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Currently, most businesses and big-scale companies are generating and storing a large amount of data in their data storage. Many companies are there which are entirely data-driven. Businesses and companies are using data to get insights about the progress and future steps for business growth. In this article, we will study the data lineage and its process, the significant reasons behind businesses investing in it, and the benefits of it, with its core intuition. This article will help one understand the whole data lineage process and its applications related to business problems.

What is Data Lineage?

Data lineage is a process of getting an idea about where the data is coming from, analyzing it, and consuming it. It reveals where the data has come from and how it has evolved through its lifecycle. It traces where the data was generated and the steps in between it went through. A clear flowchart for each step helps the user understand the entire process of the data lifecycle, which can enhance the quality of the data and risk-free data management.

Data lineage enables companies to track and solve problems in the path of the data lifecycle.

It provides a thorough understanding of the solutions to errors in the way of the data lifecycle with lower risk and easy solution methods.

It allows companies to combine and preprocess the data from the source to the data mapping framework.

Data lineage helps companies to perform system migration confidently with lower risk.

Data lineage tools help organizations manage and govern their data effectively by providing end-to-end data lineage across various data sources, enabling data discovery, mapping, and data lineage visualization, and providing impact analysis and data governance features.

Here are some of the top data lineage tools and their features:

1. Alation

Alation provides a unified view of data lineage across various data sources. It automatically tracks data changes, lineage, and impact analysis. It also enables collaboration among data users.

2. Collibra

Collibra provides end-to-end data lineage across various data sources. It enables data discovery, data mapping, and data lineage visualization. It also provides a business glossary and data dictionary management.

3. Informatica

Informatica provides data lineage across various data sources, including cloud and on-premise. It enables data profiling, data mapping, and data lineage visualization. It also includes impact analysis and metadata management.

4. Apache Atlas

Apache Atlas provides data lineage for Hadoop ecosystem components. It tracks metadata changes, lineage, and impact analysis for data stored in Hadoop. It also enables data classification and data access policies.


MANTA provides data lineage for various data sources, including cloud and on-premise. It enables data discovery, data mapping, and data lineage visualization. It also provides impact analysis and data governance features.

6. Octopai

Octopai provides automated data lineage for various data sources, including cloud and on-premise. It enables data discovery, data mapping, and data lineage visualization. It also includes impact analysis and data governance features.

Data Lineage Application Across Industries

Data lineage is a critical process across various industries. Here are some examples:

Healthcare: In the healthcare industry, data lineage is important for ensuring patient data privacy, tracking data lineage for medical trials, and tracking data for regulatory compliance.

Finance: Data lineage helps financial institutions comply with Basel III, Solvency II, and CCAR regulations. It also helps prevent financial fraud, risk management, and transparency in financial reporting.

Retail: In the retail industry, data lineage helps in tracking inventory levels, monitoring supply chain performance, and improving customer experience. It also helps in fraud detection and prevention.

Manufacturing: In manufacturing, data lineage tracks the production process and ensures the quality of the finished product. It helps identify improvement areas, reduce waste, and improve efficiency.

Government: Data lineage is critical for ensuring transparency and accountability. It supports regulatory compliance, public data management, and security.

Why Are Businesses Eager to Invest in Data Lineage?

Just the information about the source of the data is not enough to understand the importance of the data. Some preprocessing on data, error solution in between the path of data, and getting key insights from the data is also important for a business or company to focus on.

Knowledge about the source, updating of the data, and consumption of the data improves the quality of the data and helps businesses get an idea about further investing in it.

Profit Generation: For every organization, generating revenue is the primary need to grow the business. The information tracked from data lineage helps improve risk management, data storage, migration process, and hunting of some bugs in between the path of the data lifecycle, etc. Also, the insights from the data lineage process help the organizations understand the scope of profit and can generate revenue.

Reliance on the data: Good quality data always helps to keep the business running and improving. All the fields or departments, including IT, Human resources, and marketing, can be enhanced through data lineage, and companies can rely on data to improve and keep tracking things.

Better Data Migration: There are some cases where there is a need to transfer the data from one storage to another. The data migration process carries out very carefully as there is a high amount of risk involved in it. When the IT department needs to migrate the data, data lineage can provide all the information about the data for the soft data migration process.

How to Implement Data Lineage? Benefits of Data Lineage

There are some obvious benefits of the data lineage, which is why businesses are eager to invest in the same.

Some major benefits are listed below:

1. Better Data Governance

Data governance is the process in which data is governed, and analysis of the source of the data, the risk attached to it, data storage, data pipelines, and data migration is performed. Better data lineage can help conduct better data governance. Good quality of it can provide all this information about the data from its source to consumption and help achieve a better data governance process.

2. Better Compliance and Risk Management

Major data-driven companies have a huge amount of data, which is tedious to handle and keep organized. There are some cases where there is a need for data transformation or preprocessing data; during these types of processes, there is a huge risk involved lose the data. Better data lineage can help the organization keep the data organized and reduce the risk involved in the process of migration or preprocessing.

