi-structured data at any scale in its native format. Data lakes act as a foundation to run different types of smart analytics, including visualizations, real-time analytics, and machine learning.
It’s important to keep in mind that when it comes to big data—there is no one-size-fits-all strategy. What works for one company may not be the right approach for your organization’s specific needs.
Here are four key concepts that our Google Cloud customers have taught us about shaping a winning approach to big data:
Open
Today, organizations need the freedom to build what they want using the tools and solutions they want. As data sources continue to grow and new technology innovations become available, the reality of big data is one that contains multiple interfaces, open source technology stacks, and clouds. Big data environments will need to be architected to be both open and adaptable to allow for companies to build the solutions and get the data it needs to win.
Intelligent
Big data requires data capabilities that will allow them to leverage smart analytics and AI and ML technologies to save time and effort delivering insights that improve business decisions and managing your overall big data infrastructure. For example, you should consider automating processes or enabling self-service analytics so that people can work with data on their own, with minimal support from other teams.
Flexible
Big data analytics need to support innovation, not hinder it. This requires building a data foundation that will offer on-demand access to compute and storage resources and unify data so that it can be easily discovered and accessed. It’s also important to be able to choose technologies and solutions that can be easily combined and used in tandem to create the perfect data toolsets that fit the workload and use case.
Trusted
For big data to be useful, it must be trusted. That means it’s imperative to build trust into your data—trust that it’s accurate, relevant, and protected. No matter where data comes from, it should be secure by default and your strategy will also need to consider what security capabilities will be necessary to ensure compliance, redundancy, and reliability
blockchain 1
A blockchainn is a distributed database or ledger shared among a computer network's nodes. They are best known for their crucial role in cryptocurrency systems for maintaining a secure and decentralized record of transactions, but they are not limited to cryptocurrency uses. Blockchains can be used to make data in any industry immutable—the term used to describe the inability to be altered.
Because there is no way to change a block, the only trust needed is at the point where a user or program enters data. This aspect reduces the need for trusted third parties, which are usually auditors or other humans that add costs and make mistakes.
Since Bitcoin's introduction in 2009, blockchain uses have exploded via the creation of various cryptocurrencies, decentralized finance (DeFi) applications, non-fungible tokens (NFTs), and smart contracts.
Because there is no way to change a block, the only trust needed is at the point where a user or program enters data. This aspect reduces the need for trusted third parties, which are usually auditors or other humans that add costs and make mistakes.
Since Bitcoin's introduction in 2009, blockchain uses have exploded via the creation of various cryptocurrencies, decentralized finance (DeFi) applications, non-fungible tokens (NFTs), and smart contracts.
fog computing 2
Fog Computing is the term introduced by Cisco that refers to extending cloud computing to an edge of the enterprise’s network. Thus, it is also known as Edge Computing or Fogging. It facilitates the operation of computing, storage, and networking services between end devices and computing data centers.
The term fog computing was coined by Cisco in January 2014. This was because fog is referred to as clouds that are close to the ground in the same way fog computing was related to the nodes which are present near the nodes somewhere in between the host and the cloud. It was intended to bring the computational capabilities of the system close to the host machine. After this gained a little popularity, IBM, in 2015, coined a similar term called “Edge Computing”.
Finally, fog computing delivers cloud capabilities to the edge of networks, increasing efficiency, lowering latency, and improving data processing capabilities. It is perfect for real-time data analysis, low-latency applications such as IoT, and situations where data privacy and security are critical. While it provides scalability and lower bandwidth usage, it also has issues in managing data congestion and increasing power consumption. Fog computing is making progress in applications such as healthcare monitoring, industrial IoT, and real-time analytics across a variety of industries.
The term fog computing was coined by Cisco in January 2014. This was because fog is referred to as clouds that are close to the ground in the same way fog computing was related to the nodes which are present near the nodes somewhere in between the host and the cloud. It was intended to bring the computational capabilities of the system close to the host machine. After this gained a little popularity, IBM, in 2015, coined a similar term called “Edge Computing”.
Finally, fog computing delivers cloud capabilities to the edge of networks, increasing efficiency, lowering latency, and improving data processing capabilities. It is perfect for real-time data analysis, low-latency applications such as IoT, and situations where data privacy and security are critical. While it provides scalability and lower bandwidth usage, it also has issues in managing data congestion and increasing power consumption. Fog computing is making progress in applications such as healthcare monitoring, industrial IoT, and real-time analytics across a variety of industries.
