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GSE: Datamart Design and Build

The Problem

A government-sponsored enterprise needed a centralized data solution for its forecasting process, which involved cross-functional teams from different business lines.​

The firm also sought a cloud-based data warehouse to host forecasting outputs for reporting purposes with faster querying and processing speeds.​

The firm also needed assistance migrating data from legacy data sources to new datamarts. The input and output files and datasets had different sources and were often in different formats. Analysis and transformation were required prior to designing, developing and loading tables.  

The Solution

RiskSpan built and now maintains a new centralized datamart  (in both Oracle and Amazon Web Services) for the client’s  revenue and loss forecasting processes. This includes data modeling, historical data upload, and the monthly recurring data process.

The Deliverables

• Analyzed the end-to-end data flow and data elements​
• Designed data models satisfying business requirements​
• Processed and mapped forecasting input and output files​
• Migrated data from legacy databases to the new sources ​
• Built an Oracle datamart and a cloud-based data warehouse (Amazon Web Services) ​
• Led development team to develop schemas, tables and views, process scripts to maintain data updates and table partitioning logic​
• Resolved data issues with the source and assisted in reconciliation of results

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8 Week SQL Challenge

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• Case Study #5 - Data Mart

Danny Ma · June 20, 2021

Introduction

Data Mart is Danny’s latest venture and after running international operations for his online supermarket that specialises in fresh produce - Danny is asking for your support to analyse his sales performance.

In June 2020 - large scale supply changes were made at Data Mart. All Data Mart products now use sustainable packaging methods in every single step from the farm all the way to the customer.

Danny needs your help to quantify the impact of this change on the sales performance for Data Mart and it’s separate business areas.

The key business question he wants you to help him answer are the following:

• What was the quantifiable impact of the changes introduced in June 2020?
• Which platform, region, segment and customer types were the most impacted by this change?
• What can we do about future introduction of similar sustainability updates to the business to minimise impact on sales?

Available Data

For this case study there is only a single table: data_mart.weekly_sales

The Entity Relationship Diagram is shown below with the data types made clear, please note that there is only this one table - hence why it looks a little bit lonely!

Column Dictionary

The columns are pretty self-explanatory based on the column names but here are some further details about the dataset:

• Data Mart has international operations using a multi- region strategy
• Data Mart has both, a retail and online platform in the form of a Shopify store front to serve their customers
• Customer segment and customer_type data relates to personal age and demographics information that is shared with Data Mart
• transactions is the count of unique purchases made through Data Mart and sales is the actual dollar amount of purchases

Each record in the dataset is related to a specific aggregated slice of the underlying sales data rolled up into a week_date value which represents the start of the sales week.

Example Rows

10 random rows are shown in the table output below from data_mart.weekly_sales :

Interactive SQL Instance

You can use the embedded DB Fiddle below to easily access these example datasets - this interactive session has everything you need to start solving these questions using SQL.

You can click on the Edit on DB Fiddle link on the top right hand corner of the embedded session below and it will take you to a fully functional SQL editor where you can write your own queries to analyse the data.

You can feel free to choose any SQL dialect you’d like to use, the existing Fiddle is using PostgreSQL 13 as default.

Serious SQL students will have access to the same relevant schema SQL and example solutions which they can use with their Docker setup from within the course player!

Case Study Questions

The following case study questions require some data cleaning steps before we start to unpack Danny’s key business questions in more depth.

1. Data Cleansing Steps

In a single query, perform the following operations and generate a new table in the data_mart schema named clean_weekly_sales :

Convert the week_date to a DATE format

Add a week_number as the second column for each week_date value, for example any value from the 1st of January to 7th of January will be 1, 8th to 14th will be 2 etc

Add a month_number with the calendar month for each week_date value as the 3rd column

Add a calendar_year column as the 4th column containing either 2018, 2019 or 2020 values

Add a new column called age_band after the original segment column using the following mapping on the number inside the segment value

• Add a new demographic column using the following mapping for the first letter in the segment values:

Ensure all null string values with an "unknown" string value in the original segment column as well as the new age_band and demographic columns

Generate a new avg_transaction column as the sales value divided by transactions rounded to 2 decimal places for each record

2. Data Exploration

• What day of the week is used for each week_date value?
• What range of week numbers are missing from the dataset?
• How many total transactions were there for each year in the dataset?
• What is the total sales for each region for each month?
• What is the total count of transactions for each platform
• What is the percentage of sales for Retail vs Shopify for each month?
• What is the percentage of sales by demographic for each year in the dataset?
• Which age_band and demographic values contribute the most to Retail sales?
• Can we use the avg_transaction column to find the average transaction size for each year for Retail vs Shopify? If not - how would you calculate it instead?

3. Before & After Analysis

This technique is usually used when we inspect an important event and want to inspect the impact before and after a certain point in time.

Taking the week_date value of 2020-06-15 as the baseline week where the Data Mart sustainable packaging changes came into effect.

We would include all week_date values for 2020-06-15 as the start of the period after the change and the previous week_date values would be before

Using this analysis approach - answer the following questions:

• What is the total sales for the 4 weeks before and after 2020-06-15 ? What is the growth or reduction rate in actual values and percentage of sales?
• What about the entire 12 weeks before and after?
• How do the sale metrics for these 2 periods before and after compare with the previous years in 2018 and 2019?

4. Bonus Question

Which areas of the business have the highest negative impact in sales metrics performance in 2020 for the 12 week before and after period?

• demographic
• customer_type

Do you have any further recommendations for Danny’s team at Data Mart or any interesting insights based off this analysis?

This case study actually is based off a real life change in Australia retailers where plastic bags were no longer provided for free - as you can expect, some customers would have changed their shopping behaviour because of this change!

Analysis which is related to certain key events which can have a significant impact on sales or engagement metrics is always a part of the data analytics menu. Learning how to approach these types of problems is a super valuable lesson and hopefully these ideas can help you next time you’re faced with a tough problem like this in the workplace!

Ready for the next 8 Week SQL challenge case study? Click on the banner below to get started with case study #6!

Official Solutions

If you’d like to see the official code solutions and explanations for this case study and a whole lot more, please consider joining me for the Serious SQL course - you’ll get access to all course materials and I’m on hand to answer all of your additional SQL questions directly!

Serious SQL is priced at $49USD and $29 for students and includes access to all written course content, community events as well as live and recorded SQL training videos!

Please send an email to [email protected] from your educational email or include your enrolment details or student identification for a speedy response!

Community Solutions

This section will be updated in the future with any community member solutions with a link to their respective GitHub repos!

Final Thoughts

The 8 Week SQL Challenge is proudly brought to you by me - Danny Ma and the Data With Danny virtual data apprenticeship program.

Students or anyone undertaking further studies are eligible for a $20USD student discount off the price of Serious SQL please send an email to [email protected] from your education email or include information about your enrolment for a fast response!

We have a large student community active on the official DWD Discord server with regular live events, trainings and workshops available to all Data With Danny students, plus early discounted access to all future paid courses.

There are also opportunities for 1:1 mentoring, resume reviews, interview training and more from myself or others in the DWD Mentor Team.

