Unveiling WhatTheDuck: The Ultimate Browser-Based SQL Engine for CSV Files Sujeet Pillai July 14, 2023

Hello data enthusiasts,

We’re excited to announce the launch of our latest innovation at Incentius – WhatTheDuck! As a new age technology and data analytics firm, we’re committed to simplifying complex workflows and making data analytics more accessible. With WhatTheDuck, we’re taking a significant step towards that goal.

WhatTheDuck is a free-to-use tool that allows you to run SQL queries on CSV files directly in your browser. No more time-consuming data transfers or security concerns about sending your data to the backend. Everything happens right in your browser, ensuring a fast, efficient, and secure experience.

We’ve built WhatTheDuck on DuckDB over WASM, a combination that guarantees speed and efficiency. This tool is perfect for those times when you need to do quick SQL queries on flat data you have as CSV files. It’s like having a lightweight, portable SQL engine that you can use anytime, anywhere!

The beauty of WhatTheDuck lies in its simplicity and accessibility. Whether you’re a data scientist needing to analyze a dataset quickly, a student working on a project, or a business professional looking to make data-driven decisions, WhatTheDuck is designed to make your life easier.

What’s more, we’ve made sure that your data stays secure. With WhatTheDuck, no data is sent to the backend. This means you can confidently work with sensitive data without worrying about security breaches.

We’re thrilled to see how WhatTheDuck can help you streamline your workflows and make the most of your data. We’re committed to continually improving and expanding the tool based on your feedback. So give WhatTheDuck a try and let us know what you think! We’re eager to hear your thoughts and suggestions.

Incentius is dedicated to pushing the boundaries of technology and data analytics. With WhatTheDuck, we’re bringing you a tool that’s not just about making data analysis easier – it’s about making data analysis accessible to everyone.

So, what are you waiting for? Dive in and give WhatTheDuck a try. Let’s make data analysis easier and more accessible together!

Happy querying!

Building a Data Stack on a Budget: An Affordable Guide to Data Management Sujeet Pillai January 17, 2023

Database management

A data stack is a combination of various tools and technologies that work together to manage, store, and analyze data. It typically consists of a data storage engine, an ingestion tool, an analytics engine, and BI visualization tools. Data stacks have become quite central to an organization’s operations and growth in recent years.

Data management is an essential part of any organization, and the way data is managed has evolved over the years. Data lakes and data warehouses were once only affordable by larger organizations. However, this has changed with the open-source data stack ecosystem’s growth. The open-source data stack ecosystem has grown significantly in recent years, providing powerful alternatives for every layer of the stack. This has pushed the envelope for data stacks and reduced entry barriers for organizations to adopt a data stack.

One of the main reasons why data stacks have become more accessible is the availability of open-source alternatives. Open-source alternatives are available for every layer of the data stack that packs a serious punch in capability. These alternatives are often just as good, if not better, than their commercial counterparts. They also tend to be more flexible and customizable, which is essential for organizations that must tailor their data stack to their specific needs.

Another reason why data stacks have become more accessible is the availability of cheap cloud resources. Cloud providers such as Amazon Web Services, Google Cloud, and Microsoft Azure provide low-cost options for organizations to set up and run their data stacks. This has enabled even smaller organizations to afford a data stack.

Organizations need to consider this framework over a patchwork of point-to-point integrations seriously. A patchwork of point-to-point integrations is often a result of an ad-hoc approach to data management. This approach is not only difficult to manage but also limits the organization’s ability to gain insights from its data. On the other hand, a data stack framework provides a more structured approach to data management, making it easier to manage and providing the organization with the ability to gain insights from their data.

An Affordable Data Stack

Affordable data stacks that organizations can consider are as follows:

Storage Engine: Clickhouse

Clickhouse is a column-oriented database management system that can handle large data loads and has great query performance. It runs on commodity hardware and can be self-hosted using Docker. Clickhouse is designed to process large amounts of data, and its columnar storage model enhances its query performance.

