Today, in our digitally-driven world, data is one of the prime aspects of any business enterprise. Today’s business enterprises are data-driven and without data, no enterprise can have a competitive advantage. Data is the new currency and oil of our generation.
Table of Contents
What is Big Data?
Today’s organizations are data-driven organizations and due to this when the data is converted into nuggets of information then there is a huge value that enterprises can extract out of it. Data is the new currency of our generation. Some of the largest organizations are sitting on a huge amount of data and this data needs to be converted into a format that can be easily understood by the right professionals in the organizations in order to drive the necessary changes to help the company grow and progress.
Big data has different definitions wherein the amount of data can be considered to be called big data or not. Today’s big data might be tomorrow’s small data but it is considered big data when the size of the data itself poses a problem.
“Information is the oil of the 21st century, and analytics is the combustion engine”
– Peter Sondergaard, Senior Vice President, Gartner.
The Types of Big Data
1. Structured Data
This is the type of data that is stored in the regular databases in terms of the rows and columns giving it a definite structure. Previously most of the data used to fall under this category but as and when our penchant for watching videos on YouTube, and Facebook grew we ventured into a world of unstructured data wherein the regular relational database management systems could no longer sort the data into a tabular format. Most of the data that is part of the structured format includes the company employee details, census records, economic data, and so on.
2. Unstructured Data
This is the type of data that can be put into a regular row and column-based format. Today over 80% of the data is unstructured data and due to this, there are a huge set of tools that are deployed for making sense of the unstructured data which is part of the Big Data Hadoop ecosystem. Going forward the percentage of unstructured big data will only increase due to the huge amounts of sensor and machine-generated data that we will be seeing as part of the Internet of Things revolution that is underway.
3. Semi-Structured Data
This is the type of data that straddles between structured and unstructured data formats. So basically, the data that is unstructured might not be so unstructured after all. The unstructured data can be converted into structured data through the addition of certain keys, attributes, or other characteristics through which they can be arranged or sorted in a database. Such data is called semi-structured data.
5 “Vs” of Big Data
This is the type of data that straddles between structured and unstructured data formats. So basically the data which is unstructured might not be so unstructured after all. The unstructured data can be converted into structured data through the addition of certain keys, attributes, or other characteristics through which they can be arranged or sorted in a database. Such kind of data is called semi-structured data.
In the realm of data-driven decision-making, “big data” stands as a cornerstone. To truly harness its potential, it’s essential to comprehend its fundamental characteristics, commonly known as the 5 Vs.
1. Volume
This “V” tells us about the generation of big data volumes, where organizations are facing challenges in storing this amount of data collected from different sources. This means that systems capable of data storage and processing are needed more than ever to create scalable data solutions.
2. Velocity
The speed at which data is processed and produced is referred to as big data velocity. To allow for faster decision-making, real-time data analytics and data streaming are imperative, especially for fast-changing domains such as social media, financial markets, and more.
3. Variety
Big data consists of different data formats, including databases (structured data), XML and JSON (semi-structured data), and text/image/video (unstructured data) forms. It is of utmost importance to have diverse data management and processing applications and tools.
4. Veracity
Veracity is concerned with the accuracy and trustworthiness of information. A suitable level of data quality can be achieved after thorough data cleansing and validation.
5. Value
New technologies are most valuable when they improve business strategic planning. Technology investment is measured by the ability to convert raw data into actionable information for meaningful processing.
Why is Big Data Important?
In the present digital sphere, big data has emerged as a necessity for an organization to gain insights and make calculated decisions. Let’s explore its usefulness in detail:
1. Improved Decision-Making
Businesses are able to uncover insightful hidden patterns and trends that can be analyzed with the help of vast data sets. This leads to the development of effective strategies with data-based decisions.
2. Enhanced Customer Experiences
Understanding the needs and behavioral patterns of the customers at a deeper level has become possible owing to big data. This facilitates personalized services, enhanced marketing strategies, and greater customer loyalty.