From your friendly data mentor, Danny :)

All 8 Week SQL Challenge Case Studies

All of the 8 Week SQL Challenge case studies can be found below:

• Case Study #1 - Danny's Diner
• Case Study #2 - Pizza Runner
• Case Study #3 - Foodie-Fi
• Case Study #4 - Data Bank
• Case Study #6 - Clique Bait
• Case Study #7 - Balanced Tree Clothing Co.
• Case Study #8 - Fresh Segments

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Home / Blogs / What is a Data Mart? Design, Examples, and Implementation Explained

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What is a Data Mart? Design, Examples, and Implementation Explained

Unlike a data warehouse that stores enterprise-wide data, a data mart includes information related to a particular department or subject area. For instance, a sales data mart may contain data related to products, clients, and sales only. Read this blog to develop a better understanding of these departmental data repositories.

What are Data Marts?

Data marts are a subset of the data warehouse that deals with one single matter. They are often constructed and managed by a single business department. Since they are subject-oriented, they typically take data from only a small number of sources, which could be internal operational systems, data lake , a centralized data repository , or external sources. They are usually condensed and less intricate than data warehouses, which makes them easier to construct and maintain.

Now that we’ve understood what they are, we will look at the three different types of data mart examples, their uses, and how they simplify data management. We’ll also illustrate a step-by-step guide on how to implement a department-specific repository of data for your specific business.

Source: Study.com

How Do Data Marts Benefit Database Management?

Before we discuss their various types, let’s briefly look at the benefits of data marts and why they are necessary for a data-driven business:

• Enable faster data access by retrieving a specific set of data for BI and reporting. As a result, it helps accelerate business intelligence.
• They are easier to implement and more cost-effective than building an enterprise data warehouse .
• Designed according to the requirements of a particular group of users working in a specific department.
• They are comparatively more adaptable than a data warehouse. Any change in the data model can be easily and quickly incorporated into the data mart because of its smaller size.
• Allow granular access control rights because of extensive partitioning and segmentation.

In short, they are a lot faster, adaptable, and cost-effective to maintain than a data warehouse. By contrast, data warehouses are created for consolidating data from a myriad of sources (often not in a structured format).

Types of Data Marts

Data marts can be classified into three main types:

1. Dependent

A dependent data mart lets you combine all your business data into a single data warehouse , giving you the typical benefits of centralization.

In this example, departmental data stores are needed and you will have to build them as dependent entities to ensure consistency and integration across all data storage systems.

Dependent data marts can be constructed using two different approaches. In the first approach, enterprise data warehouses and data marts are built so the operator can access both whenever needed. In the second approach, also known as the federated approach, the results of the ETL process are stored in a temporary storage area such as a common data bus instead of a physical database so the operator can only access the departmental data.

The latter methodology is not ideal as it occasionally yields a data junkyard in which all data originates from a shared source, but it’s mostly discarded.

2. Independent

An independent data mart can be created without using the central data warehouse. It is mostly recommended for smaller units or groups within an organization. As the name suggests, this kind of repository is neither related to the enterprise data warehouse nor any other entity. It inputs data separately, and the analyses are also executed independently.

As more and more independent data marts are constructed, the data redundancy also increases across the organization. This is because every independent data store needs its own, usually a duplicate copy of the comprehensive business information. As these independent stores of data directly access files and/or tables of the operational system, they considerably limit the scalability of the decision support systems (DSS) .

By using a hybrid data mart, you can combine data from several operational source systems in addition to a data warehouse. These are particularly useful when you require ad hoc integration, such as adding a new group or products to the business.

As the name indicates, a hybrid data mart is a mixture of both the dependent and independent types. It is suitable for businesses that have multiple databases and need a quick turnaround. Data mart examples of this type need slight data cleaning, support huge storage structures, and are flexible as it combines the benefits of both dependent and independent systems.

Designing Data Marts for Data Warehousing

Here’s how you can go about designing a data mart for your enterprise needs:

The first step is to create a robust design. Some critical processes involved in this phase are:

• Collecting the corporate and technical requirements.
• Identifying data sources.
• Choosing a suitable data subset.
• Designing the logical layout (database schema) and physical structure.

2. Build/Construct

The next step in the process is to construct the data mart. This includes creating the physical database and the logical structures. In this phase, you’ll build the fact tables, dimension tables, fields, indexes, and access controls.

3. Populate/Data Transfer

The next step is to populate the mart, which means transferring data into it. In this phase, you can also set the frequency of data transfer, such as daily or weekly. This step usually involves:

• Extracting source information.
• Cleaning and transforming the data.
• Loading the data into the departmental repository.
• Building the metadata and storing it.

4. Data Access

In this step, the data loaded into the data mart is used in querying, generating reports, graphs, and publishing. The main tasks involved in this phase are:

• Setting up a meta-layer and translating database structures and item names into corporate expressions so that non-technical operators can easily use the data mart.
• Setting up and maintaining database structures.
• If necessary, you can also set up API and interfaces to simplify data access.

The last step involves management and observation, which includes:

• Controlling ongoing user access.
• Optimization and refinement of the target system for improved performance.
• Addition and management of new data into the repository.
• Configuring recovery settings and ensuring system availability in the event of failure.

Looking at The Future – Data Marts and the Cloud

Data marts offer an efficient, flexible, and scalable approach to storing data. However, on-premise solutions are quickly becoming obsolete due to the growing amounts of incoming data. As a result, many businesses are moving their data storage solutions to the cloud.

A cloud-based architecture enables businesses to create and store their data online. The speed and flexibility of this approach create an opportunity for analytics in the cloud. Performing analytics in the cloud allows for greater scalability and cost-efficiency compared to on-premise options. That’s not all. Cloud-based data marts offer other numerous benefits as well:

• Real-time data analytics.
• On-demand access to data.
• A unified view of all data marts.
• Access to cloud-native sources and destinations.

The Bottom Line

A data mart includes a subsection of enterprise-wide data, which is valuable to a particular user group in the organization. Unlike a data warehouse that’s expensive and complex to create, it offers a cost-efficient alternative. It also allows faster data access and is simple to use, as it’s precisely designed according to the operators’ requirements and focuses on a single department/subject area.

A data mart can help fast-track your corporate processes, as it takes less time to implement than a data warehouse. It also encloses past data so your data analysts can easily determine data trends.

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Data Mart solution

Client needed a three-tier architecture solution encompassing a source layer, reconciliation layer and data warehouse layer, Caprium provided a solution that involved the development of ETL jobs utilizing Talend to reconcile data from diverse source systems, leading to the creation of a centralized data repository. Additionally, the team delivered a Data Mart solution that facilitated the presentation of client-specific data within the Portal applications, ensuring seamless data consolidation and accessibility across various systems.

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Rethinking data marts in the cloud

Become more agile with business intelligence and data analytics.

Many of us are all too familiar with the traditional way enterprises operate when it comes to on-premises data warehousing and data marts: the enterprise data warehouse (EDW) is often the center of the universe. Frequently, the EDW is treated a bit like Fort Knox; it’s a protected resource, with strict regulations and access rules. This setup translates into lengthy times to get new data sets into an EDW (weeks, if not months) as well as the inability to do exploratory analysis on large data sets because an EDW is an expensive platform and computational processing is shared and prioritized across all users. Friction associated with getting a data sandbox has also resulted in the proliferation of spreadmarts , unmanaged data marts, or other data extracts used for siloed data analysis. The good news is these restrictions can be lifted in the public cloud.