Ingestion Engine: Airbyte

Airbyte is an open-source data integration platform that automates the ingestion of data sources and can be monitored and managed from a UI. It can also be self-hosted using Docker and has the ability to use Clickhouse as a sink. Airbyte automates the ingestion of data sources, making it easy to bring data into the data stack.

Analytics Engine: DBT

DBT is a powerful analytics engine that helps organize data models and processing. It’s built on SQL with jinja templating superpowers, making it accessible to many more people. DBT is a hero in the data lakes space, helping enterprises organize their data models and processing. When building out an analytics process in DBT, it’s quite helpful to use a conceptual framework to organize your models. I found this blog excellent to be an excellent starting point, providing great insights. 

Visualization Engine: Metabase

Metabase is a powerful visualization tool that makes it easy for organizations to gain insights from their data. It has lots of visualizations that cover most bases. The SQL query builder or ‘question wizard’ in Metabase is quite powerful for non-SQL experts to get answers from their data. It also has a self-hostable open-source version and can easily be set up in Docker.


For infrastructure, we recommend using Amazon Web Services. This stack can be deployed on a single m5.large instance for smaller-scale data and scaled up to a cluster configuration for larger data sets. Additionally, the different components of the stack can be separated into different servers for scaling. For example, if many Metabase users are accessing the data, it may be necessary to move Metabase onto its own server. Similarly, if ingestions are large, it’s best to move Airbyte to its server. And if storage and queries are large, it’s best to move Clickhouse into a cluster formation. This way, a company can ensure its system can handle more data as needed.

Production considerations

When it comes to taking the data stack to production, there are a lot of other considerations. Organizations should ensure reliable, fault-tolerant backups and provide security and role-based access. They should also build DBT models to cater to multiple use cases and normalize data values across sources. Other considerations may include monitoring and alerting, performance tuning, and disaster recovery planning.

Reliable, fault-tolerant backups are crucial to ensure that data is not lost in the event of a disaster. Organizations should have a well-defined backup and recovery plan in place. This should include regular backups, offsite storage of backups, and testing of backups to ensure they can be restored in an emergency.

Security and role-based access are also crucial implications. Organizations should ensure that only authorized personnel have access to sensitive data. This can be achieved by setting up role-based access controls, which ensure that only users with the necessary permissions can access sensitive data.

Further, organizations should ensure that their data is accurate, consistent, and reliable. This can be achieved by building DBT models that cater to multiple use cases and normalizing data values across data sources.

Finally, monitoring and alerting, performance tuning, and disaster recovery planning are also crucial. Organizations should ensure that their data stack is performing at optimal levels and that they are alerted to any issues that arise. Performance tuning is necessary to ensure that the data stack performs optimally. Disaster recovery planning is crucial to ensure that data can be recovered in the event of a disaster.


In conclusion, data stacks have become increasingly affordable and accessible for organizations of all sizes. The open-source data stack ecosystem has grown significantly, providing powerful alternatives for every layer of the stack. Designing DBT models to cater to multiple scenarios and standardizing data values across various sources are crucial. A data stack framework provides a more structured approach to data management, making it easier to manage and providing the organization with the ability to gain insights from their data.

Deploying a data lake to production with all these elements is a non-trivial technical exercise. If you do not have this expertise in-house, consider using the services of a consulting organization with expertise in this area, like Incentius. Drop us an email at info@incentius.com, and we’d be happy to help

10 Best Data Warehouse Tools to use in 2023 Marketing November 23, 2022

What is a data warehouse?

A data warehouse is notably designed for data analytics, which involves reading huge amounts of data to figure out relationships and trends across the data. A data warehouse typically stores processed data in databases, which are used to collect and organize data. These databases store information in a structure of predefined tables and columns. Business users rely on data warehouses to gain insights into their company’s data, which further aids them in future business decisions.

Data warehouses require more storage, computing, networking, and memory because of the volume and variety of data produced by businesses. The amount of enterprise data organizations generate is increasing, as they expand their customer base and embrace new technologies.

Why is there a demand for data warehouse tools?