3. Operational Efficiency
Improvement in processes accompanied by cost reduction and productivity enhancement can be achieved by identifying the weak areas of operational data analysis.
4. Risk Management
Understanding patterns and their anomalies enables organizations to pinpoint probable risks and eliminate them with the assistance of big data. This is vital for industries such as health care and finance.
5. Innovation and Development
Innovation is greatly achieved from big data. Businesses can discover innovative possibilities for additional goods and services by investigating extensive data sets.
6. Fraud Detection
Big Data aids in detecting fraudulent activity almost instantaneously by studying vast quantities of transactional data.
What Are Examples of Big Data?
Since Big Data is a big part of every organization we are here concentrating on some of the most important big data projects that can help you understand the type of ways in which you can work using Big Data in the real world.
“Data really powers everything that we do.”
– Jeff Weiner, Chief Executive of LinkedIn
1. MovieLens Dataset Project
This is a big data project that involves working with the MovieLens data that is available in the form of rating data sets. Some of the aspects of this project include:
- Writing a MapReduce program for finding the top 10 movies by working on the data file
- Use Apache Pig to create the top 10 movies list by loading the data
- Deploying Hive for creating the top 10 movies list by loading the data
2. Hadoop YARN Project
This project involves working with the Hadoop YARN which is part of the Hadoop 2.0 ecosystem thus letting it decouple from the MapReduce application for computing of big data. This includes working on the Hadoop central resource manager. Some of the aspects of this project include:
- Movie data importing
- Appending the data and using Sqoop to bring data to HDFS
- Determining end to end transaction flow.
3. Hive Table Partitioning Project
This project involves working with Hive data table for partitioning of data. With the right partitioning the data can be read, deployed on HDFS, can be made to run the MapReduce jobs faster. There are different ways of partitioning of data through Apache Hive. Some of them are as below:
- Dynamic partitioning
- Manual partitioning
- Bucketing
4. Connecting Hadoop with Pentaho ETL Project
This project involves working with Pentaho ETL tool and connecting it with Hadoop. Some of the aspects of connecting Pentaho with Hadoop are as follows which you will be working in this project:
- Interactive data analysis with Pentaho data analyzer.
- Deploying the graphical build for reading and writing of data into Hadoop
- Data orchestration, data movement and other aspects of working with data
- Working with pixel perfect data reporting
Big data truly shines when supported by a strong ecosystem of specialized tools. Let’s take a quick look at the highlights!
- Data Storage:
- Cluster Management:
- YARN manages resources within Hadoop clusters, enabling efficient processing.
- Kubernetes increasingly orchestrates big data workloads, providing containerization.
- Stream Processing:
- Spark Streaming enables real-time data analysis.
- Kafka Streams builds real-time data pipelines.
- Flink is used for stateful computations on data streams.
- NoSQL Databases:
- Cassandra handles high-volume write operations with scalability.
- MongoDB offers flexibility for unstructured data.
- HBase works on top of HDFS.
- Data Lakes & Warehouses:
- Hive facilitates SQL-like queries on Hadoop datasets.
- Cloud warehouses (Redshift, BigQuery, Snowflake) deliver high-performance analytics.
- Iceberg, Hudi, and Delta Lake bring data warehouse features to data lakes.
- SQL Query Engines:
- Spark SQL enables SQL queries within the Spark environment.
- Presto/Trino provides fast, interactive analytics.
- Apache Drill offers SQL on NoSQL and Hadoop.
Big Data Analytics: Working with Big Data
Industries today leverage big data analytics to gain insights that drive innovation and growth. Organizations must extract, process, and visualize data effectively to stay competitive.
Key Aspects of Big Data
“The world is one big data problem.”
– Andrew McAfee
1. Data Organization
- Cleansing & Segregation: Removing errors and categorizing structured and unstructured data.
- Transformation: Converting raw data into an understandable format.
2. Data Processing & Analytics
3. Data Visualization
- Insight Generation: Transforming complex data into meaningful visuals.
- User-Friendly Representation: Dashboards, charts, and reports.