3. Quick and Easy Root Cause Analysis

During the entire data lifecycle, many steps are in between, and many bugs and errors are involved. With a good-quality data lineage, it can help businesses to find the cause of the error easily and solve it efficiently with less amount of time.

4. Easy Visibility of the Data

In a data-driven organization, due to a very high amount of data stored, it is necessary to have easy visibility of the data to access it quickly while spending less time searching for it. Good-quality data lineage can help the organization access the data quickly with easy data visibility.

5. Risk-free Data Migration

There are some cases where data-driven companies or organizations need the migrations of the data due to some errors occurring in existing storage. Data migration is a very risky and hectic process with a higher rate of data loss risk involved. It can help these organizations conduct a risk-free data migration process to transfer the data from one to another data storage.

Data Lineage Challenges Lack of Standardized Data Lineage Metadata 

It becomes difficult to track data lineage consistently across different systems and applications. Solution: Standardizing metadata and using common data models and schemas can help overcome this challenge.

Complex Data Architectures Data Lineage Gaps

There can be gaps in data lineage due to incomplete or inconsistent data, missing metadata, or gaps in the data collection process. Solution: Establishing a comprehensive data governance framework that includes regular data monitoring and auditing can help identify and fill data lineage gaps.

Data Lineage Security and Privacy Concerns

Data lineage information can be sensitive and require protection to avoid security and privacy breaches. Solution: Implementing appropriate security measures, such as data encryption and access controls, and complying with data privacy regulations can help to ensure data lineage security and privacy.

Lack of Awareness and Training

Lack of awareness and training among data stakeholders on the importance and use of data lineage can lead to limited adoption and usage. Solution: Providing training and awareness programs to educate data stakeholders on the importance and benefits of data lineage can help to overcome this challenge.

Data Lineage vs Other Data Governance Practices

Data lineage is a critical component of data governance and is closely related to other data governance practices, such as data cataloging and metadata management. However, data cataloging is the process of creating a centralized inventory of all the data assets in an organization. At the same time, metadata management involves creating and managing metadata associated with these assets.

Data lineage helps establish the relationships between data elements, sources, and flows and provides a clear understanding of how data moves throughout an organization. It complements data cataloging and metadata management by providing a deeper insight into data’s origin, quality, and usage.

While data cataloging and metadata management provide a high-level view of an organization’s data assets, data lineage provides a granular understanding of how data is processed, transformed, and used. Data lineage helps to identify potential data quality issues, track changes to data over time, and ensure compliance with regulatory requirements.

Data Mapping vs Data Lineage

Data MappingData LineageFocuses on identifying the relationships between data elements and their corresponding data sources, destinations, and transformations.Focuses on tracking the complete journey of data from its origin to its final destination, including all the data sources, transformations, and destinations in between.Primarily used to understand data flow between systems and applications.Primarily used to understand the history and lifecycle of data within an organization.Typically involves manual or semi-manual documentation of data chúng tôi be automated or semi-automated using tools and platforms that capture and track metadata.Often used for specific projects or initiatives, such as data integration or data chúng tôi for ongoing data governance and compliance efforts, as well as for specific projects.Helps ensure consistency and accuracy in data movement across systems.Helps ensure data quality and compliance with regulatory requirements by providing a clear understanding of data lineage.

Regulatory Compliance

Compliance with regulations like GDPR and CCPA requires companies to comprehensively understand their data.

Data lineage provides a detailed data usage history, making it easier to comply with regulations like GDPR and CCPA.

With data lineage, organizations can easily identify where data is being stored, who has access to it, and how it is being used.

By maintaining a clear data lineage, organizations can demonstrate compliance to regulatory bodies and provide evidence of their data privacy and security practices.

Data lineage can also help with compliance by enabling organizations to easily audit their data usage and identify areas that may be non-compliant.

Data lineage can be particularly useful in the case of data breaches, as it allows organizations to quickly identify what data was affected and take appropriate action to notify affected individuals and regulatory bodies.

Future of Data Lineage

Adoption by More Industries: As more industries recognize the importance of data governance, data lineage will become more widely adopted as a critical tool for ensuring regulatory compliance and data quality.

Increased Automation: Automation will play a more significant role in data lineage, reducing the amount of manual effort required to maintain data lineage and providing more timely and accurate data lineage information.

Integration with Machine Learning and AI: Data lineage will be integrated with machine learning and artificial intelligence to enhance its capabilities for data discovery, quality management, and governance.

Improved Interoperability: Improved interoperability between data lineage tools and other data management systems will allow for more comprehensive data governance across organizations.

Greater Emphasis on Security: With increased concerns about data breaches and cyber threats, data lineage will be essential in ensuring data security by tracking data access and providing visibility into how data is used.