A new set of opportunities for BI in the cloud

Business intelligence (BI) and analytics in the cloud is an area that has gained the attention of many organizations looking to provide a better user experience for their data analysts and engineers. The reason frequently cited for the consideration of BI in the cloud is that it provides flexibility and scalability. Organizations find they have much more agility with analytics in the cloud and can operate at a lower cost point than has been possible with legacy on-premises solutions.

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The main technology drivers enabling cloud BI are:

1.The ability to cost-effectively scale data storage in a single repository using cloud storage options such as Amazon S3 or Azure Data Lake Store (ADLS).

2.The ease that one can acquire elastic computational resources of different configurations (CPU, RAM, and so on) to run analytics on data combined with the utility-based cost model where you pay for only what you use. Discounted spot instances can also offer a unique value for some workloads.

3.An open and modular architecture consisting of analytic optimized data formats like Parquet and analytic processing engines such as Impala and Spark , allowing users to access data via SQL, Java, Scala, Python, and R directly and without data movement.

These capabilities make for an amazing one-two-three punch.

Because the cloud offers the ability to decouple storage and compute, all of an organization’s data can now live in a single place, thus eliminating data silos, and departments and teams can provision computes to run analytics for their use cases as needed. This new arrangement means self-service BI and analytics are a reality for those who adopt such a model. And with an open architecture, there are no worries about technology lock-ins.

Architecture patterns for the cloud

Now that we’ve discussed what technology options there are for BI in the cloud, what are the considerations an organization should think about?

Generally speaking, there are two common use cases for BI and analytics in the cloud that map to the two main architecture patterns: long-lived clusters and short-lived (or transient) clusters. Let’s discuss each in more detail.

Transient (short-lived) clusters for individuals or small teams

Often, data analysts, data scientists, and data engineers want to investigate new and potentially interesting data sets, but would like to avoid as much friction as possible in doing so. It’s quite common for data sets to originate in the cloud, so storing and analyzing them in the cloud is a no-brainer. Such data sets can easily be brought into S3 or ADLS in their raw form as a first step.

Next, a cluster can easily be provisioned with the instance type and configuration of choice, including potentially using spot instances to reduce cost. Generally, instances for transient clusters need only minimal local disk space, since data processing runs directly on the data in the cloud storage. There are tools, like Cloudera Director , that can assist with the instance provisioning and software deployment, making it as easy as a few clicks to provision and launch a cluster. Once the cluster is ready, data exploration can take place, allowing the data analyst to perform an analysis. If a new data set will be created as part of the work, it can be saved back to the cloud storage.

Another advantage to compute-only clusters is that they can easily and quickly be resized, allowing for growth or shrinkage, depending on data processing needs. When the analysis is finished, the cluster can be destroyed.

One of the main benefits of transient clusters is it allows individuals and groups to quickly and easily acquire just-in-time resources for their analysis, leveraging the pay-as-you-go cost model, all while providing resource isolation. Unlike an on-premises deployment in which multiple tenants share a single cluster consisting of both storage and compute and often compete for resources, teams become their own tenants of a single compute cluster, while being able to share access to data in a common cloud storage platform.

Long-lived clusters for large groups and shared access

The other common use case for BI and analytics in the cloud is a shared cluster that consists of many tenants. Unlike the transient cluster that may only run for a few hours, long-lived clusters may need to be available 24/7 to provide access to users across the globe or to data applications that are constantly running queries or accessing data. Like transient clusters, long-lived clusters can be compute-only, accessing data directly from cloud storage, or like on-premises clusters, they can have locally attached storage and HDFS and/or Kudu . Let’s discuss the use cases for both.

Long-lived compute-only clusters

For multitenant workloads that can vary in processing requirements over time, long-lived compute-only clusters work best. Because there is no local data storage, compute-only clusters are elastic by definition and can be swiftly resized based on the processing demands. During peak demand, a cluster can be scaled up so that query times meet SLA requirements, and during hours of low demand, the cluster can be scaled down to save on operational costs. This configuration allows the best of both worlds—tenants’ workloads are isolated from each other, as they can be run on different clusters that are tuned and optimized for the given workload. Additionally, a long-lived compute-only cluster consisting of on-demand or reserved instances can be elastically scaled up to handle additional demand using spot instances, providing a very cost-effective way to scale compute.

Long-lived clusters with local storage

While elastic compute-only clusters offer quick and easy scale-up and scale-down because all data is remote, there may be some workloads that demand lower latency data access than cloud storage can provide. For this use case, it makes sense to leverage instance types with local disk and to have a local HDFS available. This cloud deployment pattern looks very similar to on-premises deployments; however, it comes with an added benefit: access to cloud storage. As a result, a cluster can have tables that store the most recent data in partitions locally in HDFS, while older data resides in partitions backed by cloud storage, providing a form of storage tiering.  For example, the most recent month of sales data could reside in partitions backed by local HDFS storage providing the fastest data access, and data older than one month could reside in cloud object storage.

Deploying data marts in the cloud can help an organization be more agile with BI and data analytics, allowing individuals and teams to provision their own compute resources as needed while leveraging a single, shared data platform. If you’re interested in learning more about how to architect analytic workloads, including the core elements of data governance, for the cloud, be sure and attend my talk at Strata Data in Singapore, Rethinking data marts in the cloud: Common architectural patterns for analytics .

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Cloud Big Data Case Study – Faster Customer Reporting with a Data Lake and Data Warehouse

• Thorn Tech Staff
• May 15, 2019
• Big Data in the Cloud , Cloud Computing

Here’s how we implemented a combination of a database, data lake, and data warehouse to make customer reporting much faster for our client’s SMS marketing platform.

This is the fifth article of our Big Data in the Cloud series. You can read the first four here:

• Big Data in the Cloud: Why Cloud Computing is the Answer to Your Big Data Initiatives
• 8 Things You Need to Do Before Kicking Off Your Cloud Big Data Initiative
• SQL vs. NoSQL – what’s the best option for your database needs?
• How data lakes, data warehouses, and data marts fit into your cloud storage architecturee To get notified of our future Big Data in the Cloud posts, click here to sign up for our email list.

In our prior blog post, we reviewed data lakes, data warehouses, and data marts and how they can all fit into your cloud storage architecture. You can implement multiple technologies to create the cloud data architecture that will meet your needs.

We’ve done a good amount of big data projects for our clients, and one particular case study is a perfect example of how databases, data lakes, and data warehouses can all be integrated. So let’s check it out.

Faster customer data processing for a top-tier wireless carrier’s SMS marketing

We developed a top-tier wireless carrier’s SMS marketing platform, which delivers over 4 million messages a day to subscribers. Many of the messages sent by the client contain unique URLs linking to mobile web content, and our client was looking for a better way to measure how subscribers interacted with these text messages and links.

With so many messages being sent each day, we had to take a creative approach with the design of our client’s cloud big data environment to be able to measure all of these interactions.

So we created an architecture to log each outbound message, the unique URL included in each message, and click behavior metrics on a massive scale.

The overall architecture consists of:

• Amazon RDS PostgreSQL database for transaction processing.
• S3 data lake for storing log files.
• Redshift data warehouse for reporting and analytics.

Let’s dig deeper into each component.

