Recently, companies have begun to understand the value of Big Data and have begun to invest in Data Science professionals. In this article, we will introduce you to the topic of Big Data, tell you where and how big data is being used now.
What is Big Data?
Big data - this is structured and unstructured data of huge volumes and diversity, as well as methods for their processing, which allow distributed information analysis.
The term Big Data appeared in 2008. It was first used by the editor of Nature magazine, Clifford Lynch. He spoke about the explosive growth of world information and noted that new tools and more advanced technologies will help to master them.
In simple words, big data is a common name for large data sets and methods of processing them. Such data is efficiently processed using scalable software tools that appeared in the late 2000s and have become an alternative to traditional databases and Business Intelligence solutions.
Big data analysis is carried out in order to obtain new, previously unknown information. Such discoveries are called insights, which means insight, conjecture, sudden understanding.
The term Big Data appeared in 2008. It was first used by the editor of Nature magazine, Clifford Lynch. He spoke about the explosive growth of world information and noted that new tools and more advanced technologies will help to master them.
In simple words, big data is a common name for large data sets and methods of processing them. Such data is efficiently processed using scalable software tools that appeared in the late 2000s and have become an alternative to traditional databases and Business Intelligence solutions.
Big data analysis is carried out in order to obtain new, previously unknown information. Such discoveries are called insights, which means insight, conjecture, sudden understanding.
In other words, Big Data is a term used to describe large data sets that grow rapidly over time, as well as tools for working with them. This is a way to collect and process a lot of information to solve complex application problems.
How is the data generated?
Big data comes from a wide variety of sources. An obvious example is social and ad networks. If you are not a giant company that provides services to millions of people, do not despair - you can still work with big data.
They can be collected, for example, using web scraping. Many services also provide APIs for accessing their data. Most likely, you will not be given 100% of the available and incoming data, but this is also a good option.
An example is the Streaming API Facebook which gives access to new content with the specified keywords. By default, only 1% of all data is available, but you can try to request all 100%.
How is data stored and processed?
Data volumes are growing rapidly, and in order to process them, distributed repositories and programs are used. As the number of data increases, you can simply add new nodes, rather than rewrite the current solution again. The article below will provide information on the tools that are used to work with Big Data.
The issue of secure data storage is important. Due to the active development of big data and the lack of established methodologies for their protection, each company must decide for itself how to approach this issue.
A reasonable step would be to remove confidential data from the cluster, such as passwords and bank card data, this will simplify the configuration of access to it.
Then you can apply various administrative, physical, and technical security measures, the requirements for which can be found in different collections of standards like ISO 27001.
For example, you can restrict employee's access to data to a level that is sufficient to carry out their work tasks. It will not be superfluous to keep logs of employee interactions with data and exclude the possibility of copying data from the repository. You can also use data anonymization.
Various structured data (such as equipment year, brand, model, etc.) and unstructured data (including millions of log entries, sensor data, error messages, and engine temperature) often hide predictable machinery Failure information.
By analyzing these data, companies can identify potential problems before an accident occurs, thereby arranging maintenance activities more cost-effectively and maximizing the uptime of parts and equipment.
The issue of secure data storage is important. Due to the active development of big data and the lack of established methodologies for their protection, each company must decide for itself how to approach this issue.
A reasonable step would be to remove confidential data from the cluster, such as passwords and bank card data, this will simplify the configuration of access to it.
Then you can apply various administrative, physical, and technical security measures, the requirements for which can be found in different collections of standards like ISO 27001.
For example, you can restrict employee's access to data to a level that is sufficient to carry out their work tasks. It will not be superfluous to keep logs of employee interactions with data and exclude the possibility of copying data from the repository. You can also use data anonymization.
3V Characteristic of big data
Volume
The "big" of big data is first reflected in the amount of data. In the field of big data, you need to process massive amounts of low-density unstructured data. The value of the data may be unknown, such as Twitter data streams, web or mobile application click streams, and data captured by device sensors. In practical applications, the amount of big data is usually as high as tens of terabytes, or even hundreds of petabytes.
High speed (velocity)
The "high speed" of big data refers to the high-speed reception and processing of data-data usually flows directly into memory instead of being written to disk. In practical applications, some networked smart products need to run in real-time or near real-time, requiring real-time evaluation and operation based on data, and big data can only meet these requirements with "high speed" characteristics.