Benefits of Big Data
1. Proactively Engaging With the Customer
You will have your finger on the pulse of the customer. Today there are so many avenues in the customer journey that during every phase the customer leaves a digital trail and the digitally aggressive companies will get their hands on this digital trail and get the insights out of it. All this helps to server the customer better and in a more streamlined manner.
2. Generation of New Revenue Streams
Gone are the days when any company used to stick to its industry vertical. Today there are no more vertical thanks to the power of digitization. If a company is an ecommerce player then there is nothing that can stop it from going into cloud computing and storage. The example of Amazon is a prime example in this arena.
“Data is the new science. Big Data holds the answers.”
– Pat Gelsinger, CEO of VMware.
3. Product and Service Redesign to Meet Customer Needs
A lot times it happens that the initial product created by a company might be of inferior quality or just not up to the expectations of the company. But this need not deter the forward-thinking companies. There are enough insights that the customer is giving to help a company tailor-make its products and services as per the needs of the customer. All that a company needs to do is dig in deeper into big data and get all the insights that are needed for creating a world-class product or service.
Every business comes with its own set of risks and also there is the risk of competitors trying to dwarf a company and eventual put it out of business. It is unlike the good old days when the game was biased in favour of the big players. But big data is making it a very democratic way of running the business. Get enough data, deploy enough tools to make sense of it and you are at the top of your game in no time.
6. Data Safekeeping and Regulatory Compliance
Today due to the deluge of big data there are a lot of regulatory compliance that needs to be adhered to either through government regulations or through other industry related regulatory authorities. So big data also helps the organizations to keep the data safe and keep it in accordance with the regulatory compliance. If there is a breach or failure to keep up with the rules then it can be easily flagged and necessary changes can be brought about without any hassles or delay.
7. Streamlining Business Processes and Maintenance
No business can stay immune to the winds of change that is sweeping the corporate world thanks to some powerful forces brewing in from all directions. Today changing of the domain or tapping into an international market is just a matter of intent rather than a big structural change. All this is possible thanks to the power of big data. It is possible to streamline the business processes and tap into an opportunity which even a decade ago was unthinkable. Maintaining a business to keep it in sync with the changing times is also easier.
Big Data Challenges
Although big data has great promise, it comes with several obstacles. Here are some of the issues confronted in trying to manage or make any meaningful use of vast datasets:
1. Identifying Valuable Data
- Sifting through massive datasets to separate useful information from clutter.
2. Data Silos and Incompatibility
- Failure to integrate data from different sources because of their varying formats.
- The inability to analyze data due to its separation and storage within different systems.
3. Data Accuracy and Reliability
- Managing immense amounts of erroneous data.
- The degree of trust that can be placed on data and its suitability for application.
Keys to an Effective Big Data Strategy
As businesses strive to turn data into informative insights, sound decision-making, and intelligence for competing successfully in the market, It is clear that a well-structured big data strategy is necessary. Below are some pointers for crafting an effective big data strategy.
1. Define Clear Business Objectives
Every initiative that utilizes big data should have a tangible organizational goal in mind. Companies should define clear use cases that deal with improving customer service, supply chain management, and fraud management.
2. Invest in the Right Infrastructure
Finding a suitable processing and storage solution is vital. Certain cloud solutions as well as the Hadoop and Spark frameworks allow processing of very large datasets.
3. Implement Robust Data Governance
A company needs to have effective policies in place regarding the quality, security, and compliance of data. The use of data governance frameworks provides the required level of quality control.
Big Data Collection Practices and Regulations
Business organizations are very much dependent on big data systems for deriving valuable insights. It helps to optimize business operations, streamline the entire lifecycle of the business from raw material to the end product. This systems provide answers faster for business to take the right data-driven decisions. It improves the quality of services and helps to understand the mindset of the customer. It tailor-makes the products and services according to the needs of the customer.
The importance of Big Data in today’s world cannot be underestimated as there is a sort of arm’s race between the various organizations in order to get the most insights into the mindset of the customers and get ahead of the competition.