Data warehouse tools use Artificial intelligence (AI) and Machine Learning (ML) to enhance data warehouse performance. Some of the key factors businesses consider for using data warehouse tools are:

  • To gain strategic and operational knowledge from the data
  • Improve decision-making and support systems
  • Explore and assess the effectiveness of marketing efforts
  • Keep track of the performance of their employees
  • Observe consumer trends and forecast the next business cycle

Investment in data warehouse tools is skyrocketing. The data warehouse market is anticipated to grow to $34 billion by 2025 from its current size of approximately $21 billionMicrosoft Azure’s SQL Data Warehouse and AWS Redshift are the two fastest-growing market players. 

10 data warehouse tools to use in 2023

  • Google Data Warehouse Tools

Given its leading position as a search engine, Google is well-known for its data management abilities. Google’s Data Warehouse Tools demonstrate the company’s advanced data management and analytics capabilities. One of the best data warehouse tools Google offers is Google BigQuery. It is a cost-effective data warehouse tool that includes machine learning capabilities. The platform uses high-speed SQL(Structured Query language), which helps to store and query large data sets.

  • Big Eval

Big Eval leverages the value of the enterprise by continuously validating and monitoring the information quality of the data. It also automates testing tasks during the development process. The tool has a unique automation approach and a simple user interface that ensures same-day benefits.

  • Oracle Autonomous Data Warehouse

Oracle Autonomous data warehouse is a top legacy software in the database market. The Oracle database is ideal for enterprise companies looking to improve their business insights through machine learning. The tool can automate functions like setting, safeguarding, regulating, scaling, and backing up data within the data warehouse. Oracle Database provides data warehousing and analytics to assist businesses in scrutinizing their data and gaining deeper insights.

  • Snowflake

Snowflake is a unique cloud-based data warehouse tool in the business world. The cutting-edge data warehouse is built with a patented new architecture to handle all aspects of data and analytics. It combines performance, simplicity, concurrency, and affordability on a higher scale as compared to other data warehouse tools. Snowflake allows for both transformation during and transformation after loading (ELT) processes. Snowflake integrates with several data integration tools, including Informatica, Talend, Fivetran, and Matillion.

  • IBM Data Warehouse Tools

IBM is used by large business clients. The company is well-known for its vertical data models, in-database, and real-time analytics, which are especially important in data warehousing. One of the most established IBM Data warehouse tools in the market is the IBM Db2 Warehouse.

IBM Db2 Warehouse tool allows for self-scaling of data storage and processing. It includes a relational database that enables you to quickly store, analyze, and retrieve data. It takes data from a source system and transforms and feeds it into the target system. And to understand how data passes through transformation and integration, you can use Data Lineage, pre-build connections, and stages in the tools

  •  Teradata Vantage

Teradata Vantage provides all-in-one data warehousing solutions. It is a cloud analytics platform combining analytics, data lakes, data warehouses, and new data sources. Teradata Vantage also supports SQL for interacting with data stored in tables.

  • Amazon Redshift

Amazon Redshift is a fully managed, petabyte-scale (measurement unit of data) cloud data warehouse solution. It is a simple and cost-effective data warehouse tool. It uses standard SQL to analyze almost any type of data. It provides huge storage capacity and offers compatible backups for your data. It is widely used, and because of its easy scalability, it can handle large enterprise databases.

  • SAP Cloud Data Warehouse

SAP Cloud Data Warehouse is used for open-source and client-server platforms. It is built in a modular format for efficient use and space utilization. It incorporates ML and AI functionality in its data warehouse solution. And also offers a pricing calculator based on its level of usage. SAP is a portable application that can be used on any device.

  • PostgreSQL

PostgreSQL is a powerful, open-source object-relational database system that has been actively developed for over 30 years and has a strong reputation for dependability, feature robustness, and high-end performance. The tool can function as a primary database and is useful for large and small corporations, as well as medium-sized businesses.

  • Microsoft Azure Data Warehouse Tools

Microsoft Azure is a cloud-computing platform that allows developers to create, test, and deploy applications. Azure is publicly available and offers Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS). One of the best data warehouse tools that Microsoft offers is the Azure SQL database. It is based on the PaaS infrastructure, which handles database maintenance tasks like updating, patching, monitoring, and backing.


