BI On Hadoop: Transforming Big Data Into Big Insights

Learn how to transform your data into insights with BI on Hadoop and effectively harness the power of this cutting-edge technology in this blog. | ProjectPro

BI On Hadoop: Transforming Big Data Into Big Insights
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Ready to take your big data analysis to the next level? Check out this comprehensive tutorial on Business Intelligence on Hadoop and unlock the full potential of your data!


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According to the latest reports, 328.77 million terabytes of data are generated daily.

This ever-increasing volume of data generated today has made processing, storing, and analyzing challenging. Organizations worldwide are realizing the potential of big data analytics, and Hadoop is undoubtedly the leading open-source technology used to manage this data. The global Hadoop market grew from $74.6 billion in 2022 to $104.95 billion in 2023 at a CAGR of 40.7%. Hadoop offers an ideal platform for running BI applications, allowing businesses to uncover hidden patterns, identify trends, and make better decisions by analyzing stored data. For instance, e-commerce companies like Amazon and Flipkart use Hadoop-based BI solutions to gain insights into customer behavior, preferences, etc., to improve their sales and customer experience. With the growing demand for big data professionals, having a solid understanding of business intelligence on Hadoop integration is becoming highly significant. This blog explores the various aspects of building a Hadoop-based BI solution and offers a few Hadoop-BI project ideas for practice. You will learn about the benefits, challenges, and best practices of implementing business intelligence on Hadoop for future big data Hadoop projects.

Why Business Intelligence On Hadoop?

Integrating and implementing business intelligence on Hadoop has revolutionized how businesses manage big data, making Hadoop-based BI solutions more efficient and cost-effective than traditional data warehousing. Business intelligence OLAP is a powerful technology used in BI to perform complex analyses of large datasets. When combined with the distributed computing framework of Hadoop, businesses can leverage the scalability and parallel processing capabilities of Hadoop to efficiently manage and process their big data.

Imagine a global e-commerce company that uses Hadoop-based solutions to handle large amounts of customer data, including purchase histories, browsing patterns, and social media interactions. By analyzing this data with business intelligence tools, the company will gain valuable insights into customer behavior, preferences, etc., allowing them to personalize their marketing strategies and improve overall customer experience. This might be difficult to achieve using traditional data warehousing due to the high cost of storage and processing. 

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Let us compare traditional data warehousing and Hadoop-based BI solutions to better understand how using BI on Hadoop proves more effective than traditional data warehousing-

Point Of Comparison

Traditional Data Warehousing

BI On Hadoop Solutions

Data Storage

Structured data in relational databases.

Both structured and unstructured data in distributed file systems.

Scalability

Vertical scaling by adding more powerful hardware.

Horizontal scaling by adding more commodity hardware.

Cost

Expensive hardware and software licenses.

Low-cost commodity hardware and open-source software.

Data Integration

Limited ability to integrate data from different sources.

Ability to integrate data from different sources, including social media, IoT devices, and weblogs.

Processing Time

Slow processing time for large data sets.

Faster processing time for large data sets.

Analytics

Limited analytics capabilities.

Advanced analytics capabilities, including machine learning, natural language processing, and predictive analytics.

Schema

Requires a predefined schema to store data.

Does not require a predefined schema, allowing for more flexibility and agility.

Example

A bank uses a traditional data warehouse to store, and analyze customer transactions from its core banking system.

A retail company uses a Hadoop-based BI solution to analyze customer sentiment data from social media, in-store transaction data, and weblogs to improve customer experience and sales.

Benefits Of BI On Hadoop

Businesses may leverage several benefits of using Hadoop-based BI solutions to acquire useful insights from their data, enhance decision-making, and stay competitive in today's rapidly growing, technology-driven business environment. 

Let us understand some key benefits of implementing Hadoop-based BI solutions-

  1. Cost-Effective Scalability

One of the major benefits of implementing business intelligence in Hadoop is cost-effective scalability. Hadoop-based BI solutions can be scaled up or down to handle large and complex datasets. As Hadoop runs on commodity hardware, it is more cost-effective than traditional data warehousing solutions. For example, a financial services company may store and analyze huge amounts of data on customer transactions, trading activities, and market trends. By implementing a Hadoop-based BI solution, they can scale efficiently and cost-effectively to handle growing data volumes.