The Future of Big Data
The future of Big Data is being shaped by several key trends, driving innovation and expanding its potential:
1. Open Source Dominance: A robust shift towards open-source technologies promotes collaboration and enhances accessibility in the Big Data ecosystem.
2. In-Memory Processing and Real-Time Analytics: Growing use of tools such as Apache Spark facilitates quicker data processing and real-time insights.
3. Machine Learning and Predictive Analytics: Major progress in utilizing big data for machine learning and predictive modeling is enhancing data-driven decision-making.
Big Data Companies
Today Big Data is so rampant that one has to look which are the companies that are not deploying Big Data. Starting from technology companies like Google, Apple, Amazon, Microsoft all the way to mining companies like Rio Tinto, retailers like Walmart and hospitality companies like Airbnb are all using big data and big data analytics.
Here we will be talking about a few of the companies that are using Big Data at scale :
- Amazon – Getting insights on all the customer data and providing better user experience.
- Google – Making sense of what the customer is searching for and providing better search results.
- Rio Tinto – Finding out which are the mining rich places and how to go about getting the best output.
- Walmart – Providing customers what they are looking for in terms of products, discounts, etc.
Top Industries Utilizing Big Data Applications
1. Banking and Finance
This is the one of the top domains when it comes to deployment of big data applications. Since banking and finance works exclusively with large amounts of data there is need to make sense of all that data at scale. It could be about understanding the credit risk of a certain customer, analyzing the fraud transactions within millions of genuine transactions, customizing the financial products to millions of customers based on their needs, financial capacity, credit risk and so on.
2. Healthcare
This is another domain that is exclusively deploying the big data applications at scale in order to unravel the mysteries of the human medical condition, the right mode of treatment depending on the patient medical history and other conditions. Mapping the human genome is a big application of big data wherein the DNA is sequenced in order to understand completely about the human body from a medical point of view.
3. Education
The domain of education is slowly but surely using big data analytics in order to improve the centuries old education system. It is all about ascertaining the learning capability of each individual in order to tailor-make a certain educational regimen to each student. It is about improving the mode of training so that the students are in a better position to make progress and ultimately become industry-ready by equipping the right skills.
Media and entertainment is going through a sea change thanks to the rapid digitization, influence of social media and such other monumental changes happening in this domain. Big data is playing a big role in this domain to understand the customer sentiment, leverage the power of social media and mobile platforms to deliver the right content at the right time to the right audience. It is also extensively used in content customization, recommendation and measurement for giving a holistic experience to the end-user.
How UPS Utilizes Big Data in Orion?
UPS (United Parcel Service) is the world’s premier courier service agency and the amount of data that is generated at UPS is nothing like anything. Due to this they need a strong data analytics system in order to make sense of all the data at their disposal. This is where the proprietary technology comes into the picture which is nothing but On Road Integration Optimization and Navigation or ORION system for the uninitiated, built by UPS exclusively for its drivers.
This system maps the routes of each driver in the grid and details the entire route that the truck has to take that can help to save precious miles and time. All this leads to huge amounts of savings to the UPS Company that ranges in the millions of dollars each month.
How to Learn Big Data?
Learning big data today is easy thanks to the proliferation of online big data professional training institutes. But not all training is created equal. You need to enroll yourself for the big data training institute which offers hands-on training, is in line with clearing the industry certification like the Cloudera Hadoop certification, and offers you the most updated Hadoop training so you can get the right job after completion of the training. Intellipaat offers the right training to learn big data from scratch which is very important to professionals who do not have a background in Big Data, Hadoop and Data Analytics.
If you’re eager to dive into the world of Big Data, consider enrolling in Intellipaat’s comprehensive Big Data Hadoop course. It’s an excellent opportunity to enhance your skills!
Conclusion
Today, Big Data has pervaded every industry that we can think of. Due to this, there is a huge change in the way we conduct business. Customers have grown super-demanding, and the big data revolution has only fueled their penchant for better products and services. Big data analytics is a whole domain in itself where valuable insights are derived from big data using various real-time analytical tools.
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