Big Data is the latest trend in the IT world. Although it has been around for some time now, there is still quite a lot of confusion about what it means. The concept is continually evolving and is the driving force behind many continuous waves of digital transformation. But what exactly is Big Data?
Big Data means data that is too diverse, fast-changing or voluminous for conventional techniques, skills, and infrastructure to address efficiently. You can say that the volume, velocity or variety of data is too high. This data can be structured, semi-structured or unstructured.
Apache hadoop is at the center of a growing ecosystem of big data technologies today.
Let’s learn more about Big Data and how it is transforming our world.
What do you mean by Big Data?
There is no predefined rule about exactly what size a dataset has to be to become Big Data. Instead, what typically describes big data is the requirement for new techniques and tools to be able to process it.
The data sets are so vast and complicated that it becomes impractically impossible to manage with traditional data management tools.
It represents a collection of data that is huge and yet growing exponentially with time.
What type of datasets are Big Data? – Examples
An example of big data can be petabytes or exabytes of data including enormous records of millions of people, all from different sources, such as Web, sales, social media, mobile data and so on.
Some prominent examples include:
- Some statistic shows that 500 terabytes of new data get ingested into the databases of social media sites like Facebook, every day. This data mainly generates in the form of photo and video uploads, message exchanges, putting comments, etc.
- Another good source is the transactional data that includes everything from stock prices to banking transactions data to customer’s purchase histories.
- Sensors embedded into everyday objects result in billions of new, constantly-updated data feeds containing environmental, location, and other information, including video. For instance, location data on a cell phone network.
How can you characterize Big Data? – The Three Vs
You can describe big data with the help of 3Vs, i.e., the volume of data, the variety of data types and lastly, the velocity at which the data generation occurs.
Other concepts later attributed with big data are veracity, i.e., how much noise is in the data and value.
The term Big Data itself points to a size which is extensive. The volume of data plays a very crucial role in determining value out of data.
Also, whether a particular data can is a Big Data or not, is dependent upon the volume of data.
Big data does not equate to any fixed volume of data. However, the term is often used to describe terabytes, petabytes and even exabytes of data captured over time.
The term Velocity indicates the speed of generation of data. How quickly the data is generated and processed to meet the demands, determines the real potential in the data. The flow of data is massive and continual.
For example, Clickstreams and ad impressions capture user behavior at millions of events per second. Infrastructure and sensors generate extensive log data in real-time.
Variety describes the heterogeneous sources and the nature of data, both structured and unstructured.
Big Data does not include only numbers, dates, and strings. It is also geospatial data, 3D data, audio and video, and unstructured text.
It contains data from a wide range of sources and formats, such as weblogs, social media interactions, e-commerce, and online transactions, financial transactions, etc.
What are the different categories of Big Data?
Big Data includes a massive volume of structured, semi-structured and unstructured data. Let’s take a closer look.
Structured data has a fixed format for recording, accessing, and processing operations. Nowadays, the size of this structured data is also growing to a considerable extent. A good example is the Student table in a database.
Semi-structured data includes both the forms of data, i.e., structured and unstructured.
You can see semi-structured data as structured in style but it has no proper definition. For example, a data represented in an XML file.
Any data with no known format or the structure is known as unstructured data. Along with the vast size, unstructured data poses multiple challenges concerning its processing for deriving value out of it.
A typical example of unstructured data is a heterogeneous data source containing a combination of simple text files, images, videos, etc., such as output returned by Google Search.
How does Big Data work?
Big Data functions on the idea that the more you know anything or any situation, the more reliably you can obtain new insights and make predictions about what will happen in the future.
Generally, big data processing involves a typical data flow, from the collection of raw data to the consumption of actionable information.
- Collecting the raw data, such as transactions, logs, etc. is the first step towards Big Data processing. An excellent big data platform makes this step more comfortable, allowing developers to ingest a wide variety of data at any speed.
- Any big data platform demands a secure, scalable, and permanent repository to store data before or even after processing tasks. You may also need temporary stores for data-in-transit.
- The next step is to process and analyze these datasets by transforming them from a raw state into a consumable format. It is done usually through sorting, aggregating, joining and even performing more advanced functions and algorithms. After that, the storage of resulting data sets occurs for further processing.
It can also be made available for consumption via business intelligence and agile data visualization tools.
- The objective behind Big data processing is to obtain a high value, actionable insights from your data assets. Based on the type of analytics, end-users may also consume the resulting data in the form of statistical predictions (predictive analytics), or recommended actions (prescriptive analytics).
What is Big Data Analytics?
Now that you know how big data processing works it will be easier for you to understand Big Data Analytics. Big data analytics is speedily gaining popularity.
Organisations now realize that their big data stores are a mostly unexploited gold mine that can help them diminish costs, escalate revenue and become more competitive.