  1. Improved Data Processing Speed

Another key benefit of Hadoop-based BI solutions is improved data processing speed. Hadoop's distributed computing architecture allows for parallel data processing across multiple nodes, improving processing speed and minimizing latency. This enables businesses to access and analyze large volumes of data in real-time, leading to faster insights and decision-making. For example, a retail company may use Hadoop-based BI solutions to analyze customer purchase history and behavior to make real-time product recommendations and pricing optimizations.

  1. Increased Flexibility

Hadoop-based BI solutions are highly flexible, allowing businesses to store and process various data formats, including structured, semi-structured, and unstructured data. This flexibility allows businesses to easily incorporate new data sources and types into their analysis, providing a more comprehensive view of their operations. For example, a media company may use Hadoop-based BI solutions to analyze user-generated content from social media platforms and incorporate that data into its marketing and content strategy.

  1. Enhanced Data Governance

Hadoop-based BI solutions provide robust data governance capabilities, enabling businesses to manage and control data access, quality, and security. This is critical for businesses handling sensitive data and complying with privacy regulations. For example, a healthcare organization may use Hadoop-based BI solutions to manage and analyze patient data while ensuring compliance with HIPAA regulations.

Why Should Big Data Experts Learn BI On Hadoop?

Big data professionals should learn business intelligence (BI) on Hadoop integration because it provides a powerful framework for working with large and complex datasets. With the rapidly increasing amount of data generated by organizations, it has become crucial for big data professionals to be equipped with the skills to analyze, visualize, and derive insights from data using BI tools on Hadoop.

Learning BI on Hadoop integration offers several benefits to big data professionals, such as-

  1. Increase In Job Opportunities- With the growing demand for professionals who can work with big data, learning BI on Hadoop integration can help big data professionals increase their job opportunities. For instance, companies that use Hadoop for big data processing often require skilled BI professionals to analyze and visualize the data. There are over 12,000 Hadoop jobs and over 56,000 Business Intelligence jobs in the United States. These numbers indicate the huge demand for professionals skilled in both Hadoop and BI.

  2. Comprehensive Data Analysis- Hadoop can store and process huge amounts of data but needs a built-in analysis and visualization framework. By integrating BI tools with Hadoop, professionals can gain the ability to perform comprehensive data analysis, including creating interactive dashboards and generating real-time insights. 

  3. Improved Data Quality- Business intelligence on Hadoop enables data professionals to work with various structured and unstructured data types from various sources. This helps improve the accuracy and quality of data, ensuring decision-makers have access to reliable information. For example, big data professionals in a retail company can use BI on Hadoop to analyze sales data from multiple sources, such as social media, transactions, and customer feedback. This can help them identify customer preferences and buying behavior, leading to better product recommendations and customer satisfaction.

  4. Increased Efficiency- Integrating BI with Hadoop can help organizations process and analyze big data more efficiently. This is because Hadoop allows for distributed processing, meaning data can be processed in parallel across multiple nodes. BI tools enable data professionals to create custom workflows and automate repetitive tasks, saving time and boosting productivity. For example, big data experts in a healthcare organization can use BI on Hadoop to analyze electronic health records from different sources, identify patterns, and create predictive disease prevention and treatment models. This can lead to improved patient results and cost savings for the organization.

You already know the benefits of building BI on Hadoop solutions, so let us now walk you through the steps to build such a solution along with two simple examples.

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BI On Hadoop Tutorial- Building A Hadoop-based BI Solution

In this section, we will understand how to implement business intelligence on Hadoop by performing a few simple steps (such as data ingestion, processing, analysis, and visualization) on publicly-available datasets.

  1. Data Ingestion

The first step in building a Hadoop-based BI solution is to ingest the data into the Hadoop ecosystem. In this example, we will use the Chicago Crime Dataset from Kaggle. The data is available in CSV format and can be ingested into Hadoop using the Hadoop Distributed File System (HDFS) or Apache Hive.

  1. Data Processing And Analysis

Once the data is ingested, the next step is to process and analyze it. We will use Apache Hive for data processing and analysis in this example. We will create a Hive table and load the data into it. Then we will write queries to analyze the data.

  1. Data Visualization

The final step in building a Hadoop-based BI solution is visualizing the analyzed data. For this example, we will use Apache Zeppelin for data visualization. We will create a Zeppelin notebook and use the results of the Hive queries to create visualizations.

Let us look at another example to understand how you can implement business intelligence on Hadoop-

In this example, we will use the NYC Taxi and Limousine Commission (TLC) Trip Record Data. This dataset includes data on every yellow taxi trip in New York City from January 2019 to December 2019.

We will use Hive and Tableau for data processing and visualization for this example.