Variety (Variety)
Diversification refers to the many types of data available. Generally speaking, traditional data is structured data that can be neatly incorporated into relational databases. With the rise of big data, various new unstructured data types are emerging, such as text, audio, and video. They require additional preprocessing operations to truly provide insight and supporting metadata.
The value and authenticity of Big data
In the past few years, the definition of big data has added two new value (Value) and authenticity (Veracity).
First of all, data certainly contains a value, but if its value is not mined by appropriate methods, the data is useless. Second, only real and reliable data makes sense.
Today, big data has become capital, and all major technology companies around the world are based on the working principle of big data. Through continuous analysis of data in various big data use cases to improve operational efficiency and promote new product development, most of the value they create is all From the data they have.
At present, many cutting-edge technological breakthroughs have caused data storage and computing costs to decrease exponentially. Compared with the past, enterprises can store more data more easily with lower economic investment, and with a large amount of data that is economical and easy to access, you can easily make more accurate and precise business decisions.
However, from the perspective of how big data works, big data value mining is a complete exploration process rather than just data analysis. It requires insightful analysts, business users, and managers to be targeted in big data use cases Raise effective questions, identify data patterns, put forward reasonable assumptions and accurately conduct behavior prediction.
Big data history
Although the concept of big data was only recently proposed, the origin of large data sets can be traced back to the 1960s and 1970s. At that time, the data world was in its infancy, and the world's first batch of data centers and the first relational database appeared in that era.
Around 2005, people began to realize that users generated huge amounts of data when using Facebook, YouTube, and other online services. In the same year, Hadoop, an open-source framework developed specifically for storing and analyzing large data sets, came out, and NoSQL began to slowly spread in the same period.
The advent of open-source frameworks such as Hadoop and Spark is of great significance to the development of big data. It is they that reduce the cost of data storage and make big data easier to use. In the following years, the amount of big data has further exploded. Today, "users" around the world-not only people, but also machines-continue to generate massive amounts of data.
With the rise of the Internet of Things (IoT), more and more devices are now connected to the Internet. They collect a large amount of customer usage patterns and product performance data, and the emergence of machine learning has further accelerated the growth of data volume.
However, although it has been around for a long time, people's use of big data has just begun. Today, cloud computing further unleashes the potential of big data. By providing true elasticity/scalability, it allows developers to easily launch Ad Hoc clusters to test subsets of data.
Advantages of big data and data analysis:
- Big data means more information and can provide you with more comprehensive insights.
- More comprehensive insights mean higher reliability and help you develop new solutions.
Big data use cases
Product development
In today's era, companies such as Netflix and Procter & Gamble generally use big data to predict customer demand.
They classify key attributes of past and current products or services and model the relationship between those attributes and successful commercial products to build predictive models for new products and services.
In addition, P & G plans produce and release new products based on data and analysis results from focus groups, social media, test market, and pre-delivery.
Predictive maintenance
By analyzing these data, companies can identify potential problems before an accident occurs, thereby arranging maintenance activities more cost-effectively and maximizing the uptime of parts and equipment.
Customer Experience
The core of today's market competition is to win customers. Compared to the past, companies now have more conditions to clearly understand the customer experience. In this regard, big data allows you to collect data through social media, website visits, call records, and other sources to improve customer interaction, provide customers with personalized products, reduce customer churn rates, proactively solve problems, and ultimately create more Multi-value.
Fraud prevention and compliance
Today, your system is facing more threats than just a few malicious hackers, as well as a well-equipped team of experts.
At the same time, the security situation and compliance requirements are constantly changing, which poses many challenges. With big data, you can identify signs of fraud by identifying data patterns, aggregate massive amounts of information, and accelerate the generation of regulatory reports.
Machine learning
Machine learning is a hot topic today, and data (especially big data) is an important driving factor behind this phenomenon.
By using big data to train machine learning models, we can "train" machines to have specific capabilities without having to write programs for them. It is the big data available for training machine learning models that have contributed to this transformation.
Operational frequency
Operational efficiency is usually not a hot topic, but big data has the most profound impact in this area. With the help of big data, you can deeply analyze and evaluate production, customer feedback, return rates, and more, to reduce out-of-stock phenomena, predict future demand, and use big data to improve decision-making based on current market demand.
Drive innovation
Big data helps you study the interrelationships between people, organizations, entities, and processes, and then drive innovation in new ways based on deep insights. With the help of big data, you can effectively improve financial and corporate planning decisions, verify trends and customer needs, better provide customers with new products and services, and implement dynamic pricing to maximize revenue. In short, big data will open the door to an innovative world and bring you endless possibilities.
