Table of contents
1.
Introduction
2.
Evolution of Data Management
3.
Building a successful big data management
4.
Traditional and advanced analytics
4.1.
Analytical data warehouses
4.2.
Big data analytics
4.3.
Reporting and visualization
5.
Big data applications
6.
The big data journey
7.
FAQs
8.
Conclusion
Last Updated: Mar 27, 2024
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Fundamentals of Big Data

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Introduction

Managing and analyzing big data always offers great benefits and challenges to businesses across the globe. In the past, when a single company had a small number of customers who all bought the same product, things were simple. Over time, markets have grown much more complicated and diversified. Therefore, it is essential to learn about the fundamentals of big data.

Evolution of Data Management

The growth of technology has transformed the way we manage and leverage data. Big data allows organizations to gather various insights about the industry. In the past, technology was used to support a specific business need and determine basic things like how many items were sold to how many customers. At present, organizations have more data than ever before. All this data might look like a gold mine, but the major challenges are how one makes sense of the data and recognizes the usage patterns?

Building a successful big data management

  1. Requirements for big data: Before diving into the architecture, it is essential to understand the functional needs for big data. Data must first be captured, then organized and integrated. After this, we can analyze the data based on the problem we address. For example, Amazon might recommend a book based on the customer's past purchase.
  2. Setting the architectural foundation: Besides providing the functionality, it is necessary to improve the performance. To do this, we need the right amount of computational power. Apart from this, we will be storing some data as well. Therefore, we need to have the right amount of redundant memory. A big data management architecture should include various services that enable businesses to use data sources in a fast and efficient manner. Apart from this, the data architecture should be able to integrate with the organization’s supporting infrastructure.
  3. Performance matters: Performance also determines the kind of database one would use. Using a suitable database improves the performance. For example, a graph database is typically used for scientific and technical applications. Other common approaches include columnar databases that store the information efficiently in columns instead of rows.

Traditional and advanced analytics

There are many different approaches to analyzing the data based on the problem that we are addressing. Some analytical techniques use a traditional data warehouse, while others use advanced predictive modelling.

Analytical data warehouses

After sorting through the large amounts of data, a subset of data is taken that reveals patterns and is put in the form available to the business. These warehouses provide multilevel partitioning and parallel processing architectures.

Big data analytics

The capability of companies to manage and analyze large amounts of data enables them to deal with clusters of information that could impact business decisions. Analytical engines are required to manage this highly distributed data and provide insights that can solve a business problem. However, these analytics can get quite complex. 

Reporting and visualization

Reports help in understanding what the analysis of the given data tells. It helps to project the growth and monthly sales figures. Big data changes how we manage and use data. With big data, data visualization and reporting help understand how data is related and the impact of these relations on the future.

Big data applications

Traditionally, businesses used the data to answer what to do and when to do it. With the arrival of big data, this is now changing. Now enterprises focus on developing applications that specifically take advantage of the unique characteristics of big data.

Some examples are in the areas like healthcare, management, etc. These areas rely on huge volumes and varieties of data to monitor and transform the behaviour of the market. In manufacturing, a big data application might help to prevent a machine from shutting down during a production run. A big data traffic management application can be used to reduce the number of traffic jams on busy highways.

The big data journey

Businesses always had to deal with lots of data in various forms. Big data brings a change in what we can do with that information. If we have the right technology, we can use the information to analyze and solve business problems and create new opportunities. We can analyze patterns to change how we manage things, prevent failures and perform experiments. It helps to improve customer satisfaction, product quality, etc. 

FAQs

  1. What is big data?
    Big data is a collection of data sets that are too large and complex that no traditional data management tools can store or process efficiently.
  2. How has data management evolved?
    In the past, technology was used to support a specific business need and determine basic things like how many items were sold to how many customers. At present, organizations have more data than ever before. Now businesses focus on developing applications that specifically take advantage of the unique characteristics of big data.
  3. Give some examples of big data applications.
    The areas like healthcare, manufacturing management, traffic management, etc use big data. In manufacturing, a big data application might help to prevent a machine from shutting down during a production run. A big data traffic management application can be used to reduce the number of traffic jams on busy highways.

Conclusion

In this article, we studied the fundamental concepts of big data and how data management has evolved over time. We hope that this blog has helped you enhance your knowledge of big data. Do upvote our blog to help other ninjas grow.

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