Data warehousing Archives

Yashica Chopra
March 13, 2025
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Top 15 Data Warehousing Companies in Manufacturing – Features & Services

A data warehouse is a central repository that helps streamline and automate workflow in an enterprise and make data-driven decisions in real-time. Here, we’ll read about the top 15 data warehousing companies in manufacturing industry and the range of services and other features they provide. Data is the core of any business. Manufacturing enterprises have tons of data about vendors, raw materials, production, suppliers, distributors, end users, etc. Storing this data in independent silos can be cumbersome and result in duplication. A data warehouse is a comprehensive solution to streamline manufacturing data and help implement the data-driven decision-making model. According to The Business Research Company, the data warehousing market was $33.76 billion in 2024 and is expected to reach $37.73 billion in 2025 at a CAGR (compound annual growth rate) of 11.7%. It is projected to reach $69.64 billion by 2029 at a CAGR of 16.6%. Whether you want to invest in a data warehouse as a service (DWaaS) or build an on-premise repository for data warehousing, it is recommended to partner with a reliable and reputed service provider. Data warehousing is not limited to setting up a central database. It is a complex process of identifying data sources, cleaning, sorting, and formatting the data, storing it in a central repository, and creating third-party integration with data analytical tools to provide real-time insights to end users. Check out the blog to find out the best data warehousing companies in manufacturing that offer tailored solutions to streamline your processes and deliver the expected outcomes. 15 Top Data Warehousing Companies in Manufacturing DataToBiz DataToBiz is among the leading data warehousing companies in manufacturing and several other industries with a global client base. It is an award-winning artificial intelligence and business intelligence company with ISO and SOC 2 certifications. Be it real-time manufacturing analytics or OEE analytics, the company knows how to provide tailored solutions that align with the client’s requirements. The company is also a certified partner of Microsoft (Gold), AWS, and Google. This expertise makes it a reliable partner for cloud data warehousing or DWaaS. It empowers manufacturers to eliminate outdated data silos and replace them with a flexible and scalable central repository on a cloud server. DataToBiz creates streamlined workflows to automate data collection, cleaning, and analytics. The teams build customized data visualization dashboards for enterprises to use graphical reports for proactive decision-making. It helps unlock the true potential of the business through transparent and cost-effective end-to-end data warehousing services. IBM IBM is a global IT and AI company offering data warehousing services to clients from around the world. It provides scalable and high-quality solutions to manage enterprise data and derive actionable insights in real-time. The company uses AI and ML tools to set up a data warehouse with several third-party integrations. It offers cloud-native Db2 and Netezza data warehouse technologies designed by the company’s experts to manage, store, and analyze diverse datasets. Manufacturers can decide the cloud platform they want to use for hosting the system. IBM works with large enterprises to help them become more agile and flexible. From optimizing the production cycle to enhancing cybersecurity and improving customer experience, the company supports the manufacturer in several ways. Amazon Redshift Amazon Redshift is a part of AWS (Amazon Web Services) offered by the tech giant. It provides seamless data storage and analytics through a data warehouse as a service solution for SMBs and large enterprises. The data warehousing platform can be integrated with other apps in the AWS ecosystem or third-party tools by independent vendors. The company offers a specialist to work with each client and set up the necessary connections. Redshift can be integrated with data lakes to derive actionable insights using SQL tools and accelerate decision-making. The company also helps enterprises monetize their business data to increase revenue sources. Amazon offers industry-specific solutions for each client to maximize results, optimize the use of resources, and mitigate risks. It is a great choice for businesses that want to use AWS for managing all business processes. Cloudera Cloudera is one of the leading data warehousing companies in manufacturing and other sectors. It has clients from various parts of the world and simplifies analytics to make them accessible to every employee in the enterprise. The company’s data warehouse provides cloud-native solutions and self-servicing analytics to quickly derive meaningful insights in real-time, and that too for cost-effective prices. The solution is integrated with third-party apps and AI tools to create a consistent framework for managing workflows. Cloudera also takes care of data security and governance to prevent unauthorized access and creates guidelines for businesses to manage their systems. The company promotes smart manufacturing through intelligent systems. From setting up IoT connections to building resilient supply chains, the company knows how to assist the manufacturer at every step. Yellowbrick Data Yellowbrick is an SQL data platform and an enterprise data warehouse provider in the market. The company’s robust platforms are designed to handle the workload of growing enterprises. Its solutions for data warehousing in manufacturing are secure, efficient, and scalable. Moreover, the system can be built on minimal infrastructure to reduce management costs for the enterprise. The platforms can be run on public, private, and hybrid clouds and are powered by cloud-native Kubernetes architecture. By using advanced artificial intelligence tools, the team of experts makes the data warehousing setup more scalable, agile, and user-friendly. Yellowbrick’s enterprise data warehouse comes with reliable ecosystem support and works anywhere (cloud and on-premises). The company also consolidates databases from different vendors to create a central data warehouse with greater efficiency. Informatica Informatica is an AI and data engineering company for clients from various industries, including manufacturing. It offers custom solutions for data warehousing in the production line to ingest, integrate, and clean the manufacturing data and derive insights in real-time. The company reduces the complexity of using different applications by creating a unified interface on a single platform. Its AI-powered low-code and no-code applications can be used by employees to access tailored reports and make

Yashica Chopra
March 10, 2025
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Building a Cost Effective Data Pipeline – The Intelligent Approach