Emergence of Blockchain-based Data Lineage: Blockchain technology is being explored to provide more secure and transparent data lineage by creating an immutable record of data transactions.

Way Ahead

Data lineage is a crucial step for any organization that deals with data. By implementing data lineage, companies can achieve better data governance, manage risks more effectively, and gain easy access to data. Top companies like Netflix, Google, and Microsoft have already embraced data lineage and have significantly benefited from it. So, if you want to learn more about data lineage and other essential data skills, consider enrolling in our Blackbelt program. It’s a comprehensive program that will help you become an expert in data science and analytics.

Frequently Asked Questions

Q1. What is data lineage in ETL?

A. Data lineage in ETL refers to the complete end-to-end history of the data from its source to destination, including transformations and metadata changes.

Q2. What are the two types of data lineage?

A. The two types of data lineage are forward lineage and backward lineage. Forward lineage tracks data flow from source to destination, and backward lineage tracks data flow from destination to source.

Q3. What is data governance and data lineage?

A. Data governance is a process of managing data quality, security, and compliance, while data lineage is a part of data governance that tracks the data flow across the organization.

Q4. What is the difference between data mapping and data lineage?

A. Data mapping involves associating source data with target data, while data lineage tracks the flow of data and metadata across various systems.

Q5. What is data lineage of a dataset?

A. Data lineage of a dataset refers to the origin of the data, its transformations, and the places where it has been stored or used.

Q6. Is data lineage a metadata?

A. Yes, data lineage is a type of metadata that provides information on the movement and transformation of data across different systems.


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What Is It Like To Be A Data Scientist In 2023?


The rise in demand for data scientist continues in 2023 too

Understand what is it like to be a Data Scientist in 2023


From big E-commerce companies like Amazon, Walmart, to social media giants Facebook and Snapchat – all the way up to hospital management – everyone is hiring data scientists! But what is it that makes this role the “Sexiest Job Role of the 21st century”? We will discuss each and every aspect of this job in this article.

If you are someone that is excited by this job role and wants to create a future in this field in 2023 then this is the place to be! Don’t worry if you think that the coronavirus has killed the job requirement of a data scientist instead it has made everyone realize the power and importance of predictive algorithms!

If you are beginning your journey in the field of data science, this comprehensive data science learning path for 2023.

The learning path for 2023 is the ultimate and most comprehensive collection of resources put together in a structured manner. This learning path is for anyone who wants to make a career in data science. So whether you are a fresher, have a few years of work experience, or are a mid-level professional – this data science learning path is for you. 

Table of Contents

Who is a data scientist?

Other Data-based roles

Qualities of a data scientist

What skills to master in 2023 to become a data scientist?

Salary of a data scientist

Who is a data scientist?

Data science is a combination of data analysis, algorithmic development and technology in order to solve analytical problems.

A data scientist works on complex and specific problems to bring non-linear growth to the company. For example, making a credit risk solution for the banking industry or use images of vehicles & assess the damage for an insurance company automatically.

In simple words, a data scientist is a problem solver who uses data to solve problems that create business value.

A typical data science project lifecycle looks like this:

Converting the business problem into a data problem

Hypothesis generation

Data collection or extraction

Exploratory Data Analysis and validating hypotheses

Data modeling

Model deployment

Presenting your work to the final user/client/stakeholder

But a data scientist may not be involved in all of these steps. Let’s look at some of the data science-based roles.

Other Data-Based Roles Data Engineer

He would Implement the outcomes derived by the data scientist in production by using industry best practices. For example, Deploying the machine learning model built for credit risk modeling on banking software.

Data Engineers are responsible for storing, pre-processing, and making this data used for other members of the organization. They create the data pipelines that collect the data from multiple resources, transform it, and store it in a more usable form.

Some of the most commonly used tools by data engineers are SQL, NoSQL databases, Apache Airflow, Spark, Amazon Redshift, etc.

You can read Data Engineering articles here and see if your interests correlate more to data engineering.

Business Analyst

Run the business and take decisions on a day-to-day basis. He’ll be communicating with the IT side and the business side simultaneously.

Business Analytics professionals must be proficient in presenting business simulations and business planning. A large part of their role would be to analyze business trends. For eg, web analytics/pricing analytics.

Some of the tools used extensively in business analytics are Excel, Tableau, SQL, Python. The most commonly used techniques are – Statistical Methods, Forecasting, Predictive Modeling, and storytelling.

You can read the business analytics articles here.

So you think that you can become a data scientist? Let’s looks at some of the qualities of a data scientist!

Qualities of a data scientist

Before choosing data science as your field, you must see if it matches your passions, career goals, and make sure it makes you happy in the long term. Let us look at a few of them –

Love Number Crunching – Are you crazy about numbers? Like, are you up for a puzzle, guess-estimates at any time of the day? Are you naturally attracted to probability and statistics? Part of being a data scientist is to frequently crunch numbers, if you love it then you are in luck!