The Database – PostgreSQL on Amazon RDS

We implemented an Amazon RDS instance using a PostgreSQL engine for our database.

As mentioned in our SQL vs. NoSQL blog post, PostgreSQL is an ACID-compliant (Atomicity, Consistency, Isolation, Durability) transactional database. We chose to use this type of database to ensure that the SMS transaction data – messages sent, message delivery status, clicked links, and messages received – were valid so we got the true story of the customer journey.

If a system error happens to occur while data is collected, the PostgreSQL database would roll back the changes to ensure that the customer behavior data was valid (atomicity). Additionally, because of the high volume of the SMS platform, many transactions are happening concurrently. The Isolation aspect of ACID will guarantee that the data is accurate when all of these writes are occurring simultaneously.

As you can see, ACID compliance is very important in maintaining accurate customer behavior data, and PostgreSQL does a great job of this.

The Data Lake – S3

The architecture of the SMS messaging system involves several services with independent but related datasets. All of this data needs to be aggregated to produce customer reports and support real-time queries from a reporting application to get a complete picture of the user’s journey. To gather all the data, JSON transaction logs from the independent services and data from our PostgreSQL database are saved to a single S3 bucket – the data lake.

Each time a file is saved in S3, a message is posted to SQS (Simple Queue Service) queues. The queues are periodically checked with a Lambda and when there are new files in the queue, an ECS (Elastic Container Service) task starts. The files are then loaded into Redshift staging tables (more details about our Redshift setup in the next section) and processed into their final format using SQL commands inside the ECS task.

The data pulled into Redshift is aggregated for specific reports, but often there is quite a bit more data in the original transaction log files stored on S3. This data might be helpful for deeper analytics, troubleshooting, or anomaly detection. Using tools like AWS Athena or Apache Hive, we can run complex SQL queries against the data where it lives, right on S3 – no need to load it into a separate database like Redshift or PostgreSQL.

As you can see, the S3 data lake is a low-cost, flexible, easily-accessible repository to store customer data, regardless of whether it will be analyzed now or in the future.

The Data Warehouse – Amazon Redshift

The system architecture that was designed prior to moving to the cloud used a traditional ETL process. Data was extracted from the transactional database, transformed for our client’s reporting purposes, and then loaded into a traditional data warehouse. This process took several hours to complete and occurred on a nightly basis.

The implementation of S3 as our data lake and Redshift as our data warehouse significantly improved this process. Instead of doing nightly exports from the database to the data warehouse, the aforementioned transaction log files are created and sent to S3 every five minutes, which kicks off the process involving SQS, Lambda, and ECS mentioned above.

That data then gets loaded from S3 into Redshift, and queries are run to aggregate or transform the data for specific reports. These reports include aggregated messaging statuses, delivery statistics, click-through rates, and many more metrics, by various campaign types across date ranges.

So instead of loading 24 hours of data once a day, we now load data every 5 minutes, giving our client a near real-time view of their customers’ behavior.

Redshift is an excellent fit for our client since it can support datasets with billions of records in a performant manner. The current 6-node cluster handles loading of over 9 million records per day and averages 20% CPU usage evenly distributed across the nodes.

Additionally, there are times when we have to make schema changes, and Redshift’s scalability combined with our S3 data lake setup allows us to do this easily.

With traditional data warehouses, making a schema change is often a long, painful process that requires long-running queries to alter the table and reload new data. In our case, with S3 as our data lake, we can simply:

• Delete all the data from Redshift.
• Make the schema changes to Redshift quickly and easily, because there is no data there.
• Spin up a bunch of additional Redshift nodes (usually to 20+).
• Reprocess every file in S3.
• Scale back the number of Redshift nodes when the file reprocessing is complete.

Instead of taking days or weeks to change the schema, we can achieve this in a few hours.

And as the dataset continues to grow and demand for reporting increases, we can increase the number of Redshift nodes to meet these requirements.

The architecture that we designed allows us to capture and analyze billions of data points in a quick, efficient manner. The ACID-compliant PostgreSQL database ensures that the customer behavior data is valid, S3 is the data lake that stores the log files that are used for analysis, and Redshift provides fast data reporting at scale.

As you can see in the example above, you can design a cloud architecture that includes a database, data warehouse, and data lake to store and analyze data at massive scale. The architecture we designed helps our client better understand how recipients are interacting with their messages, measure the success of SMS campaigns, and gain insight into planning future campaigns.

What are your thoughts about our architecture? Is there anything that you would do differently? How have you designed your cloud big data architecture? Post your comment below, we would love to hear your thoughts!

Like this post? It likes you too. 🙂 Please share it using the share buttons to the left. Then join our mailing list below, follow us on Twitter @thorntech , and join our Facebook page for future updates.

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Case study solutions for #8WeekSQLChallenge Using MySQL Workbench 8.0 CE at https://8weeksqlchallenge.com

SriRammSS/8-Week-SQL-Challenge

Folders and files, repository files navigation, 🍔 8 week sql challenge.

This repository contains the solution for the 8 case studies in #8WeekSQLChallenge !

Thanks @DataWithDanny for the excellent SQL case studies! 👋🏻

📚 Table of Contents

Case study #1: danny's diner, case study #2: pizza runner, case study #3: foodie-fi, case study #4: data bank, case study #5: data mart, case study #6: clique bait, case study #8: fresh segments.

View the case study here and my solution here and on Medium .

Business Task

Danny wants to use the data to answer a few simple questions about his customers, especially about their visiting patterns, how much money they’ve spent and also which menu items are their favourite.

Entity Relationship Diagram

Insights and Findings

Danny is expanding his new Pizza Empire and at the same time, he wants to Uberize it, so Pizza Runner was launched!

Danny started by recruiting “runners” to deliver fresh pizza from Pizza Runner Headquarters (otherwise known as Danny’s house) and also maxed out his credit card to pay freelance developers to build a mobile app to accept orders from customers.

Danny and his friends launched a new startup Foodie-Fi and started selling monthly and annual subscriptions, giving their customers unlimited on-demand access to exclusive food videos from around the world.

This case study focuses on using subscription style digital data to answer important business questions on customer journey, payments, and business performances.

View the case study here and my solution here and on [Medium].

Danny launched a new initiative, Data Bank which runs just like any other digital bank - but it isn’t only for banking activities, they also have the world’s most secure distributed data storage platform!

Customers are allocated cloud data storage limits which are directly linked to how much money they have in their accounts. There are a few interesting caveats that go with this business model, and this is where the Data Bank team need your help!

The management team at Data Bank want to increase their total customer base - but also need some help tracking just how much data storage their customers will need.

This case study is all about calculating metrics, growth and helping the business analyse their data in a smart way to better forecast and plan for their future developments!

Data Mart is an online supermarket that specialises in fresh produce.

In June 2020, large scale supply changes were made at Data Mart. All Data Mart products now use sustainable packaging methods in every single step from the farm all the way to the customer.

Danny needs your help to analyse and quantify the impact of this change on the sales performance for Data Mart and it’s separate business areas.

The key business question to answer are the following:

• What was the quantifiable impact of the changes introduced in June 2020?
• Which platform, region, segment and customer types were the most impacted by this change?
• What can we do about future introduction of similar sustainability updates to the business to minimise impact on sales?

