Billions of digital solutions generate data, but only a small proportion of data is processed. The penetration of data into a traditional system has overburdened companies with Data Silos. This is where there is a need for using Big Data Analytics at scale to harness the potential of massive information. Big Data Analytics facilitates handling colossal amounts of data and faster computation to take revolutionary business decisions, making it a must for organizations to taste success.
This article provides you with comprehensive aspects of Big Data Analytics. It also explains the characteristics and processes involved while performing Big Data Analytics. Lastly, the article gives an overview of the tools and advantages of Big Data Analytics.
Table of Contents
What is Big Data Analytics?
Data is generated every moment in various forms, but creating value from a mixture of data requires extensive Analysis. Big Data refers to large volumes of data that can handle structured, semi-structured, and unstructured data. The large size and complexity of data govern the importance of using Big Data tools for various Business Analytics processes. The pinnacle of modern Data Science is purely designed to handle enormous amounts of data. Over a while, Big Data Analytics as a field saw a rampant change in how data is captured and processed for Business Growth.
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Characteristics of Big Data
With the astronomical growth of data, understanding the Big Data deluge has encouraged many companies to look at their data in many ways to extract the potential lying in their Data Lakes. Big Data is characterized by compiling with five V’s, resembling its characteristics:
An enormous volume of information is produced daily, but collecting data is not the mere solution for businesses. Organizations invest in several Big Data technologies, as it not only facilitates Data Aggregation and Storage but also assists in garnering insights from raw data that could help companies gain a competitive edge in the market.
In general, data is diverse and is collected from various sources involving external and internal business units. Big Data is generally classified into three types:
- Structured Data: Structured Data has a predefined format, length, and volume.
- Semi-structured Data: Semi-structured data may partially conform to a specific data format like key-value pair.
- Unstructured Data: Unstructured Data includes audio and video, thereby they are unorganized.
However, Unstructured Data accounts for more than 80% of total data generated through digital solutions.
The speed at which data is generated, collected and analyzed refers to the Velocity component of Big Data. With advancements in Big Data technology, data is captured as close to real-time as possible to make the data available at the receiver’s end. This high-speed data can be accessed with Big Data tools to generate insights, which could have a direct impact on making timely and accurate business decisions.
The Veracity of data refers to the assurance of credibility or quality of collected data. The variations of dimensions in Big Data often cause challenges related to inaccurate information in business processes. With adequate tools, the Veracity of data can be controlled to help organizations uncover insights and devise strategies with accurate information to target customers.
A colossal amount of data requires infrastructure like Data Warehousing, Data Lake, and Databases to handle large volumes of data for different needs. However, Data Explosion has become a challenge for every organization, with projected growth of 180 zettabytes by 2025, eventually pushing industries to embrace modern business intelligence tools to effectively capture, store, and process such anomalous amounts of data in real-time.
Hevo Data is a No-code Data Pipeline that offers a fully-managed solution to set up data integration from 100+ data sources (including 30+ free data sources) and will let you directly load data to a Data Warehouse such as Snowflake, Amazon Redshift, Google BigQuery, etc. or the destination of your choice. It will automate your data flow in minutes without writing any line of code. Its Fault-Tolerant architecture makes sure that your data is secure and consistent. Hevo provides you with a truly efficient and fully automated solution to manage data in real-time and always have analysis-ready data.
Its completely automated pipeline offers data to be delivered in real-time without any loss from source to destination. Its fault-tolerant and scalable architecture ensure that the data is handled in a secure, consistent manner with zero data loss and supports different forms of data. The solutions provided are consistent and work with different BI tools as well.
Check out why Hevo is the Best:
- Secure: Hevo has a fault-tolerant architecture that ensures that the data is handled in a secure, consistent manner with zero data loss.
- Schema Management: Hevo takes away the tedious task of schema management & automatically detects the schema of incoming data and maps it to the destination schema.
- Minimal Learning: Hevo, with its simple and interactive UI, is extremely simple for new customers to work on and perform operations.
- Hevo Is Built To Scale: As the number of sources and the volume of your data grows, Hevo scales horizontally, handling millions of records per minute with very little latency.
- Incremental Data Load: Hevo allows the transfer of data that has been modified in real-time. This ensures efficient utilization of bandwidth on both ends.
- Live Support: The Hevo team is available round the clock to extend exceptional support to its customers through chat, email, and support calls.
- Live Monitoring: Hevo allows you to monitor the data flow and check where your data is at a particular point in time.
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Process of Big Data Analytics
Big Data Analytics is being used with emerging technologies, like Machine Learning, Deep Learning, and AI (Artificial Intelligence) to discover and scale more complex insights. To uncover insights from an enormous set of data follows a process as mentioned below:
- Problem Statement
- Data Requirements
- Data Processing
- Data Analysis
- Data Visualization
1) Problem Statement
The first step in Big Data Analytics consists of business understanding. If any requirement emerges, business objectives are determined, the situation is assessed, data mining goals are determined, and then the project plan is framed as per the requirements.