  1. Data Ingestion

The first step is downloading and uploading the dataset to the Hadoop Distributed File System (HDFS). We will use the following command to upload the dataset to HDFS:

  1. Creating a Hive Table

We will use Hive to create a table to store the dataset. We will create a table in Hive using the following command:

  1. Loading Data Into Hive Table

Once the table is created, we will load the data into it using the following command:

  1. Data Processing With Hive

We will use Hive to process and analyze the data. For example, we can run the following query to find the total revenue earned by each passenger count:

  1. Data Visualization With Tableau

Finally, we will use Tableau to create visualizations based on the processed data. We will connect Tableau to Hive using the ODBC driver and create a new worksheet. We will drag the passenger count and revenue columns to the rows and columns section, respectively, to create a bar chart showing each passenger count's total revenue.

These two examples show how you can build effective solutions by implementing business intelligence on Hadoop using various business intelligence tools. Now, let us further understand the various tools you must know to perform crucial steps such as data analysis and visualization for implementing BI on Hadoop.

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BI On Hadoop- Data Analysis Tools

Hadoop-based business intelligence solutions are becoming increasingly popular due to the growing need among businesses to process and analyze massive amounts of data. They provide organizations with a powerful, scalable, cost-effective data storage, processing, and analysis platform. Hive, Impala, and Pig are popular Hadoop data analysis tools widely used in building and implementing business intelligence solutions.

  1. Hive

Hive is a data warehousing tool that offers an SQL-like interface for querying large datasets stored in Hadoop. It is widely used in BI solutions due to its ease of use and familiarity with SQL. Hive allows businesses to analyze large datasets in real-time and provides integration with Hadoop's MapReduce and Spark frameworks. Hive also supports custom user-defined functions, making it flexible and customizable to fulfill the needs of various businesses.

  1. Impala

Impala is another SQL query engine that allows users to run queries on data stored in Hadoop in real-time. Unlike Hive, Impala doesn't require MapReduce and can execute queries directly on Hadoop's distributed file system, making it faster for complex queries and large datasets. Impala is ideal for BI solutions that require fast query processing and analysis.

  1. Pig

Pig is a popular platform for creating MapReduce programs to analyze large datasets. It provides a scripting language called Pig Latin that abstracts away the complexities of MapReduce programming, making it easier for users to work with Hadoop. Pig is highly customizable and supports integration with Hadoop's ecosystem of tools, including Hive, HBase, and Flume, making it a popular choice for businesses that require flexibility and scalability in their BI solutions.

BI On Hadoop- Data Visualization Tools

Several Hadoop-based data visualization tools are available, but three of the most popular ones are Tableau, Apache Zeppelin, and QlikView. These tools enable users to easily create visualizations and gain insights from large amounts of data stored in Hadoop clusters. Let us briefly overview each of these Hadoop-based BI tools-

  1. Tableau

Tableau is a powerful data visualization tool business analysts and data scientists use to create interactive and dynamic dashboards. Its user-friendly interface lets users connect to different big data sources, including Hadoop, and it offers a drag-and-drop interface to create visualizations without coding skills. Tableau provides a range of visualization types, including charts, graphs, maps, and tables. Users can add filters and parameters to their dashboards to interactively explore the data.

  1. QlikView

QlikView is a powerful data discovery and visualization tool that enables users to explore and analyze data from multiple sources, including Hadoop. It provides an intuitive user interface that enables users to create dynamic visualizations without coding skills. QlikView allows users to create charts, graphs, and tables easily and supports a drag-and-drop feature for building dashboards. Users can also add filters and drill-down features to their visualizations to explore the data in more detail.

  1. Apache Zeppelin

Apache Zeppelin is an open-source web-based notebook for data exploration, analysis, and visualization. It supports multiple data sources, including Hadoop, and allows users to create and share interactive data visualizations. With Zeppelin, users can write code in several languages, including SQL, Python, and R. Zeppelin provides a range of visualization types, including charts, graphs, and tables. Users can also customize the visualizations using HTML, CSS, and JavaScript.

Now that you know the basics of building Hadoop-based BI solutions, let us understand the major challenges you might encounter while building the ideal BI on Hadoop solutions for your business problem and the best practices you must follow to avoid facing such challenges.

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Challenges With Implementing BI On Hadoop

Implementing Hadoop-based BI solutions can be challenging, but by understanding and addressing these common challenges, businesses can successfully adopt Hadoop-based BI solutions, generating valuable insights for optimal growth.