Here are some further details about the dataset:

• Data Mart has international operations using a multi- region strategy.
• Data Mart has both, a retail and online platform in the form of a Shopify store front to serve their customers.
• Customer segment and customer_type data relates to personal age and demographics information that is shared with Data Mart.
• transactions is the count of unique purchases made through Data Mart and sales is the actual dollar amount of purchases.

Each record in the dataset is related to a specific aggregated slice of the underlying sales data rolled up into a week_date value which represents the start of the sales week.

Clique Bait is an online seafood store. In this case study - you are required to support the founder and CEO Danny’s vision and analyse his dataset and come up with creative solutions to calculate funnel fallout rates for the Clique Bait online store.

View the case study here and my solution [here] and on [Medium].

Fresh Segments is a digital marketing agency that helps other businesses analyse trends in online ad click behaviour for their unique customer base.

Clients share their customer lists with the Fresh Segments team who then aggregate interest metrics and generate a single dataset worth of metrics for further analysis.

In particular - the composition and rankings for different interests are provided for each client showing the proportion of their customer list who interacted with online assets related to each interest for each month.

Danny has asked for your assistance to analyse aggregated metrics for an example client and provide some high level insights about the customer list and their interests.

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What is Data Mart?

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A data mart is a specialized subset of a data warehouse focused on a specific functional area or department within an organization. It provides a simplified and targeted view of data, addressing specific reporting and analytical needs. Data marts are smaller in scale and scope, typically holding relevant data for a specific group of users, such as sales, marketing, or finance. They are organized around specific subjects, such as sales, customer data, or product information, and are structured, transformed, and optimized for efficient querying and analysis within the domain.

Types of Data Mart

There are three common types of data marts:

• Independent Data Mart
• Dependent Data Mart
• Hybrid Data Mart

1. Independent Data Mart

An independent data mart is created and maintained separately from the data warehouse. It is created to satisfy the particular needs of a specific business unit or department. Independent data marts are typically smaller in size and more rapidly and readily set up. They offer flexibility and agility since they are not constrained by the challenges of the centralized data warehouse. Nevertheless, data redundancy and inconsistency may result if it is replicated over several different data marts.

2. Dependent Data Mart

A dependent data mart is generated right out of a data warehouse. It takes some of the data from the data warehouse and arranges it to meet the needs of a specific industry. Dependent data marts, which profit from the data integration, data quality, and consistency provided by the data warehouse, allow for the centralization and preservation of all data in a single source of truth. They are often developed to serve particular reporting and analytical needs, and they are frequently updated from the data warehouse. Dependent data marts offer data consistency and prevent data duplication because they rely on the data warehouse as their main source of data.

3. Hybrid Data Mart

Both independent and dependent data mart components can be found in a hybrid data mart. As well as combining additional data sources particular to a given business unit or department, it makes use of the centralized data warehouse for the integration and consistency of the core data. By offering flexibility and agility for department-specific needs while keeping the integrity and consistency of shared data from the data warehouse, hybrid data marts offer the benefits of both strategies. This strategy creates a balance between localized data management and centralized control.

Difference between Data Mart, Data Lake, and Data Warehouse

Although both a data mart, a data warehouse, and a Data Lake are methods for storing and analyzing data, their scopes, objectives, and structures vary in these terms :

Structures of Data Mart

These typical structures are used by data marts to represent and store information :

A common data mart structure is the dimensional model, commonly referred to as a star architecture. It comprises numerous dimension tables surrounding a core fact table. The fact table includes quantifiable information or metrics about a certain business procedure or topic matter, such as sales or inventory. Dimension tables offer contextual or descriptive details about the data in the fact table. Typically, each dimension table depicts a certain feature or perspective of the data, such as time, region, products, or consumers. Through primary-key and foreign-key associations, the fact table and dimension tables are connected, creating a star-like structure. This format makes it simple for users to quickly slice and dice data along many dimensions, which supports effective querying and analysis.

2. Snowflake

A dimensional model extension that offers more normalized data structures is the snowflake model. By dividing them into several linked tables, this structure further normalizes dimension tables. When working with complex hierarchies or when a dimension has a lot of properties, this normalization can help decrease data redundancy. The snowflake model , however, can make searches and data integration procedures more difficult.

Advantages of Data Mart

For organizations that require targeted and specialized data storage and analysis solutions, data marts provide several benefits. Here are some benefits listed below :

• Data marts are built to serve the specific reporting and analytical requirements of a particular business unit or department.
• Data marts are designed to provide optimized performance for specific business areas or departments.
• By storing a subset of relevant data and tailoring the structure to meet specific analytical needs, data marts can deliver faster query response times and improved data retrieval performance.
• Data marts empower business users by providing them with direct access to relevant data and analytical tools .
• Users can access and analyze data more efficiently, leading to enhanced productivity and decision-making.

FAQs on Data Mart

Q.1 what are the three types of data mart.

There are three types of data marts, they are :- Dependent Independent Hybrid

Q.2 What is a data mart vs database?

A database stores structured data in tables for various applications, while a data mart is a specialized subset for specific business needs.

Q.3 Can multiple data marts be connected or integrated?

Yes, distinct data marts can be joined together or combined to support cross-functional analysis or to produce a thorough perspective of data across several business sectors. Common data structures, shared dimensions, or data aggregation methods can all be used to achieve this integration.

Q.4 What is the purpose of a data mart?

A data mart satisfies the specialized reporting and analytical requirements of a specific business area or department inside a company. Users can quickly find relevant information and carry out analysis within their particular domain due to the concentrated and customized view of data it offers.

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How Organizations Can Utilize Data Lakes, Marts, Vaults, and Warehouses

Click to learn more about author Neil Barton. As businesses begin the movement to be more data-driven, it can be difficult to assess an organization’s exact data infrastructure need. With terms such as data lakes, data vaults, and data warehouses, IT business leaders may be asking themselves: “What exactly does our organization need to extract […]

Click to learn more about author Neil Barton.

As businesses begin the movement to be more data-driven, it can be difficult to assess an organization’s exact data infrastructure need. With terms such as data lakes, data vaults, and data warehouses, IT business leaders may be asking themselves: “What exactly does our organization need to extract the most value?”

Data Warehouses

A data warehouse, or an enterprise data warehouse as it is sometimes known, is a curated repository of data. It provides business users with access to the right information in a usable format, and can include both current and historical information. As data enters the data warehouse environment , it is cleansed, transformed, categorized, and tagged – making it easier to manage, use, and monitor from a compliance perspective, which is where automation comes in.

The volume and velocity of data experienced by businesses today means that manually ingesting this data, processing it, and making sure it’s stored and accessible in a way that meets compliance requirements within a data warehouse is unfeasible in the modern world. However, with businesses constantly looking to data as the source of both reports and forecasts, a data warehouse is invaluable.

It’s important that data lakes do not subsume the role of a more structured data infrastructure just because of the perceived effort of ingestion.  Automation  can help speed the ingestion and processing to fast-track time to value with data-driven decision-making in a data warehouse.

Data lakes are huge collections of data, ranging from raw data that has not been organized or processed, through to varying levels of curated data sets. One of their benefits from an analytics purpose is that varying types of consumers can access appropriate data for their needs. This makes it perfect for some of the newer use cases such as Data Science, AI, and Machine Learning, which are viewed by many companies as the future of analytics work.