Enjoy solving unstructured problems – It is very rare that a data scientist actually encounters a structured problem statement, instead he deals with unstructured data. Are you someone that aces in this area?

You are curious – asking why comes naturally to a good data scientist. Some of the best data scientists would stop anyone and ask for a rationale if they are not clear – Why did you ask this question? What was your thought process? Why do you assume so? are just a few examples of these questions!

Crave problem-solving – Data Scientists require a knack for problem-solving. Most of the problems businesses would face would be unique to them and it would take a smart solver to solve them.

Enjoy deep research – A great data scientist is always digging deep to understand the hidden secrets of data. You need an outlook of a researcher to be a good data scientist. When was the last time you spent hours and hours immersed in solving a problem? Can you do that again and again?

Love telling Stories – A data scientist needs to be a fluid presenter. What is the use of all the hard work, if he is not able to influence his stakeholders? Communicating with data and presenting stories backed by data is one of the most important elements in the life of a data scientist.

What skills to master in 2023 to become a data scientist?

Data Science Toolkit –  The most important skill to gain at the beginning of your journey as a data scientist is the basics of data science and machine learning. Start from the most common and frequently used data science tools – Python and its libraries such as Pandas, NumPy, Matplolib, and Seaborn. 

Data Visualization and SQL – As you have cleared the basics, you need to begin with the most crucial skillset of a data scientist. Familiarize yourself with different data visualization tools and techniques such as Tableau. During this time, you should also begin your SQL journey.

Data Exploration – The data is hidden with important information. Bringing out this information in the form of insights is data exploration. It is the most essential skill to learn how to explore your data with Exploratory Data Analysis (EDA). Along with this, you will also need to understand the important concepts of statistics required to become a data scientist. 

Basics of Machine Learning and the art of storytelling – Now let’s get down to actual machine learning! After gaining all the above skills, it’s time for to you start your Machine Learning journey. In this duration, you will need to cover basic ML techniques and the art of storytelling using Structured thinking. 

Unsupervised Machine Learning – Dealing with unstructured data can be challenging so let’s jump into the solution! It is time for you to learn about unsupervised machine learning algorithms like K-Means, Hierarchical Clustering, and finally deep dive into a project!

Recommendation engines – Curious how Netflix, Amazon, Zomato give such amazing recommendations? It is time for you to delve into recommendation systems. Learn different techniques to build recommendation engines. Learn using different projects.

Working with Time Series Data – Organizations around the world depend heavily on time-series data and machine learning has made the scenario even more exciting. In this duration, you will learn how to work with Time Series data and different techniques to solve time series related problems.

Introduction to Deep Learning and Computer Vision – Deep Learning and Computer Vision is at the forefront of the most happening projects in the field of AI be it Self driven cars, mask detection cameras, and more. In this time, you will start your journey in the field of Deep Learning. You will learn basic deep learning architectures and then solve different computer vision projects.

Basics of Natural Language Processing – Do you wonder how Social media giants like Twitter, Facebook, Instagram process incoming text data? It is time to move your focus to the field of Natural Language Processing (NLP). Here you will learn more deep learning architectures and solve NLP related projects.

Model Deployment – What is more essential than building a data science model? Deploying it! Now finally you must be aware of model deployment. Learn different ways to deploy your models. You’ll get to spend time on exploring streamlit for model deployment, AWS, and also get to deploy the model using Flask.

A Data scientist’s salary

Making a career switch to data science for getting a salary bump is entirely justified. However, it isn’t as straightforward as you might think. There are certain things, such as work experience and your current domain, that will play a MASSIVE role in deciding your salary post-transition.

Taking figures from the popular and relatively accurate website called Glassdoor, this is what the salary situation looks like for a data scientist:

As you can see, the average salary in 2023 is approximately INR 10,00,000 per year.

If you bring a bit more experience to the table and you have relevant domain experience, you might look at a more senior role (though this is a bit rare if you have no prior data science experience):

As we said, it comes down to how relevant your previous experience is. More often than not, if you are transitioning from another role to data science, you’ll be looking at the first graph.

End Notes

To summarize, Data Science is the most emerging field today and data scientists are creating a better future for humanity. Are you someone that is attracted to this field? I have mentioned all the things you must know before building a career in data science in the year 2023.

Happy Learning!


What Are Schemas In Data Warehouse Modeling?

This article was published as a part of the Data Science Blogathon.


Do you think you can derive insights f It’s possible, of course, but it can be tiresome and not be as accurate as it should be. Wouldn’t the process be much easier if the raw data were more organized and clean? Here’s when Data warehousing comes in handy. It is the process of constructing a data warehouse containing essential data. We need to archive and store the data for future use. ETL (Extract, Transform and Load) turns raw data into information. Through this article, let’s understand schemas and their role in data warehouse modeling.

