What is Big Data? Everything about Big Data (Part 1)
Analyzing much data is one of Big Data’s parts that differs from previous data analysis. Let’s find other angels.
Have data and then big data. So what is the difference?
What is Big Data?
Big Data is data sets that are too large and complicated to be dealt with by traditional processing, which is hard to capture, curate, and manage at a reasonable time.
Those big data includes structured data, unstructured and semi-structured data. Each set can be exploited to reveal insights.
Big Data’s characteristics
The following characteristic can describe big data.
- Volume: The quantity of data.
- Variety: The type and nature of data.
- Velocity: The speed at which data is analyzed and processed.
The definition of Big data with the relevant components allows organizations to put data in practical use and solve some marketing issues, including:
- Necessary IT infrastructure to support big data.
- The analysis applies to the data.
- The technology required for big data projects is related to skill sets.
- And the down-to-earth situations are meaningful with big data.
Big data and analytics
What gives value to big data organizations is data analysis. Without analysis, it is only a data set with limited business use.
By analyzing the big data, big enterprises can make benefits such as increase sales, improve customer service, which is more effective and increases the competition.
Data analysis involves examining datasets to gather insights or draw conclusions about what they contain, such as trends and predictions about future performance.
By analyzing data, organizations can make better marketing decisions, such as when and where to run a campaign or introduce a new product or service.
The analysis can refer to more advanced business applications. Scientific organizations use predictive analysis as an application.
The most advanced data analysis type is data mining, where analysts evaluate large datasets to determine relationships, patterns, and trends.
Data analysis can include exploratory data analysis (EDA) (to identify patterns and relationships in data) and confirmation data analysis (applying statistical techniques to find hypotheses about a data set, correct or not).
Another aspect is quantitative data analysis (or analyzing numerical data with statistically comparable variables) versus analyzing qualitative data (focusing on non-personal data like video, image, and text).
IT infrastructure to support big data
For the definition of big data to work, organizations need the infrastructure to collect and store data, provide access and secure information during storage and forwarding.
The advanced level, including storage systems and hosts, is designed for big data, management software data integration, business information and data analysis software, and big data apps.
Most of the infrastructure will focus on one place because companies want to invest in their data centers. But more and more organizations rely on cloud computing services to handle many of their big data requests.
Data collection requires resources. Many of the following applications, such as web apps, social media channels, mobile apps, and email archives, are pre-installed.
But when IoT becomes more popular, companies may need to deploy sensors on every means of device, vehicles, and products to collect data and new apps generating data. IoT-oriented data analysis has its specialized tools and techniques. To store all incoming data, organizations need to have sufficient storage space in place. Storage options include traditional data warehouses, data lakes, and cloud storage.
Security infrastructure tools can include data encryption, user authentication, other access controls, monitoring systems, firewalls, enterprise mobility management, and other products to protect systems and data.
Superior technologies for Big data
Besides the IT infrastructure used for general data, there are several big data specific technologies that your IT infrastructure should support.
Hadoop is one of the technologies most closely related to big data. Apache Hadoop project develops open-source software for scalable and distributed computers.
The Hadoop software library is a template that enables the distributed processing of large data sets across groups of computers using simple programming models. It is designed to scale from a single server to thousands of others, providing local compute and storage.
The project includes many parts:
- Hadoop Common, popular utilities that support other Hadoop sections
- Hadoop Distributed File System, which provides high application data access
- Hadoop YARN, a template for cluster resource management and work planning
- Hadoop MapReduce, a YARN-based system for parallel processing of large data sets.
Apache Spark is an open-source cluster computing part of the Hadoop economy used as a big data processing engine in Hadoop.
Spark has been one of the necessary templates for big data processing and can be deployed in different ways. It supports Java, Scala, Python (especially is Anaconda Python distro), and programming language R ( R is gradually suitable with big data) and supports SQL, streaming data, machine learning, and grasp processing.
Data lakes vast archive amounts of raw data in its native format until business users need the data.
Factors that help data growth lakes are digital movements and the development of IoT. The data lakes are designed to help users easily access large amounts of data when required.
SQL databases usually are designed for reliable transactions and random inquiries.
But it has limitations such as rigid schemas, making it unsuitable for several apps. NoSQL databases address the constraints and store and manage data to allow for high speed of operation and great flexibility.
Companies design many databases to find a better way to store content or process data for big websites. Unlike SQL databases, many NoSQL databases can be horizontally scaled across hundreds or thousands of servers.
In-memory database (IMDB) is a database management system that relies on main memory (Ram) instead of HDD to store data. In-memory databases are faster than in-disk optimized databases, a key point for using big data analysis and creating data storage and metadata.
Big data skills
Big data and analytics efforts require specific skills, whether from within the organization or through outside experts.
Many skills related to important technological data components such as Hadoop, Spark, NoSQL in-memory database, and analysis software.
With the typical data analysis projects and the lack of human resources of those skills above, the searching experts can be one of the biggest challenges.
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