It is a great way to store masses of raw data on scalable storage solutions without attempting traditional ETL or ELT (extract, transform, load), which can be expensive at this volume. However, for more traditional analytics, this type of data environment can be unwieldy and confusing – which is why organizations turn to other solutions to manage essential data in more structured environments. 

In terms of positioning within a data infrastructure, data lakes are, if you like, up-stream of other data infrastructure, and can be used as a staging area for a more structured approach such as a data warehouse, as well as providing for data exploration and Data Science.

A data mart is a specific subset of a data warehouse, often used for curated data on one specific subject area, which needs to be easily accessible in a short amount of time. Due to its specificity, it is often quicker and cheaper to build than a full data warehouse. However, a data mart is unable to curate and manage data from across the business to inform business decisions.

Data Vaults

Data vault modeling is an approach to data warehousing that looks to address some of the challenges posed by transforming data as part of the data warehousing process. One of the great advantages of a data vault is that it makes no assessment as to what data is “valuable” and what isn’t, whereas once data is processed and cleansed into a warehouse environment, this decision has typically been made. Data vaults have the flexibility to manage this, and to address changing sources of data, leading the data vault approach to be credited with providing a “single version of the facts” rather than a “single version of the truth.”

For enterprises with large, growing, and disparate datasets, a data vault approach to data warehousing can help tame the beast of big data into a manageable, business-centric solution, but can take time to set up. Data vault automation is a critical component to ensuring organizations can deliver and maintain data vaults that adhere to the stringent requirements of the Data Vault 2.0 methodology and will be able to do so in a practical, cost-effective, and timely manner. 

Each data approach plays its own part in ingesting, managing, and delivering data across an organization. Being able to understand how they all fit together is a valuable tool for IT managers and business leaders when trying to comprehend how to make the most out of big data. Using technologies such as automation can help speed up the establishment and management of these practices and can help businesses fully utilize their data infrastructure.

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D'Mart: Most Successful Indian Chain of Hypermarkets[DMart Case Study]

Varad Kitey

D'Mart is an Indian chain of hypermarkets established by DMart owner Radhakishan Damani on May 15, 2002. DMart has 214 stores in 72 cities across 11 states in India including Maharashtra, Andhra Pradesh, Telangana, Gujarat, Madhya Pradesh, Chhattisgarh, Rajasthan, National Capital Region, Tamil Nadu, Karnataka, Uttar Pradesh, Daman, and Punjab. So, let's get started with the D'mart case study .

Mumbai headquartered DMart is owned and operated by Avenue Supermarts Ltd. (ASL). After the IPO posting (as Avenue Supermarts Ltd.), it made a record opening on the National Stock Exchange(NSE). DMart’s valuation rose to ₹39,988 crore after the close of the stock on 22 March 2017. This made DMart the 65th most significant Indian firm, followed by Britannia Industries , Marico, and Bank of Baroda. As of 21 November 2019, the market capitalization of DMart was around ₹114,000 crore, taking it on 33rd position of all recorded organizations on the Bombay Stock Exchange.

This article will shed insights on the supply chain model of DMart, its business model, marketing strategies, How DMart was started, key financial highlights of DMart, growth and future of DMart in India & more.

In this Dmart Case Study, we have discussed the -

DMart - Company Highlights Foundation of DMart & Why DMart is Successful? Strategic & Organization Structure of DMart DMart - Business Model & Supply chain Model Marketing Strategy of DMart Factors Affecting the Profit of DMart DMart - Important Financial Metrics Growth of DMart in India Future of DMart

DMart - Company Highlights

Foundation of dmart & why dmart is successful.

Unlike Flipkart was established by two 25-year old youngsters toward the beginning of their professions, DMart's establishing story couldn't have been more extraordinary as DMart was established in 2002 by a then-45-year-old Radhakishan Damani at a moment that he'd effectively made his millions. When he established DMart, Damani was an incredible name in Indian securities exchanges. He had already got a few worth stocks that surpassed Gillette and HDFC Bank’s valuations.

Damani, who dropped out of a trade degree after the primary year, had first joined his dad's metal rollers business, yet had begun putting resources into stocks when he was 32. He wound up getting to be one of the greatest stock financial specialists of the 90s, and current securities exchange bull Rakesh Jhunjhunwala believes him to be a tutor. In any case, after an effective financial exchange profession putting resources into shopper confronting organizations, Damani chose to begin his own.

On May 15, 2002, Damani established grocery store chain DMart and embraced techniques that were one of a kind to Indian retail. Up to that point, most retail chains rented their stores, yet DMart picked carefully do its exploration and possessed its very own stores by and large. That technique appears to have worked as DMart has never needed to close down a store since it's opened in every one of the long periods of its activity.

While other retail players forayed into different classifications, including hardware and design, DMart stayed focussed on its center sustenance and basic food item business. What's more, when other store chains are on the whole propelling their very own private brands in an offer to improve edges, DMart still stocks just outsider items.

It's this moderate methodology that has worked for DMart. Other retail chains were picking development, yet for the initial 15 years, Dmart just worked its stores in 4 states. Indeed, even today, the company has 214 stores in 72 cities across 11 states. DMart had a benefit to-deals proportion of 3.7%.

In correlation, other significant Indian retailers don't passage very also Future Group has a benefit to deals proportion of 0.21%, Spencer's Retail had a negative benefit to deals proportion of - 8.9%, and Reliance Retail which works high-edge classifications including hardware and adornments and has more than double the incomes of DMart just dealt with a benefit to deals proportion of 1.6%.

DMart's traditionalist yet beneficial approach is by all accounts demonstrated after its author. Damani is famously media-bashful and gives no meetings. He's said to be modest, all things considered, also he doesn't appear to talk much, yet is evidently a decent audience, engrossing a lot of data rapidly, and afterward following up on it.

And keeping in mind that Damani's success has made him hugely rich because of the flood in DMart's stock value, he's currently worth $15.5 Billion (over Rs 116,200 Crores) regardless he wears a white shirt and white jeans to work, the dress he's been wearing since the 80s. Despite everything, he goes for night strolls on Girgaum Chowpatty in Mumbai and unconditionally converses with the outsiders who approach him after his Dmart's open achievement.

Strategic & Organization Structure of DMart

The ultimate start with DMart needs to make a picture among the majority of a rebate store that offers the vast majority of the items from over every single real brand. Fundamentally, a store that offers an incentive for cash! Presently, since individuals for the most part come to DMart on the grounds that they all what they need under one rooftop; consequently, DMart stores are operational in high rush hour gridlock territories and crosswise over three organizations including Hypermarkets that are spread crosswise over 30,000-35,000 sqft, Express group, that is spread more than 7,000-10,000 sqft and in conclusion, the SuperCenters, that are set up at more than 1 lakh sqft.

What's more, Dmart's intended interest group being the center pay gathering, it uses Discount offers as a special instrument for baiting the clients and expanding deals too. Generally speaking – Dmart's prosperity is centered around three things: Customers, Vendors, and Employees ! Take Customers. Since Dmart is focusing on center salary family units, every one of their stores is in, or near, neighborhoods and not in shopping centers.