Big data challenges
Big data contains an endless potential, but also brings many challenges.
First, big data is huge. Although many new technologies have been developed for data storage, the amount of data is doubling every two years. At present, various enterprises are struggling to cope with the rapid growth of data and constantly looking for more efficient data storage methods.
Second, storing data alone is not enough. The value of data lies in its use, which in turn depends on data management. At present, we need to do a lot of work to get clean data, which is closely related to and to facilitate analysis of the way to organize the data with customers, such as data scientist before you actually start using the data, typically spend 50% to 80% of the time to manage and prepare data.
Finally, the update speed of big data technology is very fast. A few years ago, Apache Hadoop was the most popular big data processing technology. In 2014, Apache Spark came out. Today, only combining these two frameworks can create the best solution. In short, keeping up with the development of big data technology is a persistent challenge.
First, big data is huge. Although many new technologies have been developed for data storage, the amount of data is doubling every two years. At present, various enterprises are struggling to cope with the rapid growth of data and constantly looking for more efficient data storage methods.
Second, storing data alone is not enough. The value of data lies in its use, which in turn depends on data management. At present, we need to do a lot of work to get clean data, which is closely related to and to facilitate analysis of the way to organize the data with customers, such as data scientist before you actually start using the data, typically spend 50% to 80% of the time to manage and prepare data.
Finally, the update speed of big data technology is very fast. A few years ago, Apache Hadoop was the most popular big data processing technology. In 2014, Apache Spark came out. Today, only combining these two frameworks can create the best solution. In short, keeping up with the development of big data technology is a persistent challenge.
How big data works?
Big data can provide you with new insights and bring new business opportunities and business models. So how does big data work?
1. Big data integration
Big data first need to bring together data from different sources and applications, and traditional data integration mechanisms such as ETL (extract, transform, and load) are usually not competent for this task. In other words, we need new strategies and techniques to analyze TB or even PB-level large data sets.
During integration, you need to import and process data, perform formatting operations, and organize data in a form that meets the requirements of business analysts.
During integration, you need to import and process data, perform formatting operations, and organize data in a form that meets the requirements of business analysts.
2. Big data management
Big data needs to be stored properly. Storage solutions can be deployed locally or in the cloud. Second, you can store data in any form, set processing requirements for the data set as needed, and introduce the necessary processing engines.
At present, many customers have to choose storage solutions based on the current location of the data. In this regard, the cloud solution can not only meet the current computing needs of customers but also support users to quickly access all data on demand, which is more and more popular.
3. Big data analysis
Only by truly analyzing the data and taking effective actions based on data insights will your big data investment pay off. You can: visually analyze various data sets to gain a new understanding; further, explore the data to gain new insights; share your insights with others; combine machine learning and artificial intelligence to build data models; act immediately to unlock the value of your data.
What technology is big data associated with?
The technologies used to work with big data can be divided into three large groups: for data analysis (A / B testing, hypothesis testing, machine learning), for data collection and storage (“clouds”, databases) and for presentation results (tables, graphs, and so on). Here are some examples of them.
Data analysis
- Apache Spark. An open-source framework for implementing distributed data processing, part of the Hadoop ecosystem.
- Elasticsearch A popular open search engine, often used when working with big data.
- Scikit-learn. A free machine learning library for the Python programming language.
Collection and storage
- Apache Hadoop. A framework that cannot be ignored when talking about Big Data. It allows you to ensure the operation of distributed programs on clusters of hundreds and thousands of nodes.
- Apache Ranger. Hadoop data security framework.
- NoSQL databases. HBase, Apache Cassandra and other databases designed to create highly scalable and reliable storage of huge data arrays.
- Data lakes. Unstructured storage for a large amount of "raw" data, not subject to any changes before storing.
- In-memory database. For example, in Redis, data is stored in RAM.
Visualization
- Google chart Multifunctional toolkit for data visualization.
- Tableau. An interactive analytics system that allows you to quickly analyze large amounts of information.
Who works with Big Data?
There are mainly two types of employees working with big data:
Engineer (Data Engineer) - builds systems for the collection and processing of data, and also turns the collected analytics into a ready-made service or product.
Analyst (Data Scientist) - analyzes and searches for patterns in the data.
Engineer (Data Engineer) - builds systems for the collection and processing of data, and also turns the collected analytics into a ready-made service or product.
Analyst (Data Scientist) - analyzes and searches for patterns in the data.
