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.

Best DevOps Tools to Use in 2022 Sumeet Shah June 10, 2022

What is DevOps? DevOps is a set of tools and practices that bring together software development and operations, bridging the gap between the two. This approach is witnessing a rise in its demand due to provision of high quality software delivery on a continual basis. The aim behind using this approach is to make sure that flaws are reduced to the minimum and productivity is increased to the maximum. 

For this purpose, although there are a number of DevOps tools that are used in the market, few of them are exceptionally good namely, AWS, Jenkins, Terraform, and Kubernetes. These tools automate the process of software development by focusing primarily on collaboration and communication between the professionals with different roles and working in different teams. 

This blog will illustrate the best tools with their respective categories to use for DevOps processes:

1. Containers- Docker:

Containers are software development platforms that enable developers to create, test, and deploy products in resource-independent conditions. Each container contains the whole runtime environment, which includes the software, its modules, source code, settings, and all of its resources. Platforms for containers provide orchestration, automation, security, governance, and other functionalities. For fast application development and deployment, DevOps mainly relies on containerization, with Docker being the most extensively used container technology. The Docker engine was created to make developing, deploying, and managing containerized applications on single nodes easier. Docker is free and open-source software that works with cloud services and runs on Windows and Linux.

2. Infrastructure as Code (IaC)-Terraform:

Hashicorp developed Terraform as an open source tool to automate the provisioning of infrastructure resources. It supports all public and private cloud infrastructure provisioning like Networks, Servers, managed services, and Firewall. Infrastructure as a coding philosophy is used to create, manage, update, and destroy infrastructure resources such as virtual machines, containers, networking, and others. Terraform uses a notion called state files to keep track of the state of your infrastructure. HCL (Hashicorp configuration language) is Terraform’s own domain-specific language.

3. Container Orchestration System – Kubernetes:

Kubernetes is a prominent container orchestration platform and an open-source DevOps solution for automating the deployment and administration of containerized applications. To achieve effectiveness and quality in production, developers use Kubernetes to automate tasks like container initialization, scalability, communication, security, and more. Kubernotes is unique because it has the ability to heal itself. It makes modifications to your application or its setup while also monitoring its health. Rollouts and rollbacks get automated. It also provides a set of Pods with their own IP addresses and a single DNS name for Service Delivery and load balancing. 

4. Continuous Integration/Delivery (CI/CD)- Jenkins:

Jenkins is a Java-based open-source automation platform with plugins designed for Continuous Integration. If you want to integrate a certain tool, you need to install the tool’s plugins. It also enables you to deliver your software on a continuous basis by interacting with a variety of tests and deployment technologies. Jenkins, which was created by Kohsuke for Continuous Integration (CI), is now the most extensively used Continuous Delivery (CD) solution. It has thousand-plus plugins that allow it to integrate with almost any tool. Jenkins continues to gain traction as the most widely used continuous integration and delivery tool in the world.


In order to actualize the benefits of DevOps, choosing the best tools is imperative. Right tools help realize the advantages by breaking down communication silos and improving productivity. It must be noted that it takes time to implement the culture shift and not just a night.

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.

Should I use python for data analysis? Marketing February 26, 2021


Should I use python for data analysis

Python programming is internationally one of the fastest-growing languages. Python is used to handle data in an efficient manner. It was designed by Guido van Rossum and first appeared in 1991 in the Netherlands. Data scientists use it frequently to handle their data better because of its high potential in the data science sector. It can be used for scripting applications or building web applications also. 

In this era of technology, we have to store, maintain and process data with a lot of data with accuracy. Incorrect handling of this data can be expensive and time-consuming. Many programming languages offer the same, and Python is one of them. All of them are unique in their way. But some features of Python make it different from any other language. First of all, it has an amazing robust ecosystem which makes it easier to read and learn. Secondly, it has a set of data-oriented feature packages. That makes programming easy and speedy for programmers. Programmers must consider Python for data analysis due to its capability and ongoing improvement. It is great for mathematical computations and algorithms.


Data analysis refers to collecting raw data and converting it into logical and statistically meaningful data. It is helpful to draw conclusions, generate insights and make better decisions for the company. Nowadays, businesses need to maintain and manage large volumes of the data generated. That helps them to extract useful information. There are many tools through which data analysis can take place. For example- Python, Java, SQL, etc.


1. Through data analysis, we can obtain accurate data for our business strategies. You can get to know about the target set or actual outcome and can make plans accordingly.

2. We can get to know about areas which are overfunded and help to cut down our costs.

3. Data analysis also gives us an idea of future consumer behavioural patterns. It enables us to make future inventions in our products.

4. Through sentiment analysis, we can analyze customer reviews online. This will help us to know about negative or positive reviews about our products. We can make changes to our products accordingly.

5. Data analysis can anchor your graphic design and digital marketing strategies.

6. Data analysis can be used to take action to enhance productivity and business gain.

7. Also, we can collect information about our customer’s demographics. Through this, we can target the consumer group accordingly.


Python is a data-centric programming language. It offers a lot of facilities and tools to programmers. These tools and features make data analysis easy and cost-effective. Let’s take a look at some of its features that make it a sound option for data analysis.

1. Libraries Collection It has a huge collection of tools as libraries. These libraries are available free of cost to any user. These tools save a lot of time and can fix most problems. Some of these libraries are Pandas, NumPy, SciPy, Matplotlib, seaborn, etc.

2. Scalability Python is one of the best languages to scale rapidly. There are many approaches to fixing the same issue. One can handle all from a few records to billions of rows of data using python.

3. Flexibility It is highly flexible, which makes it one of the most requested languages among programmers. We can build data models, and web services can apply data mining, etc., using python. Many new models and algorithms can be built on it.

4. Python Community Python has great community support. It is open-source, which means it’s available freely and, as a result, has a large community. It employs a community-based development model. 

5. Easy to learn It is one of the easiest languages to start with. It has a smooth learning curve. Its ecosystem makes it very understandable and readable and a great first programming language to start learning.

6. Data handling capacities We can easily install well-tested packages for data analysis in python. We can also handle data available in bulk. It also contains several libraries with renowned algorithms. NumPy and Pandas are some of the mainly used libraries for data analysis in Python.


Python has one of the best ecosystems among all the languages for data analysis. It has a robust ecosystem that makes it one of the easiest languages also. It is growing gradually, which makes Python a better platform to work on. It makes it understandable and readable, which helps a newbie learn Python. It has a simple syntax, and its commands mimic the English language. It is great for building a data science pipeline and machine learning. It is a general programming language that we can use for production as well as for research & development. Python is easy to write and easily interpretable.


Python is a well-structured programming language that is very helpful for any business. Also, it is very easy to learn and understand Python. There are many reasons to select Python, as we discussed all the features above in this article. Its tools and features make it more efficient and unique. It gives us various solutions to the same problem, which makes it flexible and scalable. It has community support as well as it is open-source. Most programmers prefer Python nowadays. Python is a base for any data scientist, especially for data analysis.