In real-word, most of the data is unstructured, making it difficult to streamline the Data Processing tasks. And since there is no end to the Data Generation process, collecting and storing information has become increasingly difficult. Today, it has become essential to have a systematic approach to handling Big Data to ensure organizations can effectively harness the power of data.
In this article, you will learn about Big Data, its types, the steps for Big Data Processing, and the tools used to handle enormous information.
Prerequisites
- Fundamental understanding of the digital world.
What is Big Data?
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Big data is the collection of Structured, Semi-structured, and Unstructured data which can be processed and used in Predictive Analytics, Machine Learning, and other advanced Data Analysis applications. According to Gartner, “Big data is high-volume, high-velocity, and high-variety information asset that demands cost-effective, innovative forms of information processing for enhanced insight and decision making.”
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In addition, Big Data is defined by Doug Laney as 5 Vs – Volume, Velocity, Variety, Value, and Veracity.
Volume represents the amount of Structured and Unstructured data collected, Velocity means the frequency at which the data is received, Variety refers to the formats of data like audio, videos, text, numerical data, and more, Value defines how much the collected data is useful, and the accuracy of the data collected is referred to as Veracity.
Despite the fact that “Big Data” does not refer to a specific quantity of data, big data implementations usually include gigabytes, terabytes, and zettabytes of data collected across periods. Today, companies are using massive datasets to enhance management, offer better client support, generate targeted marketing campaigns, and more. Big Data, for instance, may supply businesses with important Consumer Analytics that can be leveraged to improve marketing strategies and practices in boosting customer involvement.
What are the Types of Big Data?
- Structured
- Semi-Structured
- Unstructured
A) Structured Data
Structured Data refers to the standardized format with a well-defined structure. Structured data is organized in a table with relationships between the columns and rows. For example, Excel files or SQL Databases contain rows and columns of Structure data. The existence of a data model — a concept of how data is stored, accessed, and processed – is required for Structured data. Each field is distinct and may be accessed independently or in conjunction with information from other areas.
B) Semi-Structured Data
Semi-structured Data is defined as data that cannot be arranged in Relational Databases or that lacks a precise functional structure but has certain structural qualities. Semi-structured data consists of information that is grouped by topic or fits into a hierarchical programming language. It is a kind of Structured data that does not hold any tabular format of Data Models related to any Relational Databases. The object-oriented database contains XML documents, HTML files, and tables are common examples of semi-structured data. The advantage of Semi-structured data is that it is widely available and can be used to generate in-depth insights.
C) Unstructured Data
Unstructured data is classified as Qualitative Data since it has no predetermined shape or structure. Every day, businesses receive massive volumes of Unstructured data – video, audio, text, and more, which is being used for creating massive Deep Learning models to solve some of the complex real-world problems. However, generating insights from Unstructured data is difficult and requires huge computational power.
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What is Big Data Processing?
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Big Data Processing is the collection of methodologies or frameworks enabling access to enormous amounts of information and extracting meaningful insights. Initially, Big Data Processing involves data acquisition and data cleaning. Once you have gathered the quality data, you can further use it for Statistical Analysis or building Machine Learning models for predictions.
5 Stages of Big Data Processing
This initial step of Big Data Processing consists of collecting information from diverse resources like enterprise applications, web pages, sensors, marketing tools, transactional records, etc. Data processing professionals extract information through many Unstructured and Structured Data Streams. For instance, in building a Data Warehouse, extracting entails merging information from multiple sources, subsequently verifying the information by removing incorrect data. To decide future decisions based on the outcomes, the data collected during the data collection phase of Big Data Processing must be labeled and accurate. This stage establishes a quantitative standard as well as a goal for improvement.
The data transformation phase of Big Data Processing defines changing or modifying data into required formats which helps in building different insights and visualizations. There are many transformation techniques like Aggregation, Normalization, Feature Selection, Binning and Clustering, and concept hierarchy generation. Using these techniques for Big Data Processing, developers transform Unstructured Data into Structured Data and Structured Data into a user-understandable format. Business and Analytical operations become more efficient as a result of the transformation, and firms can make better data-driven choices.