In a nutshell:

Utilizing pooled data and data warehouse tools can effectively streamline your business organization. Data warehouse solutions (tools) can translate gathered data from diverse sources into a more straightforward arrangement.

Modern Database Management Best Practices Amit Jain May 20, 2022

As the volume of company data has increased, database management has become increasingly critical. Rapid data expansion causes a slew of undesirable outcomes, including poor application performance and compliance risk, to mention a few. 

Database management entails a variety of proactive strategies for mitigating the negative consequences of data accumulation. Database Management is the process of organizing, storing, and retrieving data from a computer. It may also refer to a Database Administrator’s (DBA) data storage, operations, and security procedures throughout the data’s life cycle. Organizations may prevent events that impair productivity and income and increase data integration for more business intelligence by controlling data across its full lifespan.

1. Draft Relevant Business Goals:

A strong data management vision, clear targets, well-defined metrics to assess progress, and a solid business purpose are all part of a well-developed data strategy. There are a plethora of other things you can do with your data, but it’s critical to start by defining your objectives. If you know what your business goals are, you can keep just the data that is useful to the organization, making database maintenance a breeze. It’s critical for your DBAs to understand the plan for the data they’re collecting and to concentrate entirely on the data that’s important to the company’s overall objectives. 

Your company needs should be reflected in an executable, focused database management plan, as well as the metrics you’ll use to measure your performance. Knowing your company’s business objectives allows you to maintain only the data that is relevant to your company, making database maintenance and administration easier. Setting meaningful company objectives is the very first practice you should consider because it offers you a guiding light so you don’t get lost.

2.Clear Policies and Procedures should be crafted:

When implementing best database management practices, you must establish rules and processes for your database settings. Creating particular backup and recovery processes and regulations allows your team to respond more quickly in the event of a disaster. Standard methods for deleting old files, conducting maintenance, and indexing files should be included in policies. These standards limit the risk of misunderstandings or errors, which is especially crucial in bigger businesses with various datasets and database managers.

Data should also be verified for correctness on a regular basis, as obsolete data might be useless to your organization. Most significantly, having explicit policies makes database maintenance and day-to-day management much easier. Lastly but not least, rules should contain procedures for erasing data and securely destroying storage media such as hard disks and servers.

3. Ensure High Standard of Data Quality:

Data is the King you do not want to mess with. Or as they say today- Data is the new Oil. Your DBA should try to maintain a high level of data quality by eliminating data that does not match the criteria and adjusting quality standards to reflect your evolving strategy. Even if they don’t work directly with the DBA or the database, everyone in your firm should understand the principles of data quality protection. Someone who is unaware of the dangers of duplicating data might add to your team’s workload. 

Teach everyone how to submit high-quality data and how to recognise good data. Train all team members who have access to the data on the right procedures to gather and enter data to help your team focus on data quality. You must set clear goals for improving data quality using relevant and quantifiable metrics. Make sure the stewards are included in the process when data managers develop objectives. These database management practices pay a lot in the short as-well-as long run.

4.Data Security and Backup must be a priority:

When it comes to database management in your company, data security and backup are key responsibilities. There are never enough backups when it comes to data. It’s critical to have a reliable backup system in place and to keep an eye on it to make sure it’s working properly. Furthermore, in the event of data loss or corruption, every organization should have a disaster recovery strategy in place. Because disasters can happen, it’s critical for your company to have a data recovery policy in place. 

Although no disaster can be completely predicted or avoided, you can strengthen your database’s data security and manage the risks associated with worst-case situations. If or when a possible breach occurs, having a strategic process in place is critical. Security concerns evolve in tandem with technological advancements, corporate expansion, and database features. Your staff should keep current with the market and seek to anticipate the demands of your database.

Here’s your Takeaway-Use Quality Data Management Services!

Choosing appropriate services is a crucial step in establishing a high-quality data management system for your business. Keep in mind that the ultimate objective is to have a modular architecture that can connect to and structure a number of data sources and analysis methods from the start. 