Here are some major challenges associated with implementing BI on Hadoop-

  1. Data Integration- As Hadoop-based BI solutions often require integrating multiple data sources, data integration can be challenging. Data from different sources often come in different formats and must be transformed and merged to ensure consistency. For example, a retail company may need to integrate data from their point of sale, inventory management, and customer relationship management systems to view their sales and customer behavior comprehensively.

  2. Poor Data Quality- Hadoop-based BI solutions store and process large volumes of data, and ensuring the quality of that data is accurate and complete can be a challenge. Data may need to be cleaned, deduplicated, and transformed before it can be analyzed. For example, a healthcare organization may need to clean and standardize patient data before using it for analysis to avoid incorrect diagnoses or treatment.

  3. Scalability- Hadoop-based BI solutions are designed to scale horizontally, but managing and optimizing the cluster for scalability can be challenging. As the data volume and complexity increase, businesses must ensure the cluster is optimized for performance and resources are managed efficiently. For example, a transportation company may need to scale its Hadoop cluster to accommodate data from new routes and vehicles to optimize logistics operations.

  4. Lack Of Data Security- Data security is a critical consideration for any BI solution, and Hadoop-based solutions require robust security measures to protect sensitive data. This includes user authentication, data encryption, and access control. For example, a financial services company may need strict security measures to protect sensitive financial data from external and internal threats.

  5. Lack Of Professional Expertise- Building and maintaining Hadoop-based BI solutions require specialized expertise, which can be challenging to find and retain. This includes expertise in Hadoop, data analytics, and software development. For example, a government agency may need to build and maintain a Hadoop-based BI solution to manage and analyze data from various sources. Still, they may lack in-house expertise and need to hire external consultants.

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Best Practices For Implementing BI On Hadoop

Here are some best practices for maintaining and optimizing Hadoop-based Business Intelligence solutions-

  1. Regular Monitoring- Regularly monitor your Hadoop-based BI solution to identify performance issues and potential bottlenecks. Set up alerts and alarms to notify you of any issues that may arise. For example, a retail company might monitor their BI solution to ensure its real-time inventory updates function correctly.

  2. Capacity Planning- Plan for future growth by regularly assessing your storage and processing capacity needs. Plan and expand your infrastructure before you reach capacity to avoid performance issues. For example, a healthcare provider might perform capacity planning to ensure that their BI solution can handle an increasing amount of patient data.

  3. Security And Access Control- Ensure your Hadoop-based BI solution is secure by implementing access controls and monitoring user activity. You should use encryption and other security measures to protect sensitive data. For example, a healthcare organization can use Hadoop-based BI to analyze patient data but implement strict security measures to protect patient privacy.

  4. Data Quality Assurance- Ensure your data is clean, accurate, and consistent to avoid errors and inconsistencies in your BI reports. For example, a marketing agency can use Hadoop to analyze customer data but clean the data to ensure accurate insights.

  5. Query Optimization- Optimize your queries to reduce processing times and improve performance. Use tools and techniques like partitioning, indexing, and caching to optimize your queries. For example, a travel company might use query optimization to identify the most popular travel destinations among their customers quickly.

Real-World Examples Of BI On Hadoop

Below are some interesting real-world examples of organizations successfully implementing Hadoop-based BI solutions:

  1. Walmart- Walmart is a retail giant that generates massive amounts of daily data. The company uses Hadoop-based BI solutions to analyze data from its retail stores, distribution centers, and online operations. Walmart's Hadoop-based BI solution provides real-time insights into sales, inventory levels, and customer behavior. It enables the company to make data-driven decisions that optimize its supply chain and improve customer satisfaction.

  2. The New York Times- The NY Times is a leading newspaper that generates large volumes of data from its readership and online operations. The company uses Hadoop-based BI solutions to analyze this data and provide its editors and advertisers insights. The New York Times' Hadoop-based BI solution provides real-time insights into readership patterns, article popularity, and ad performance, enabling the company to optimize its content and revenue strategies.

  3. CERN- CERN is a European research organization that operates the Large Hadron Collider, the world's largest and most powerful particle accelerator. CERN generates enormous amounts of data from its experiments, which it uses to study the fundamental nature of matter and the universe. CERN uses Hadoop-based BI solutions to analyze this data and identify patterns and anomalies leading to several significant discoveries.

  4. American Express- American Express is a popular financial services organization that generates large amounts of data from credit card transactions and customer interactions. The company uses Hadoop-based BI solutions to analyze this data and identify fraud and other anomalies in real-time. American Express' Hadoop-based BI solution has helped the company reduce fraud losses, improve customer satisfaction, and increase revenue.