Their thought isn't to meet each customer's need like different contenders, yet rather, Dmart tries to meet most normal shopper needs, while offering some benefit for their cash. Furthermore, since, 90% of these stores are possessed legitimately by Dmart, they don't need to stress over month-to-month rentals and their ascent, or migration chance. Moreover, this is helping them manufacture resources on their books.

This likewise keeps Dmart all around promoted and obligation light, while its tasks produce extra money. All the cash that is spared utilizing this procedure is at the end offered back to the clients as limits! Sellers! Seller connections are the second mainstay of their model. Since he originates from a dealer foundation, his seller connections have been his greatest quality.

The FMCG business has an installment standard of 12-21 days, however, Dmart pays its sellers on the eleventh day itself. This causes him to remain in the great books of the merchants and dodges stockouts. Furthermore, since Dmart purchases in mass and pays its sellers well in time, they additionally get the chance to win higher edges. Essentially, their procedure is to "Get it low, Stack it high and sell it shabby"!Workers! This is the third mainstay of their model. DMart offers great cash, adaptability, strengthening, and loose and effective work culture.

They even proceed to employ tenth standard dropouts with the correct frame of mind and duty. They incline toward procuring crude ability and afterward put intensely in preparing, to shape them according to their prerequisite. Representatives are simply educated once concerning the worth framework and arrangements at D-Mart and after that are enabled by giving them the opportunity to work without someone continually investigating their shoulders. There is outright lucidity on what should be accomplished, yet you don't have to dread targets.

DMart - Business Model & Supply chain Model

The business model lies at the core of a successful company. A good, foolproof business model not only acts as a pillar for a business to grow but also helps it prosper in a comparatively less amount of time.

DMart, often termed as the Walmart of India, has been quite successful in its business so far, and a major credit goes to the robust business model it has developed over the years.

The chain of DMart operates on a B2C (Business to Consumer) model in which the company sells its goods from the manufacturer’s house to that of the end-user. DMart sells a wide range of products ranging from home care and personal care to grocery and staples, daily essentials, home appliances, footwear, luggage, fruits and vegetables, men’s and women’s apparel, and more. These goods, as we all know, fulfill our everyday needs, and hence, have a significant demand throughout the year. Therefore, they wipe out the possibilities of fluctuations due to high demand and helps the brand get the stability that many others dream about.

DMart is recognized for its thrifty cost structure that has made the company keep its losses under control. Here are some prominent characteristics of DMart’s business model:

Low operational costs and fewer expenses

DMart believes in the effective utilization of the spaces instead of adorning its interiors and shelves fancifully. The company works in launching more and more products in fewer spaces for the customers to choose from, which can also be summed up as a low-interior-cost concept to reduce the operational costs. Besides, when you walk into a DMart store you would also find lesser billing counters, which further works in reducing employee costs.

Ownership model

Damani, the company’s founder, had decided quite early in the game to adopt a store-ownership model. This played a major part in making DMart a low or no debt company, thereby strengthening it financially. Furthermore, the company doesn’t accrue any rental costs, which helps DMart open more stores and gain high positive cash flows. The company owns around 80% of all the stores that it is credited for.

Affordable rates of products

It is usually observed that in the FMCG sector, the retailers pay off the credit to their vendors within a period of 3 weeks whereas DMart pays off their credit within a week. This helps the company benefit in many ways including the huge discounts that they get from the vendors , which in turn is entirely rewarding for the end-users too.

Affordable rate of products with tons of discounts on various products leads to increasing the overall footfall and spike up the sales volume. This increasing sale also helps the manufacturers to rely on the brand and bring in more stocks for the rising demand, which extends another volume discount from the manufacturers' end.

Also Read: The Complete Psychology behind Free Samples & How it Works

Slotting fee

DMart levies a ‘Slotting Fee’. As the term might indicate, it is a fee that DMart charges from the manufacturers to store their products on the shelves of DMart stores , which is also sometimes referred to as an entry fee. DMart, on the other hand, with its appealing marketing strategies and attractive discounts ensures that the products are sold out as quickly as possible.

Sales channel

As discussed earlier, DMart opts for a B2C (Business to Consumer) business model, where the company sells the products directly from manufacturers to the end-consumer. The company purchases its goods in bulk and this eliminates the middleman (distributors and wholesalers) from the chain, which helps in passing their commissions as discounts to the consumers.

Target customers

DMart’s target customers are the middle-class groups and lower-middle-class groups, those who often want to buy low-cost goods that come with hefty discounts but are of good quality. This makes DMart attract an extensive customer base than many other retailers.

Regional Goods

A land of diversity, India nurtures an array of region-specific goods. This gave DMart an amazing opportunity to capture the niche markets with products specific to different regions. DMart researches the popular local brands of a particular region and makes them available, thereby avoiding people’s need to go to the local Kirana stores. This has helped DMart to gain more market share.

Operating strategy

Contrary to their peers and rivals, DMart has always stuck to their own stores and deliberately avoided the malls, which might have otherwise risked the overall sales of the company and increased the expenditure.

Besides, the company is also not very comfortable expanding geographically. The company had its stores only in 4 Indian states until 2014, which only expanded in recent years to 11 states. One another thing is that DMart attracts low marketing costs because the main marketing strategy of DMart is that the company is recognized among its end-users via “ word of mouth ”.

Marketing Strategy of DMart

DMart is a company that doesn’t believe in marketing aggressively unlike many of its competitors. The company maintains a marketing mix where its Unique Selling Position (USP) lies in offering the products at less than Maximum Retail Price (MRP). This is the most important factor that contributes to keeping the company ahead of its peers.

What DMart indulges in is aggressive CSR activities and other low-cost promotional activities . One of the most promising campaigns is:

Better School, Brighter Futures!

DMart is a company that takes pride in the laudable CSR initiatives that it takes. Over the years, the company has grown to be a huge support for its employees and other communities alike with the help of its socially responsible business practices. This undoubtedly spreads positive vibes all around.

In its “Better School, Bright Futures!” campaign, DMart has launched an amazing program in various schools that are there in and around Mumbai. The sole aim of which helps students understand things better and create an ecosystem that allows them to benefit from better education, mentoring research facilities, and new networking opportunities.

Embracing Low-Cost Advertising Mediums for Promotion

DMart looks up to visual and print mediums to promote its brand name and products. The print medium of advertising revolves around newspaper ads with information about their products, discounts, sales, and coupons.

On the other hand, the visual component of advertisement comprises the banners, flexes, and hoardings that are put to display in locations near the stores to mention the product-specific offers, seasonal discounts, and other freebies that the company offers from time to time.

Digital Presence of DMart

DMart was founded back in 2002 and boasts of an enviable offline presence but when it comes to digital presence it bothered little about it to be true. However, the company has taken a few steps to place it ahead on the digital front. These steps include the installation of a chatbot on Facebook Messenger and the launch DMart Ready.

As of now, DMart uses Facebook as a medium for information, which the brand uses to inform and clear customers’ doubts. The company is yet to explore Instagram and Twitter fully, the proper utilization in the upcoming times will surely help the company set itself more stable in the future.