Data Warehouse

A Data warehouse is a digital location to store data from many sources such as databases and files. To solve a business question and make data-driven decisions, we need to mine the data. We do this through this central data repository to get insights and generate reports. It works based on OLAP (Online Analytical Processing). As a result, it is a location to store an organization’s historical and archived data. It is also the single source of truth. All the required information (organized data) is present in a single place. It helps to answer a detailed-oriented question and find trends in historical data.

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Data Modeling

Before building a building, we first need to create its design and make a model. In the same way, to create a data warehouse, we need to design it first using data warehouse modeling tools and techniques. We do this to represent the data in the real world and see how business concepts relate. Data warehouse modeling is the process of designing the summarized information into a schema.


Schema means the logical description of the entire database. It gives us a brief idea about the link between different database tables through keys and values. A data warehouse also has a schema like that of a database. In database modeling, we use the relational model schema. Whereas in the data warehouse, we use modeling Star, Snowflake, and Galaxy schema.

To get a good understanding of how a Schema looks. Let’s look at an example Schema of the top_terms table. It is from the google_trends database in Google BigQuery.

Key Concepts of Schemas

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Primary Key – An attribute in a relational database having unique values. There are no duplicate values. We identify each record with its unique value. In the above example, Stud_id is the primary key. It is because each student will have only one unique id.

Foreign Key – An attribute in a relational database that links one table to another. It refers to the primary key from another table. In the above example, Stud_id is the foreign key in the department table. It is because it was the primary key in the student table. We link the student and the department table together via joins.

Dimensions – Dimensions are the column names in a dimension table. Also, dimensions have their attributes sub-divided in the table. We use dimensions as a structured way of describing and labelling the information. Dimension tables are the tables describing dimensions. Example: Date, products, and customers are some common dimensions.

Measures – Quantitative attributes in the fact table. We perform calculations like average and sum on them. Example: No. of products, discount.

Fact Table – A fact table contains a dimension key from the dimension table and measures. The measures here are to perform calculations for analysis. The dimension key and measures describe the facts of the business processes. A fact table consists of measurements of our interests. Example: Product_id, Date_id, No. of products.

Schema Definition

Data Mining Query Language (DMQL) defines Multidimensional Schema. Using a multidimensional schema, we model data warehouse systems. Cube definition and dimension definition are the two primitives. This is because we view data in the form of a data cube. They help to define data warehouses and data marts.





define dimension as () Types of Schemas

There are three main types of data warehouse schemas :

Snowflake Schema

Galaxy Schema

Star Schema

Star Schema is the easiest schema. It has a fact table at its centre linked to dimension tables having attributes. It is also called as Star-Join Schema. It has a primary and foreign key relationship between the dimension table and the fact table. It is de-normalized means the normalization is not done as it is for relational databases. Its characteristic is that we represent each dimension with only a one-dimension table. Example: The Fact_Sales table has Date_id, Store_id, and Product_id as the dimension keys. These keys link to only one dimension table per key.

In the diagram below, Fact_Sales is the fact table. Dim_Date, Dim_Store, and Dim_Product are the dimension tables. Id, Store_Number, State_Province, and Country are the attributes of the dimension table Dim_Store. In the same way, other dimension tables have their attributes.


1. Most Suitable for Query Processing: View-only reporting applications show enhanced performance.

2. Simple Queries: Optimized Navigation through the database. It is because the star-join schema logic is much simpler.

3. Simplest and Easiest to design.


1. They don’t support many to many relationships between business entities.

2. More data redundancy: It is a result of each dimension having only one dimension table.


define cube Fact_Sales_star [Dim_Date, Dim_Store, Dim_Product]:Units_Sold = count(*) define dimension Dim_Date as (Date_Id, Date, Day, Day_of_Week, Month, Month_Name, Quarter, Quarter_Name, Year) define dimension Dim_Store as (Store_Id, Store_Number, State_Province, Country) define dimension Dim_Product as (Product_Id, EAN_Code, Product_Name, Brand, Product_Category)

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Snowflake Schema

It is an extended version of the star schema where dimension tables are sub-divided further. It means that there are many levels of dimension tables. It is because of the normalized dimensions here. Normalization is a process that splits up data to avoid data redundancy. This process sub-divides the tables and the number of tables increases. The Snowflake schema is nothing but a normalized Star schema.

The following diagram shows Dim_Store has Id, Store_Number, and Geography_Id as its attributes. There is a link between Geography_Id and the Dim_Geography dimension table. The Dim_Geography dimension table has Id, State_Province, and Country as its attributes. In the same way, Dim_Date and Dim_Product are normalized.