2) Data Requirements
The Data Requirements Analysis process employs a top-down approach to emphasize business-driven needs and ensure identified needs are relevant and feasible. This process involves understanding different data types’ convenience and their extraction to fall in line with required applications. Consequently, it becomes crucial to have prior knowledge and monitor the requirements to maintain data quality.
3) Data Processing
Once the Data Requirements are confirmed with stakeholders, data is collected from multiple sources to convert into the desired format. This extracted data is stored in a Data Lake or Data Warehouse. It is further processed considering business rules and regulations to remove noise and duplicate information. Data Processing standardizes data to either perform timely Batch-Processing or Stream-Processing to make quick decisions.
This entire process of collecting, processing, and transforming data is automated using ETL (extract, transform, load) tools for making sensible decisions from large datasets.
4) Data Analysis
Data Analysis is the primary aim of any organization to understand its customers. However, getting Big Data to a usable state takes time. The advanced Analysis tools can turn Big Data into effective insights using Data Mining, Predictive Analytics, and Deep Learning algorithms.
5) Data Visualization
Big Data enables organizations to track the entire timeline of business operations, including various attributes, to get an in-depth understanding. Data Visualization enables stakeholders and non-technical audiences to interact with data giving the pulse of the market. Many Visualization tools offer a plethora of features enhancing the representation of data by creating interactive dashboards through integration with Databases.
Big Data Analytics requires a wide range of tools to perform tasks like Collecting, Cleaning, Processing, Analyzing, and Visualizing. Several types of tools work together to perform Big Data Analytics, and a few tools are mentioned below:
It’s a repository that stores business data collected from multiple resources. Data Warehouses are designed to support business intelligence activities and generally contain vast amounts of structured and semi-structured data.
It is a framework that can handle enormous volumes of data. Hadoop helps in Data Storage and Data Processing using HDFS (Hadoop Distributed File Systems) and MapReduce framework. This is an open-source tool that supports the handling of structured, semi-structured, and unstructured data, making it a valuable tool in any Big Data operation.
Three steps i.e. Extract, Transform, and Load make the ETL process, which helps in extracting data from different sources, transforming it into the analytics-ready format, and storing the quality data in Data Warehouses. No-code ETL tools like Hevo Data can help in expediting the ETL process by automating this entire process.
An open-source cluster computing framework that uses implicit data parallelism and fault tolerance to provide an interface for programming entire clusters. Spark is capable of handling both Batch and Stream Processing for fast computation.
Kafka is a reliable Stream-Processing platform that is capable of ingesting real-time data into Data Lakes, Redshift, and several Analytics platforms in a distributed and fault-tolerant manner. It is also flexible and scalable, with numerous users for analyzing Big Data in real-time.
Data is visualized by integrating data with business intelligence tools like Power BI and Tableau, providing custom interactive dashboards. This tool also provides real-time Visualization and can be accessed by any non-technical professional.
Advantages of Big Data Analytics
The majority of businesses have Big Data. The requirement to harness and derive value from such data has increased, and Big Data Analytics has gained wide acceptance in the market. Some of the advantages of Big Data Analytics include:
- Organization Become Smarter
- Optimizing Business Operation
- Cost Reduction
1) Organization Become Smarter
Big Data Analytics helps detect and identify patterns to predict the likelihood of events for making informed decisions. It gives businesses sufficient time to create strategies and set benchmarks in the market by analyzing the information and forming an action plan to succeed.
2) Optimizing Business Operations
Organizations optimize their operations by tracking customer behavior and interest trailing in their Databases. For instance, e-commerce websites use click-stream data and purchased data to provide customized results, improving user experience and eventually increasing turnover.
3) Cost Reduction
Big Data Analytics not only hails as a revenue generator for companies by offering data-driven solutions but also diversifies their benefits by optimizing operational expenses. It leverages companies in making strategic decisions and thereby increasing overall revenue.
This article has provided a comprehensive aspect of Big Data Analytics. It also gave a brief overview of the characteristics and processes involved in Big Data Analytics. Furthermore, an overview of different tools and advantages of Big Data Analytics was also discussed.
Big Data Analytics plays a vital role in every segment and is continuously evolving to give new business ideas. As organizations are becoming data-driven, the application of Big Data is flourishing its roots in every possible direction. Big Data Analytics gives vision to organizations by bringing past, present, and future in one timeline. This helps companies to learn from their success and prevent themselves from upcoming risks.
Businesses can use automated platforms like Hevo Data to set this integration and handle the ETL process. It helps you directly transfer data from a source of your choice to a Data Warehouse, Business Intelligence tools, or any other desired destination in a fully automated and secure manner without having to write any code and will provide you with a hassle-free experience.
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