Big data Good practice
To help you successfully start your big data journey, we have summarized some important good practices from various big data use cases based on how big data works. These principles help to lay the foundation for successful big data.
Coordinate big data with specific business goals
A more comprehensive data set can help you gain new insights. To do this, you first need to invest in new skills, organizations, and infrastructure to ensure that the project continues to receive investment and funding in a business-driven environment.
Second, to ensure proper implementation, please evaluate whether your big data can truly support and promote your critical business and IT work, including understanding how to filter web logs to reveal e-commerce behavior; insight into customer sentiment through social media and customer support interaction; Understand statistical correlation methods and their significance for the customer, product, manufacturing, and engineering data.
Alleviate skills shortage through standardization and effective governance
An important obstacle for enterprises to implement big data is lack of skills.
First, you can mitigate this risk by adding big data technology, big data considerations, and decisions to your IT governance plan. Second, standardization helps to better manage costs and make full use of resources.
Third, in order to successfully implement big data strategies and solutions, please evaluate the needs of big data skills early and regularly, and proactively identify potential skills deficiencies. Fourth, you need to train/cross-train existing staff, recruit new staff, and seek support from consulting companies when necessary.
Optimize knowledge transfer through the Center of Excellence
By setting up a center of excellence to share knowledge, control supervision, and manage project communication, regardless of whether the big data project is a new investment or an expanded investment, you can share all software and hardware costs throughout the enterprise to provide a more structured and systematic.
This method expands the functions of big data and improves the maturity of the overall information architecture.
Maximize returns by harmonizing structured and unstructured data
Big data analysis can bring value, but by combining low-density big data with the structured data you currently use, you can gain more meaningful insights.
In practical applications, whether it is capturing customer, product, equipment or environmental big data, your goal is to add more relevant data points to the core master data and analysis summary to draw more accurate conclusions.
For example, compared with the opinion of all customers, only the opinion of high-quality customers is more refined and more targeted. Therefore, many people see big data as an important extension of their existing business intelligence functions, data warehousing platforms, and information architecture.
In this regard, big data can build analysis processes and models based on both people and machines. Using analytical models and big data analysis capabilities (including statistics, spatial analysis, semantics, interactive exploration, and visualization), you can correlate data of different types and sources to derive meaningful insights. Using analytical models, you can correlate data of different types and sources and derive meaningful insights.
In practical applications, whether it is capturing customer, product, equipment or environmental big data, your goal is to add more relevant data points to the core master data and analysis summary to draw more accurate conclusions.
For example, compared with the opinion of all customers, only the opinion of high-quality customers is more refined and more targeted. Therefore, many people see big data as an important extension of their existing business intelligence functions, data warehousing platforms, and information architecture.
In this regard, big data can build analysis processes and models based on both people and machines. Using analytical models and big data analysis capabilities (including statistics, spatial analysis, semantics, interactive exploration, and visualization), you can correlate data of different types and sources to derive meaningful insights. Using analytical models, you can correlate data of different types and sources and derive meaningful insights.
Create an efficient exploration laboratory
Exploring the value of data is by no means a smooth road. Sometimes we do n’t even know where we are going, which is what we expected.
Nonetheless, the management team and IT department still need to provide due support for this "indifferent purpose" or "lack of clear demand" exploration activities.
At the same time, analysts and data scientists also need to work closely with business departments to determine what key business knowledge they need and what knowledge gaps exist during the cooperation process.
Finally, in order to implement interactive data exploration and statistical algorithm experiments, you need an efficient work area, need to provide support and proper supervision for the sandbox environment.
Nonetheless, the management team and IT department still need to provide due support for this "indifferent purpose" or "lack of clear demand" exploration activities.
At the same time, analysts and data scientists also need to work closely with business departments to determine what key business knowledge they need and what knowledge gaps exist during the cooperation process.
Finally, in order to implement interactive data exploration and statistical algorithm experiments, you need an efficient work area, need to provide support and proper supervision for the sandbox environment.
Consistent with the cloud operation model
Big data processes and users need to access various resources for iterative testing and production work.
In this regard, the big data solution should cover all data areas, including transactions, master data, reference data, and summary data. Supports you to create analysis sandboxes on demand.
At the same time, resource management is essential for the control of the entire data flow (including pre-processing and post-processing, integration, summary, and analysis modeling in the database), and properly planned private and public cloud supply and security strategies are necessary to meet these evolving changes. The demand is also very important.