1. Easy to maintain: It is due to reduced data redundancy.

2. Saves Storage space: Dimension tables are easier to update.


1. Complex Schema: Source query joins are complex.

2. Query Performance is not so good: because of the complex queries.


define cube Fact_Sales_snowflake [Dim_Date, Dim_Store, Dim_Product]:Units_Sold = count(*) define dimension Dim_Date as ( Date_Id, Date, Day, Dim_Day_of_Week (Day_of_Week_Id, Day_of_Week), Dim_Month (Month_Id, Month_Name), Dim_Quarter (Quarter_Id, Quarter_Name), Year ) define dimension Dim_Store as ( Store_Id, Store_Number, Dim_Geography (Geography_Id, State_Province, Country) ) define dimension Dim_Product as ( Product_Id, EAN_Code, Product_Name, Dim_Brand (Brand_Id, Brand), Dim_Product_Category (Product_Category_Id, Product_Category) )

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Galaxy Schema

It consists of more than one fact table linked to the dimension tables having attributes. It is also called a fact constellation schema. Conformed dimensions are the dimension tables shared with the fact tables. We can normalize the dimensions in this schema further, but it will lead to a more complex design.

The following diagram shows Placement and Workshop as the two fact tables present. And the dimension table, Student, and TPO are the conformed dimensions.


1. Flexible schema.

2. Effective analysis and reporting.


1. Has huge dimension tables hence resulting in difficulty in managing.

2. Hard to maintain: It is because of their complex design and as there are many fact tables.


define cube Placement [Student, TPO, Company]:No. of students eligible = count(eligible_students), No. of students placed = count(placed_students) define dimension Student as (Stud_roll, Name, CGPA) define dimension TPO as (TPO_id, Name, Age) define dimension Company as (Company_id, Name, Offer_Package) define cube Workshop [Student, TPO, Training Institute]:No. of students selected = count(selected_students), No. of students attended = count(attended_students) define dimension Student as Student in cube Placement define dimension TPO as TPO in cube Placement define dimension Training Institute as (Institute_id, Name, Full_course_fee)

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In this article, we learned about what schemas are, their different types, and their role in data warehouse modeling. There were some key concepts such as what is a primary key, foreign key, and fact tables. They play an important role in developing an understanding of schemas. Schemas help to see how business concepts relate by designing data models. Hence, they play a huge role in turning raw data into information.

Some of the key takeaways are as follows:

1. Schemas help define relationships between different database tables. A primary key-foreign key relationship forms the link.

2. Normalization and the number of fact tables define what type of schema to form.

3. We view the data in the form of a data cube.

End Notes

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What Is Stakeholder Management? What Is Its Role In Leadership?

blog / Leadership What is Stakeholder Management? What is its Role in Leadership?

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Are you managing a project that involves different people who may be impacted by its results? If so, let’s get into what is stakeholder management, and why it is important, let us begin by first understanding who a stakeholder is. 

A stakeholder refers to an individual, group, or organization with a ‘stake’ in the outcome of a particular project. They could be board members, investors, suppliers, or anyone who may be directly involved in a project and be impacted by its outcome. 

What is Stakeholder Management

It is the practice of identifying, analyzing, and prioritizing relationships with internal and external stakeholders who are directly affected by the outcome of a venture or project. It involves proactively implementing the right actions to build trust and foster better communication with multiple stakeholders. 

ALSO READ: What is Project Management and How to Become a Successful PM?

Why is Stakeholder Management Important?

According to Pulse of the Profession (PMI) 2023, 63% of companies have already integrated stakeholder engagement strategies. After all, it enables a deep understanding of stakeholders by establishing trust and strengthening interpersonal communication. Thereby ensuring that all stakeholders have a shared, similar understanding of the organization’s key goals and work together to fulfill these objectives. The main benefits are:

Ensures robust risk management

Creates a strong base for social license 

Aligns project concepts with business goals 

Supports conflict management 

Improves business intelligence 

ALSO READ: 7 Leadership Skills for Managers in Today’s Workplace

What are the Different Types of Stakeholders?

Internal stakeholders work within the organization and are directly invested in the project’s performance. For example, a company’s employees, top management, team members, and board of directors can all be considered internal stakeholders. 

External stakeholders may not be directly employed at the company or engaged with it but are impacted by the project in some way. Customers, shareholders, creditors, and suppliers are a few examples of external stakeholders. 

Stakeholder Management Examples

Looking at an example will help answer the ‘what is stakeholder management’ question. 

Let’s assume a government agency is working on developing a new policy. While refining a policy or developing a new one, there could be competing interests and varied opinions. Local councils, community groups, or certain businesses may not be supportive of this change. This is where stakeholder management can play a transformative role. Through effective stakeholder management, one can engage with these groups, find common ground, and address key changes that will enable a smooth decision-making process. 

What is a Stakeholder Management Plan?

It is a document that outlines core management techniques to effectively understand the stakeholder landscape and engage them throughout the project lifecycle. A stakeholder management plan usually includes: 

All the project stakeholders and their basic information

A detailed power interest matrix or a stakeholder map

The main strategies and tactics that are best suited to key stakeholder groups 

A well-laid-out communication plan

A clear picture of the resources available (budget, expertise, etc.)