This is when Incentius comes into picture. You want to locate a customer data platform that will provide you with a truthful and concise view into your connections and user data, as well as the ability to communicate with your market precisely and promptly. You want your data management system to make your tasks simpler by automatically enriching and cleaning data to guarantee you have the most comprehensive and full picture of your data. We are here, consider bothering us for it.

Data is the new Oil- Here’s how big data fuels business Marketing March 25, 2022

What do we mean by “Data is the new Oil”?

Data is more precious than ever in today’s digital economy. It’s essential for proper operation of everything, from public organizations to startups. Progress would come to a standstill without it. It’s evident that data is the new oil, and that the major prospect is how big data can better our lives. Many firms’ data infrastructure is still a cost center today, but it can be turned into a profit center by leveraging big data to enhance things on a daily basis. Every firm in the world has a significant opportunity to produce new goods and services across all lines of business by combining internal and external data. 

Owing to these benefits, it’s reasonable to argue that information is the 21st century’s oil, and analytics is the combustion engine. Perhaps we’ll come back with a separate blog to talk about the emergence and significance of analytics.

How does data help businesses?

The statement has clearly grown in popularity over a few years. However, in what way can businesses leverage big data to comprehend its position in the business world? It is worth mentioning that this is an era of advancements in AI and ML. What are some of the ways that big data may be used as a useful resource? Despite the alleged threat that automation poses to jobs, I prefer to take an affirmative approach. By automating routine yet time-consuming operations, experienced people may focus on more profitable tasks. Let’s have a look.

1. Data drives better and faster business decisions:

Leaders can use data to make better judgments about where their company should go. You can do more than just analyze historical patterns with current insights. While big data analytics tools might display a lot of statistics, knowing what data you’re searching for in the first place is essential for generating valuable insights. Any company that has a website, a social media presence, and accepts any type of electronic payment is gathering information on its customers, user habits, web traffic, demographics, and more. If you can figure out how to get all of that data, it’s full of possibilities. Nothing beats having actual data to back you up since it provides organizations with the tools they need to make better decisions based on evidence rather than assumptions or gut feelings. 

You may take proactive measures based on known data, utilizing patterns and trends to make important adjustments that will increase your production — and your bottom line. However, in order for this to happen, everybody in the firm must have access to the information they require to make better decisions. Users throughout the organization should be able to examine and analyze data to get answers to their most urgent business problems. You’ll have more confidence in your company decisions and become more adaptable if you have good data on your side. Businesses may use big data to make better marketing decisions, track social media participation, and forecast sales patterns.

2. Data enhances business operations and processes:

Data is a driving force behind the growth of automation. From recruiting to learning and development, data and automation have the ability to totally revolutionize a wide variety of manual tasks. Difficult, repetitive activities can thus be performed by robots or algorithms. Big Data aids in the understanding and improvement of company operations, allowing you to save money and time. By analyzing data on how different marketing channels function, you can determine which ones provide the best return on investment and concentrate on that sector. Alternatively, you may investigate why other channels aren’t functioning as well and attempt to enhance them. This would enable you to create more leads without increasing your advertising budget. 

Data from business processes may be tracked and reviewed to address potential shortfalls. It allows you to better understand each phase of the process and you may take actions accordingly. Big Data enables you to quickly test several versions of increased software designs. You may learn about lead times, material prices, efficiency, and other topics. It enables you to increase the effectiveness and competitiveness of a variety of business processes.

3. Data provides a better understanding of consumers:

Businesses may use big data to make decisions about attracting new consumers, retaining existing customers, and enhancing customer service. A corporate organization may profile clients in a wide range of ways using data. With good data in hand, you can figure out who appeals to your target audience and who doesn’t. This allows a company to have a one-on-one dialogue with customers in real-time. Based on what you learn, you may change your marketing approach or even your sales training. How do you know who your customers are if you don’t have data? How can you tell if customers enjoy your items or if your marketing efforts are working? How can you know how much money you make or spend? Understanding your consumers, market, and useful patterns requires data. 