Factors Affecting the Profit of DMart

Damani is a calm man who stays under the radar, yet his triumphant characteristics are too obvious to possibly be missed. The following are his ways to deal with a business that drove him to thundering achievement:

Design of Logo

Like Warren Buffett, Damani too has been a worth speculator who might take a shrewd perspective on the long haul. When he turned into a business person, he held a similar methodology and manufactured DMart without depending on any speedy alternate ways. For example, he never rents the property for his stores however gets it. In the long haul, it spares him from a major rental outgo. This was a key factor behind the productivity of DMart.

What Is Trifle That's Important

Damani began little and did not rush to grow. Low scale gave him superior control of the store network and enabled him to concentrate on benefits directly from the earliest starting point. In the 18 years of its reality, D Mart has turned a benefit every year.

Evaluation Of People

Damani started with purchasing an establishment of Apna Bazar. That was the point at which he started fabricating individual relations with merchants and providers. He esteems both and they never let him down. The stores never leave stock.

Selling As Cheap

Damani realized what he was doing: offering individuals buyer results of everyday use at substantial limits. That turned into his sole objective. One of his strategies was to pay his providers and sellers inside days rather than weeks which was the business standard. They gave the merchandise at a less expensive rate to him in lieu of early installment. He passed on the money-saving advantages to his clients, which guaranteed steady success.

Go Steady And Slow

In spite of the fact that D-Mart began 18 years prior, despite everything it has 119 stores in a couple of states, a modest number contrasted with those claimed by Ambani and Biyani. Rather than fast development, Damani received a moderate pace which gave him his emphasis on productivity. That is the reason D-Mart has not closed a solitary store since it began and creates higher per-store incomes than the stores of Ambani or Biyani.

Neglect the Herd

Damani had learned and drilled with the progress the craft of not following the crowd while he was a financial specialist. As a business person, he has a similar methodology. There have been such a large number of brand new thoughts in retail, for example, different online business patterns, which he didn't give any significance. Designs or patterns can't impact the man who realizes what he needs and how he can get it.

Available Locally

Despite the fact that DMart is the best basic food item retail chain in the nation, Damani has restricted it towards the western states. One reason is his dependence on neighborhood supplies rather than expand supply chains.

A Job Has Conversation

Damani stays under the radar which bears him all-out devotion to his work. His moderate and quiet ascent in a discouraged division is a sign of his resolute spotlight on work. He has once in a while given a meeting to a TV channel or a paper.

DMart - Important Financial Metrics

The below table highlights the important financial metrics of DMart as per its audited, consolidated financial statements -

(Rs. in crores, unless otherwise stated)

Standalone Results -

For the quarter ended March 31, 2021 (Q4FY21):

• Total Revenue stood at Rs. 7,303 Crore, YoY growth of 17.9%  
• EBITDA of Rs. 617 Crore; YoY growth of 47.6%
• PAT stood at Rs. 435 Crore; YoY growth of 51.6%  
• Basic EPS for Q4FY21 stood at  Rs.6.71, as compared to Rs. 4.49  for Q4FY20
• 13 stores were added in Q4FY21

For the year ended  March 31, 2021  (FY21):

• Total Revenue stood at Rs. 23,787 Crore, lower by 3.6%
• EBITDA of Rs. 1,742 Crore; YoY decline of 17.9%
• PAT stood at Rs. 1,165 Crore; YoY decline  of 13.7%
• Basic EPS for FY21 stood at Rs.17.99,  as compared to Rs. 21.49  for  FY20
• 22 stores were added in FY21 and 2 stores were converted into fulfillment centers for Avenue ECommerce Limited.

Growth of DMart in India

Avenue Supermarts running the DMart chain of stores in the nation revealed a 21.4 % year-on-year net benefit development and a 32.1 % year-on-year income development for the quarter finished March 31, 2019, (Q4) at Rs 203 crore and Rs 5,033 crore, separately.

For the three months finished December 31, 2018, DMart had announced its slowest net benefit development in eight quarters at 2.1 % as it pondered developing challenges in basic food item retail.

Second from last quarter income development came in at 33 % (year-on-year), which is likewise a merry quarter, said experts, suggesting the organization had figured out how to keep up its pace of development as far as the top line in Q4 in the midst of focused power. The numbers were comprehensively in accordance with Street gauges. A survey by investigators of Bloomberg had pegged net benefit at Rs 211 crore and income at Rs 5,122 crore for the quarter under audit.

Income before intrigue, duty, deterioration, and amortization (Ebitda) for Q4 was at Rs 377 crore, up 27.9 % throughout the year-prior period and again extensively in accordance with Street assessments of Rs 395 crore. Yet, Ebitda edges contracted for the third straight quarter, however, the drop was negligible at 20 premise focuses to 7.5 % from a year sooner.

This is additionally the most reduced as far as Ebitda edges for DMart in 75%. While the organization did not indicate same-store deals development for Q4, examiners said it was somewhere in the range of 15 and 18 % for the period under audit.

Same-store deals development is the development of a similar deal of stores for one year or more. For the entire year finished March 31, 2019, (FY19), Neville Noronha, overseeing executive (MD) and (CEO), Avenue Supermarts, said same-store deals development was 17.8 % even as income grew 32 % year-on-year to Rs 19,916 crore and net benefit went up 19 % from a year sooner to Rs 936 crore.

The FY19 same-store deals development was higher than the 14.2 % revealed for FY18, division examiners stated, as the firm drove higher deals throughput at its stores. Income from deals per square feet at DMart stores remained at Rs 35,647 for FY19 against Rs 32,719 in FY18, an ascent of about 9 %. The organization additionally included 21 stores in FY19, of which 12 were included in Q4 alone, taking the aggregate to 176 for the monetary year.

Future of DMart

Avenue Supermarts runs the DMart grocery store chain of stores. If in any case, the nation experiences a crisis, financial specialists question whether the organization shows enough strength during these intense occasions. But examiners in a note from Systematix Shares and Stocks (India) Ltd. said, " The continuous crisis in utilization and higher aggressive force in staple retail should confine development in determining deals per square feet to 7% in the financial year 2020 from 13% in FY19."

While speculators will intently follow how that works out in the coming quarters, Avenue Supermarts' income development of almost 27% in the June quarter is nothing to get surprised at. Obviously, it should likewise be referenced at the same time that high development rates are a basic for the DMart share, which is one of the most costly stocks in the nation.

It currently exchanges at amazing multiple times evaluated income for FY20. FY20 has begun an idealistic note for the organization. The development in EBITDA (income before premium, assessment, deterioration, and amortization) edge in the June quarter will mitigate financial specialists' uneasiness about weights on productivity somewhat.

What is Dmart?

Founded in 2002, Dmart is an Indian retail corporation that is designed to stand as a one-stop supermarket chain that brings a wide range of products ranging from basic home products, personal products and more.

Where is the Dmart headquarters?

DMart headquarters is in Mumbai, Maharashtra.

Who founded Dmart?

Radhakishan Damani and his family founded Dmart in 2002.

Where was the first branch of D mart?

The first branch of D mart is in Powai's Hiranandani Gardens.

What is the vision and mission of Dmart?

The mission and vision of DMart is " to provide the best possible value for consumers so that every penny spends on shopping gives them more value for money than they would get anywhere else," as per the vision and mission statement of Dmart.

How many D mart stores in India are there in total?

Currently, the total number of D mart stores in India were reported to be more than 234 in number, spread across more than 11 states in India, as per February 2022's reports.

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