  5. LinkedIn- LinkedIn is a professional networking portal that generates huge amounts of data from user profiles and interactions. The company uses Hadoop-based BI solutions to analyze this data and provide insights to its users. LinkedIn's Hadoop-based BI solution provides personalized recommendations for job openings, people to connect with, and content to read based on users' interests and behavior.

These real-world examples demonstrate the potential of Hadoop-based BI solutions for various industries and use cases. Organizations can gain useful insights that improve their business operations, enhance customer experiences, and drive innovation by analyzing huge volumes of data in real time.

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Business Intelligence On Hadoop Projects For Practice

In the previous sections, you have learned how to implement business intelligence on Hadoop and a few real-world examples of using BI on Hadoop. It’s time for you to apply this knowledge to some interesting BI on Hadoop projects that involve using Hadoop and various business intelligence tools, such as Tableau, QlikView, etc.

Below are some useful Hadoop BI big data projects worth exploring by big data professionals to gain expertise in Hadoop and business intelligence (BI) tools-

In this big data project, you will analyze the Dallas Police Data using Hadoop, Hive, and Pig to find crime patterns. You will use the open dataset 'Dallas Police Data' for this project and join the dataset with various other demographic datasets to solve the problem statements. You will use HDFS, Hive, and Pig for storing, processing, and analyzing the data and Excel and Tableau for the visualizations.

Source Code- Dallas Police Data Analysis Using Hadoop And Tableau

In this big data project, you will collect nearly 20000 tweets, 500 articles on NY Times, and 500 articles on Common Crawl Data about Entertainment. You will perform data preprocessing and feed it to MapReduce to determine the Word Count and Word Co-Occurrence. You will use Python to perform Data Analysis. To visualize the data, you will use Tableau and sort the data in descending order of the count and take the Top 10 Words. Then, you will use these words to create a Word Cloud of the 10 words for all 6 Data Outputs.

Source Code- Big Data Analysis Using Hadoop MapReduce And Tableau

In this big data project, you will learn how to build an ETL Pipeline on Amazon EMR with AWS CDK and Apache Hive. You will deploy the pipeline using S3, Cloud9, and EMR and use Power BI to create dynamic visualizations of the transformed data. create an interactive dashboard on Power BI for dynamic visualization of the resultant transformed data.

Source Code- Build An ETL Pipeline On EMR Using AWS CDK And Power BI

Master Business Intelligence On Hadoop With ProjectPro

Business intelligence on Hadoop is a powerful combination that enables organizations and big data professionals to leverage the benefits of both technologies. Hadoop offers the ability to store, process, and analyze large amounts of data in a distributed environment, while business intelligence provides the tools for reporting, visualizing, and gaining insights from the data.  Learning business intelligence on Hadoop integration will help big data professionals gain the necessary skills to analyze and extract insights from huge volumes of data organizations generate today, enabling them to make strategic business decisions.

ProjectPro offers various Hadoop-based BI big data projects that enable professionals to work on real-world datasets and gain an in-depth understanding of the integration. By working on these end-to-end solved big data projects, big data professionals can explore different Hadoop data analysis tools, such as Hive, Impala, and Pig, and data visualization tools, like Tableau, Apache Zeppelin, and QlikView, etc. This real-world practical experience will give big data professionals a better understanding of the potential of this integration and how it can be used to solve specific business problems. Business intelligence on Hadoop is a game-changer for big data professionals. Working on real-world big data projects by ProjectPro is an excellent way to gain the necessary skills and experience to work with these technologies.

So, take your big data career to new heights with ProjectPro today!

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FAQs for BI On Hadoop

Hadoop is not a business intelligence tool but a powerful platform for storing and processing large volumes of data. Business intelligence tools can be built on top of Hadoop to analyze data and generate insights from the stored data.

The best BI tool for big data depends on different factors, such as the size and complexity of the data, the specific business use case, and the organization's budget and resources. Some popular BI tools for big data include Tableau, QlikView, Microsoft Power BI, and SAP BusinessObjects.

Hive is not a traditional BI tool but is often used as part of a BI solution. Hive is a data warehousing tool that lets users query and analyze large datasets stored in Hadoop, making it a crucial component of BI solutions.

Hadoop is an open-source distributed computing framework used in business intelligence to process and store large amounts of data. It enables organizations to gain valuable insights from their data and improve their operations and strategies.

 

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