One of the most significant benefits of data is that the more you know about your clients, the better you can offer them. You will become a business of the past if you do not use data to unearth insights that can steer your organization into the future. One of the most effective methods to gather and use feedback is through data. It assists you in gaining a better understanding of how clients see your services and goods. Understanding your clients is crucial to your business’s success. Data may be used in practically every aspect of your client interaction. It may also help you gain a deeper understanding of your present consumers and boost your retention efforts. As a result, you’ll be able to make the essential adjustments and rework your items. Fortunately, innovations in big data processing and analytics have made using data to build your organization easier than ever before. And we have tools to help you accomplish it.

4. Data helps you generate more income:

Data may be monetized to increase revenue or generate a new revenue source. Data may help you get insights into the market and your customers. However, this information is useful not just to you, but also to others. Trend data might be sold to major enterprises in the same industry. By utilizing the big data collected, companies and customers may get closer. It’s also worth mentioning that data may help businesses save money. 

Apart from this, data-driven fraud-detection technology has saved billions of dollars by detecting and preventing fraudulent transactions. Machines and algorithms now have more data to learn from as a result of the explosion of data. Businesses will have even more interesting options to automate operations, make better decisions, delight consumers, and more as a result of this. Data will undoubtedly continue to play a significant part in a variety of sectors throughout the world. It has the potential to achieve wonders for a company. It’s critical to teach your personnel on data management in order to gain additional rewards. Your firm will be more productive and efficient if you manage big data properly.

At a Glance

Data is called the new oil because of the capability that it holds to transform the business and operational model of today’s organizations. Big Data is now at the heart of almost all business decisions. Data helps businesses in the following ways:

  • Smarter decision-making
  • Enhanced business operations
  • Better customer experience
  • Revenue stream

It is 2022 and if you fail to make the most of the data available to you, you will be left behind. Want to stay ahead of the competition? Incentius is just the right place for you. To know about our Data Transformation and Analytics solutions, click here or contact us.


The importance of data warehouses Marketing November 29, 2021

What is a data warehouse? 

Data warehouses are enormous storage facilities for data collected from a variety of sources. It’s an abstracted representation of the company’s operations, arranged by subject. It has undergone a lot of transformation and has a lot of structure. Data isn’t entered into the data warehouse until its purpose is determined. Data that is organized, filtered, and has been processed before for a clear objective is stored in a Data warehouse. 

Why should startups choose a data warehouse?

Decisions are made based on a set of data. Data is processed, analyzed, and then the decision part of the process takes place. Data warehouses show significant differences from operational databases in the sense that they hold past data, allowing corporate leaders to study data over a prolonged period of time. Your startup needs a data warehouse because: 

1. They ensure consistency:

Data warehouses are storage spaces programmed in a way that eases your work. They apply a standard format to all the data collected and makes it easier for the employees to analyze this structured data and share insights with the team later.

2. They will help make better decisions: 

Understanding the trends and patterns of the market is important. Decisions need to be based on facts and that is exactly where data warehouses come in. They increase the speed and accuracy with which multiple data sets can be accessed, making it easier for business decisions to extract insights that help them develop market strategy that would set them apart from their peers.

3. Maximises Efficiency:

Data warehouses allow leaders to access the data that helps them understand the pattern and make future strategies. Understanding what has worked in the past and how effective their previous methods have been really saves time and is effective.

How do data warehouses benefit startups?

If you are planning on starting a software startup and are worried about data storing options, then a data warehouse would make for a great choice. Data warehouses are capable of delivering enhanced business intelligence, improve the quality of data, maintain consistency, save time, generate a high run on investment (ROI), enable organizations to forecast confidently, improve the decision-making process, and provide competitive advantage. These are some of the ways data warehouses can prove to be beneficial for your business. 

Can a data warehouse replace a data lake? 