Once you get to know what stakeholder management is really about, it’s essential to understand how to create an effective stakeholder management plan. 

How to Make a Stakeholder Management Plan?

Typically, a project manager is responsible for creating a stakeholder management plan. However, it is ideal also to involve all the project members to ensure accuracy. These are some steps to be followed while creating a stakeholder management plan: 

1. Identify Stakeholders

Conduct stakeholder analysis to identify key stakeholders and how they can impact the project’s scope.

2. Prioritize Stakeholders

Learn which stakeholders have influence over what areas of the project. This can be done by creating a power interest grid—a matrix that helps determine the level of impact a stakeholder has on the project. 

3. Establish a Communication Plan

It must include the type of communication, frequency, format, and distribution plan for communicating with each stakeholder. 

4. Manage Expectations

Develop dedicated timelines and share them with individual stakeholders to ensure the project is managed smoothly and also remains true to the stakeholders’ expectations. 

5. Implement the Plan

Make sure that all stakeholders have the final management plan before it is implemented. This helps build trust among teams and promotes transparency. It is also important to track the accuracy of the stakeholder management plan and make any changes based on the overall requirement. 

Stakeholder Management Principles

Now that you have a clear picture of what is stakeholder management, let’s take a look at the Clarkson Principles of Stakeholder Management. Max Clarkson, after whom these principles were named, was a renowned stakeholder management researcher. 

First Principle: Actively monitor and acknowledge the concerns of stakeholders and consider their interests throughout operations and decision-making processes. 

Second Principle: Have open and honest communication with stakeholders regarding any concerns, contributions, or risks that they may assume because of their association with the project.

Third Principle: Adopt practices and behaviors that are considerate toward the capabilities and concerns of all stakeholders. 

Fourth Principle: Recognize the efforts of stakeholders and ensure fair distribution of burdens and benefits of corporate activities while taking potential risks into consideration. 

Fifth Principle: Ensure cooperation with public and private entities to minimize risk from corporate activities. 

Sixth Principle: Avoid any activity that could potentially threaten stakeholders or jeopardize human rights.

Seventh Principle: Acknowledge any conflicts between the project manager and stakeholders. Such conflict should be addressed with open communication and reporting wherever required. 

Stakeholder Management Process

The process is simple to understand once you have in-depth knowledge about what is stakeholder management. These are the five main steps involved: 

Stakeholder Identification

It involves outlining key stakeholders and segregating them into internal and external stakeholder groups. 

Stakeholder Mapping

Once the list of stakeholders is segregated, you can analyze the stakeholders based on their level of influence, involvement, and importance vis-à-vis the project. 

Stakeholder Strategy

Since strategies are formed based on individual stakeholder groups in order of influence, this is your next important step. It defines the type of communication relevant to each stakeholder. 

Stakeholder Responsibility

It is essential to determine which team or individual should be responsible for which aspect of stakeholder engagement is essential. A stakeholder communication plan or template can be of great help here. 

Stakeholder Monitoring

Decide how to track stakeholder activities and integrate changes with ease. This may also involve using related software to boost convenience. 

ALSO READ: How to Develop Leadership Skills in Employees

Stakeholder management plays a vital role in leadership as it enables leaders—or managers in the case of projects—to identify and assess stakeholders’ expectations with a vested interest in a project. They do so by ensuring that everyone involved has a common understanding of the goals and objectives. Furthermore, it enables them to effectively manage any potential conflicts between stakeholders. 

By Neha Menon

Write to us at [email protected] 

Why Is Java Important For Big Data?

Big data refers to extremely large and complex data sets that traditional data processing software and tools are not capable of handling. These data sets may come from a variety of sources, such as social media, sensors, and transactional systems, and can include structured, semi-structured, and unstructured data.

The three key characteristics of big data are volume, velocity, and variety. Volume refers to a large amount of data, velocity refers to the speed at which the data is generated and processed, and variety refers to the different types and formats of data. The goal of big data is to extract meaningful insights and knowledge from these data sets that can be used for a variety of purposes, such as business intelligence, scientific research, and fraud detection.

Why is Java needed for Big Data?

Java and Big Data have a fairly close relationship and data scientists along with programmers are investing in learning Java due to its high adeptness in Big Data.

Java is a widely-used programming language that has a large ecosystem of libraries and frameworks that can be used for big data processing. Additionally, Java is known for its performance and scalability, which makes it well-suited for handling large amounts of data. Furthermore, many big data tools such as Apache Hadoop, Apache Spark, and Apache Kafka are written in Java and have Java APIs, making it easy for developers to integrate these tools into their Java-based big data pipelines.

Here are some key points we should investigate where Java’s importance can be mentioned cut-shortly;

Performance and Scalability

Java is known for its performance and scalability, which makes it well-suited for handling large amounts of data.