Stage 3: Data Loading
The converted data is transported to the centralized database system in the load stage of Big Data Processing. Before loading the data, index the database and remove the constraints to make the process more efficient. Using Big Data ETL, the process of loading became automated, well-defined, consistent, and Batch-driven or Real-time.
Stage 4: Data Visualization/BI Analytics
Data Analytics tools and methods for Big Data Processing enable firms to visualize huge datasets and create dashboards for gaining an overview of the entire business operations. Business Intelligence (BI) Analytics answer fundamental business growth and strategy questions. BI tools make predictions and what-if analyses on the transformed data that help stakeholders understand the depth patterns in data and the correlations between the attributes.
Stage 5: Machine Learning Application
The Machine Learning phase of Big Data Processing is primarily concerned with the creation of models that can learn to evolve in response to the new input. The learning algorithms allow for more quickly analyzing large amounts of data.
- The first type of Machine Learning is Supervised Learning, which uses labelled data for training the models and predicting the outcomes. Data patterns are used in Supervised learning to identify new information output for the labels. This method is often used in applications that utilize historical data to predict future outcomes.
- Unsupervised Learning is the second type where the data is unlabeled and trained by the algorithm. Unsupervised Machine Learning is utilized against information that doesn’t have any historical labels.
- Reinforcement Learning is the final type in which there is no primary data that can be inserted as input to models. The algorithms have to figure out the decisions on their own based on observations or situations that happen surrounding them. The decisions are manipulated with a reward function so that the models try to make the correct decisions.
The Machine Learning phase of Big Data Processing enables automatic recognition patterns and can perform feature extraction in complicated unstructured information without any human interference, making this a significant resource for Big Data research.
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1) Apache Spark
Apache Spark is a Big Data Processing and Machine Learning Analytics Engine that operates at lightning speed. Spark provides an API that is easy to use and handles large datasets for fast analytics queries. It also provides several libraries which support SQL Queries, Graph Processing, and building Machine Learning models. These conventional packages help developers work more efficiently while creating complicated workflows.
2) Hadoop
Apache Hadoop is a Java-based open-source, robust, and fault-tolerant Big Data Processing platform from the Apache software foundation. Hadoop is built to handle any type of information, including Organized, Semi-structured, and Unstructured data. Each task in Hadoop is broken into small sub-tasks, which are then allocated to each data node in the Hadoop cluster. In a Hadoop cluster, each data node processes a modest quantity of data, resulting in low network traffic.
3) Altas.ti
With accessible research tools and best-in-class technology, ATLAS.ti helps you find meaningful insights. This may be used in academia, market research, and customer experience study, including qualitative and combined methodologies analysis.
4) HPCC
HPCC’s Big Data Processing solution was created by LexisNexis risk solutions company that provides data processing services under a common platform, structure, and scripting languages. It represents one of the most effective big data solutions available, allowing users to complete jobs using significantly minimum programming.
5) Apache Cassandra
The Apache Cassandra database is commonly utilized to organize large volumes of information effectively. It is the best tool for businesses that can’t afford to lose their data when the data center is down. Cassandra is a NoSQL Database that allows you to transfer data horizontally across clusters seamlessly. It has the capacity for huge scalability and is not limited to joins or predefined schemas.
6) Strom
Apache Storm is a master-slave architectural computation system. It’s ideal for analyzing large volumes of data in a small period of time. The Storm is the leading tool in real-time intelligence due to its low latency, scalability, and ease of deployment. Since Strom is open-source, it is used by small-scale as well as large-scale businesses.
Conclusion
In this article, you learned about big data processing and its characteristics. Big data processing has become a trending technology, and big data tools play a huge role in the organizational data analysis process. The usage of Big Data tools to store, process, and analyze data has changed the environment of data knowledge discovery, particularly data preprocessing processes.
There are various Data Sources that organizations leverage to capture a variety of valuable data points. But, transferring data from these sources into a Data Warehouse for a holistic analysis is a hectic task. It requires you to code and maintains complex functions that can help achieve a smooth flow of data. An Automated Data Pipeline helps in solving this issue and this is where Hevo comes into the picture. Hevo Data is a No-code Data Pipeline and has awesome 100+ pre-built Integrations that you can choose from.
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Pranay is curious about topics related to data science at heart with a passion for data, software architecture, and writing technical content. He is passionate about solving business problems through content tailored to data teams.