A data lake is not a replacement for a data warehouse. As mentioned above, these terms cannot be used interchangeably. There are significant differences between the two. Some of these differences include: 

1. Structure of the data:

 Raw data is data in its original form. It has not been processed for any purpose yet. One of the major differences between data lakes and data warehouses is the structure of data stored. Data warehouse generally stores data that has been processed, about the needs of a clear objective or specific goals whereas data lake stores data in raw form, which is unprocessed data. This is one reason why data lakes require a much larger storage capacity than data warehouses. Data that has not been processed is pliable and may be readily evaluated for any purpose, making it perfect for machine learning. Moreover, with so much raw data, data lakes can easily become data swamps if proper data quality and control mechanisms aren’t in place.

2. Purpose:

The purpose of data stored in data lakes is undetermined. They may be used in the future for a specific purpose but till then we just have floating raw data that is taking up storage space. On the other hand, if we talk about data warehouses, the data stored there is structured and filtered according to the needs of a particular objective. This means that the space used by that data is never going to be wasted as this data will surely be used. However, one cannot say the same for data stored in data lakes. 

3. Processing:

Data warehouse needs structured and organized data. You must filter and alter the data before entering it into a data warehouse. Frequently, you’ll need to represent it as a star or snowflake schema, which adheres to the schema-on-read principle (SQL). If we talk about data lakes, you don’t have to process the data here as any and every form of data can be stored in data lakes. When you’re prepared to use the data, you can use schema-on-write to give it the required shape and structure.

4. Security:

The data lake will contain essential and frequently extremely sensitive company data as big and growing volumes of different data are poured into it. Hence, the security of the data becomes a major concern. Data warehouses are more established and reliable than data lakes. Advanced technologies, which include data lakes, are still in their infancy. As a result, the capacity to secure data in a data lake becomes immature. Unlike advanced technologies, data warehouse advancements have been here and in use for decades.

5. Insights and Users:

Since data lakes contain all forms of data and allow users to access data before it has been processed, cleansed, or structured, they can get to their results faster than with a standard data warehouse. Those inexperienced with raw data may find it challenging to navigate data lakes. To comprehend and translate raw, unstructured information for any unique business use, a data scientist and specialized tools are usually required. Data scientists are now using data lakes. We can locate structured data in a data warehouse that is straightforward to navigate for business professionals. Processed data, such as that found in data warehouses, just needs that the user is knowledgeable about the subject matter.


A data warehouse is a centralised collection of data that can be studied to help people make better decisions. Moving beyond conventional databases and into the world of data warehousing can help organisations get more out of their analytics initiatives.

Data Visualization using MS Excel Sumeet Shah March 31, 2021


The ability to analyze data is a powerful skill that helps you make better decisions. When it comes to choosing a tool, there are several options; however, the first one you think of is Microsoft Excel. Why? It is a tool that is deep-rooted in almost every business and our day-to-day life. The extent of our dependence on it is such that “Export to Excel” is the most used option amongst BI communities. In terms of usage, there are over 750M Excel users worldwide, and literally, any function in any organization/industry can benefit through the use of Microsoft Excel. Additionally, the ability to learn and quickly put it into practice gives you another reason to adopt it.

“I never guess, it is a shocking habit—destructive to the logical faculty” – Sherlock Holmes

Today, we live in an information-rich and time-poor world. Businesses have transformed from being intuitive to data-driven engines. Leaders crave data because they are aware of its availability. However, what they are looking for is not data; it is information and knowledge extracted from it. The big problem, though, isn’t data or too much of it. It’s the lack of time; there just isn’t enough of it to analyze the tons of data you have at your disposal. So, if your organization isn’t using BI tools like Tableau or PowerBI and plan to use Microsoft Excel as a tool for data analyses, this might be of your interest. 

Above is a quick piece of analysis done on the recently concluded India vs England Cricket Test Series. A simple export of scorecards for all the test matches and the use of basic excel functions like IF, SUM, RANK, VLOOKUP, and OFFSET have been used to transform the data into a structured format. There are no VBA macros used anywhere in the analysis, and the visualizations have been created by basic chart options available as a part of the default excel package. 


If you are an organization looking to analyze your data and are struggling with it, feel free to reach out to us at Incentius.