Java APIs

Many big data tools such as Apache Hadoop, Apache Spark, and Apache Kafka are written in Java and have Java APIs, making it easy for developers to integrate these tools into their Java-based big data pipelines.


Java is platform-independent, meaning that the same Java code can run on different operating systems and hardware architectures without modification.

Support and Community

Java has a large and active community of developers, which means that there is a wealth of resources, documentation, and support available for working with the language.

Prime Reasons Why Data Scientists Should Know Java

Java is a popular language for big data scientists because it is highly scalable and can handle large amounts of data with ease. Data science has heavy requirements, and being the top 3 listed programming languages Java can meet the requirements easily. With active Java Virtual Machines around the globe and the capability to scale Machine Learning applications, Java offers scalability to Data science development.

Widely-used big Data Frameworks Large Developer Community

Java has a large developer community, which means that there is a wealth of resources available online for learning and troubleshooting. This makes it easy for big data scientists to find answers to questions and learn new skills, which can help them quickly and effectively solve problems that arise during data science development.


Java is platform-independent and can run on a variety of operating systems and architectures, which makes it a great choice for big data scientists who may need to develop applications that run on different platforms.


In short, Java is a powerful and versatile language that is well-suited for big data development, thanks to its scalability, wide use of big data frameworks, large developer community, portability, and familiarity in the industry. It is a language that big data scientists should consider learning to excel in the field.


In conclusion, Java is a powerful and versatile language that is well-suited for big data development. Its scalability, ability to handle multithreading and efficient memory management makes it an excellent choice for handling large amounts of data.

Additionally, Java is the primary language for many popular big data frameworks, such as Hadoop and Spark, which provide pre-built functionality for common big data tasks. The large developer community also means that there is a wealth of resources available online for learning and troubleshooting. Furthermore, Java is platform-independent, which makes it a great choice for big data scientists who may need to develop applications that run on different platforms.

What Is Shell In Linux?

Introduction to Shell in Linux

Linux is a code that transmits the system commands., Compilers, Editors, linkers, and command-line interpreters are essential and valuable but are not part of the operating system. We will look briefly at the LINUX command interpreter, called the SHELL, which, although not part of the operating system, makes heavy use of many operating system features and thus serves as an excellent example of how the system calls can be used. It is also the primary interface between a user sitting at his terminal and the operating system.

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Following are the different examples:

It prints the current date and time.



The user can specify that the standard output be redirected to a file,


The user can specify that standard input can be redirected, as in


Which invokes the sort program with input taken from file1 and output sent to file2

The pipe helps connect a particular program’s output as input to other programs.


This invokes the cat program to concatenate three files and send the output to sort to arrange all the lines alphabetically. The output of sort is redirected to the file /dev/lp, a familiar name for the special character file for the printer.

Types of Shell

If you wish to use any of the above shell types as the default shell, the variable must be assigned accordingly. However, after reading a field in the file /etc./passwd, the system makes this assignment. This file must be edited if you wish to change the setting permanently. The system administrator usually sets up your login shell while creating a user account, though you can change it whenever you request.

Shell Keywords

Echo if until trap

read else case wait

set fi esac eval

unset while break exec

read-only do continue ulmit

shift Done exit umask

export For return

1. Unchanging variables- set keyword

In some applications, a need for variables to have a constant or fixed value may arise. For instants, if we want that a’s variable should always remain at 20 and not change, we can achieve this by saying,

Example #1

$a = 20 $readonly a

The shell will not permit to change a value when they are created as read-only. To create read-only variables, type “read-only” at a command prompt.

When there is a need to clear or erase a particular command from the shell, we will use the “unset” keyword as a command.

Example #2

$a = 20 $echo a 20 $unset a $echo a  2. Echo keyword

To print either the value of any variable or words under double quotation.

Example #1

x=20 echo $x


Example #2

echo "Hello World!"


Pwd command



Ls command

mkdir newdir ls


Mkdir command

mkdir imp ls


3. Cd command: read keyword

The read statement is the shell’s internal tool for taking input from the standard input. Functionally, it is similar to the INPUT statement of BASIC and the scanf() function in C. But it has one or two interesting features; it can be used with one or more variables to make shell scripts interactive. These variables read the input supplied through the standard input during an interactive session. The script chúng tôi uses the statement to take the search string and the filenames from the terminal.

Command – Shell in Linux $cat #Script : chúng tôi - Interactive version #The pattern and filename to be supplied by the user echo "nEnter the pattern to be searched : c" read pname echo "nEnter the file to be used :c" read flname echo "nSearching for $pname from the $flnamen" grep "$pname" $flname echo "nSelected records shown above" $_ Run it, and specify the input accordingly $ Enter the pattern to be searched: director Enter the file to be used: emp2.lst Searching for director from file emp2.lst


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