Now their objective is not just storing this extensive data, but they also want to convert that data into valuable insights.
Big data analytics refers to the utilization of advanced analytic tools against huge and diverse datasets to uncover hidden patterns, unknown correlations, which might provide valuable insights about the users who created it.
Analyzing big data allows analysts and businesses to make better and faster decisions using data that was previously inaccessible or unusable.
Analysts working with Big Data typically want the knowledge that comes from analyzing the data.
What are the technologies used in Big Data?
Interpretation of Big Data emphasizes discovering hidden trends, or patterns. Sounds easy, right? Well, it necessitates the use of new technologies and skills to examine the flow of information and draw conclusions.
As a result, many organizations turn to newer and efficient data analytics environments and technologies such as NoSQL databases as well as Hadoop and its companion tools, including MapReduce, Hive, Spark, etc.
What is Big Data and Hadoop?
Hadoop is at the center of a growing ecosystem of big data technologies today. It is an open source distributed processing framework that manages data processing and storage for big data applications running in clustered systems.
It gives users more flexibility for collecting, processing and analyzing data than relational databases and data warehouses provide.
Hadoop provides massive storage for any data, enormous processing power and the ability to handle virtually limitless concurrent tasks or jobs.
A set of functions known as Map Reduce coordinates the processing of data in different segments of the cluster then breaks down the results into more manageable chunks which are summarized.
Hadoop Distributed File System (HDFS) is the storage system of Hadoop which splits big data and distribute across many nodes in a cluster.
Why is Big Data so important?
The rapidly growing stream of sensor data, images, text, audio, and video data makes it possible to use data in ways that were not possible even a few years ago.
It is revolutionizing the world of business across almost every industry.
Intelligent decision making
By analyzing this data, organizations can understand trends about the data they are measuring, as well as the people generating this data.
Big Data has the potential to assist companies to refine operations and make faster, more intelligent decisions. The sources of data include emails, smartphones, search engines, social media platforms, etc.
This data can help an organization to gain useful insight to increase revenues, get or retain customers and improve operations.
Improved Customer Service
New systems embedded with Big Data and natural language processing technologies are replacing the traditional customer feedback systems.
These new systems are being used efficiently to read and evaluate consumer responses.
Companies can now accurately determine what specific segments of customers will want to purchase, and when, to an incredibly accurate degree.
Today, new technologies make it feasible to realize value from Big Data. For instance, retailers can track user web clicks to identify behavioral trends that improve campaigns, pricing and stocking.
Social media networks analyze their user’s data to learn more about them and connect them with content and advertising relevant to their interests.
Better operational efficiency
A sound big data strategy can assist organizations to diminish costs and gain operational efficiencies by moving heavy existing workloads to big data technologies.
These technologies create a staging area or landing zone for new data before identifying what data needs to be in the data warehouse.
Also, such integration of Big Data technologies and data warehouse helps an organization to offload infrequently accessed data.
Big Data is not limited to the business world. It is equally helpful in other fields too.
For instance, Data-driven medicine involves analyzing a large number of medical records and images for patterns that can help identify disease early and develop new medicines.
Big Data Challenges
So far you learned about the plus points of big data. But when something is so messy and huge, it will hold some challenges too.
Big Data gives us remarkable insights and opportunities, but it also raises concerns and questions that must be addressed by organizations who want to take advantage of this data.
Increasing Data Volume
Although new technologies are developing for data storage, data volumes are also growing in size about every two years. Organizations still try hard to keep pace with their data and find ways to store it effectively.
Big Data Processing and evolving technology
When dealing with more massive datasets, organizations face challenges in manipulating and managing such data.
Big Data is specifically a problem in business analytics because standard tools and procedures are not designed to search and analyze massive datasets.
Finally, big data technology is evolving at a rapid pace. That’s why keeping up with big data technology is also an ongoing challenge.
Data Privacy and Security
The Big Data we now generate includes a lot of information about our personal lives, which hampers and affects our privacy. Google and Facebook already track your daily online habits.
Your smartphone and computing life leaves digital footprints every day, and sophisticated companies are studying those footprints. The security of your data is also questionable.
Final thoughts on Big Data
Big Data is not just a technology, technique or initiative. Instead, it is a trend across many areas of business and technology. It is a fascinating thing for people with analytic minds and a love for tech.
Data is transforming our world and the way we live, at an exceptional rate. Imagine that if Big Data is capable of all this today, what it will be capable of tomorrow.
The extensiveness of data available to us is only going to increase in the future, and analytics technology will become more advanced. That’s why, failure to correctly address significant data challenges can result in escalating costs, as well as reduced productivity and competitiveness. Those organizations that see data as a strategic asset are the ones that will survive and thrive in the coming years.
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