A data pipeline can bridge the gap between raw data and actionable insights by creating a comprehensive and multi-step infrastructure on-premises or cloud platforms. Here, we’ll discuss data pipelines, analyze their associated costs, and demonstrate how to construct profitable pipelines using modern data engineering techniques. Data is everything for a business, be it a startup or a multinational enterprise. Converting raw data into actionable insights helps an organization make decisions quickly and gain a competitive edge. The process of transforming data into insights happens in complex data pipelines, a system where data from multiple sources goes through various stages like cleaning, storage, transformation, formatting, analysis, and reporting. The data pipeline is vital to implement the data-driven model in an enterprise. Fortune Business Insights reports that the global data pipeline market will reach $33.87 billion by 2030 at a CAGR (compound annual growth rate) of 22.4%. Tools and technologies are an integral part of the data pipeline. According to a report by The Business Research Company, the global data pipeline tool market has grown from $11.24 billion in 2024 to $13.68 billion in 2025 at a CAGR of 21.8% and is expected to touch $29.63 billion in 2029 at a CAGR of 21.3%. The same report says that the increase in the adoption of cloud computing technologies and migration to cloud platforms contributes to the higher demand for data pipeline tools. Tech giants like Google, IBM, Microsoft, AWS, etc., are among the top companies whose data pipeline tools are used by enterprises from around the world. However, data pipelines come with a few complications, money being the biggest concern for businesses. Is your data warehousing setup draining your budget? You are not alone! Data pipelines that haven’t been optimized and managed effectively become costly over time and drain business money. In this blog, we’ll learn more about finding out if your data pipeline is expensive and how data pipeline management using cloud solutions can optimize costs. Building a Cost Effective Data Pipeline Microsoft Azure and AWS (Amazon Web Services) are the top two cloud platforms in the market, followed by Google Cloud. You can migrate your existing data pipeline and architecture to the cloud or build a new cloud-native data pipeline and optimize it to save costs from spiraling over the years. With help from data engineering companies, you can make informed decisions about how to use existing resources to maximize performance and get better results by investing in cloud solutions. Structuring the Pipeline Start with the basics. If the foundation is strong, the entire data infrastructure in your organization will be robust, scalable, and aligned with your objectives. Identify and define the goals of building the data pipeline. Set the path for data flow and check which processes can be run in parallel without consuming too many resources. Create comprehensive data security, governance, and compliance documentation to ensure no one who is not authorized can access the system or data. Parallelization Parallelization is the process of dividing data processing tasks into smaller units that can be executed in parallel or concurrently across distributed computing resources. This is done to make the data management system more effective and increase its speed. It also makes the data pipeline easier to scale as and when required. Data engineers use different techniques like parallel execution, batch processing, distributed computing, etc., to achieve the goals. Cloud platforms like Azure and AWS make parallelization simpler by allowing experts to choose the resources and programming language to set up concurrent processing. Increase the data pipeline performance without adding to the cost. Caching and Compressing Caching reduces the latency of data pipelines to promote near real-time data processing and insights. A high-performing data pipeline will use caching and compressing techniques. With caching, the data is temporarily stored in the memory. With data compression, the size of transferred data is reduced, thus limiting the load on the network. Together, the entire data processing model will be quicker and more effective while consuming fewer resources. This ultimately reduces the cost of maintaining and using the data pipeline in your organization. The data engineering team will balance the procedures to free up computational resources and allow the processing of large data volumes in quick time. Azure Spot Virtual Machines Azure data engineering services give you access to spot virtual machines (Spot VMs) which are available on an auction-based pricing model. It is cheaper than the pay-as-you-go subscription model though Azure has the right to reclaim them if other customers require the capacity. If you have non-critical workloads with flexible start and end times, a spot VM is the best place to run them. Businesses can benefit from unused Azure capacity by using it for their processes. The pricing is categorized into three models: achieve, cool, and hot. You can also automate the processes to speed up the results. Shut Down and Remove Unused Resources A common reason for increased costs is the presence of unused resources in your plan. Data engineers can identify such resources and shut them down to optimize costs. This can be easily done by using tools like Azure Advisor and Azure Cost Management. The cloud platform provides customers with numerous tools and applications for resource and cost optimization. It’s up to you to use them effectively to manage the data pipelines. Even after shutting down idle resources, they will still accumulate in your account. When you no longer require the resources, remove them and increase the storage capacity. It’s vital to know why a resource is not necessary and how removing it doesn’t affect other processes. Infrastructure as Code (IaC) AWS data engineering has a practice called IaC or infrastructure as a code. It is the process of setting up and managing the systems using code instead of manual processes. Simply put, the developer will write code for the infrastructure that will automatically be executed whenever necessary. It is similar to how a website or a mobile application works. IaC is a great choice for DevOps teams

Yashica Chopra
February 26, 2025
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Model-Based Systems Engineering: Is It for You?

MBSE is a new process that promotes the use of digital modeling and systems to enhance system lifecycle management. Here, we’ll discuss model-based systems engineering, its components, processes, tools, and benefits for enterprises in any industry in detail. Model-based systems engineering (MBSE) is a process or methodology in which different models and tools support a system’s lifecycle and track data through digital threads. It differs from traditional systems engineering, which uses text-based documentation and manual processes. In MBSE, digital modeling and simulations are used for interactions between various interfaces in the network. The global model-based systems engineering market is expected to reach $7310.9 million by the end of 2030 with a CAGR (compound annual growth rate) of 15.8%. A couple of years ago, North America led the global market with a share of 35%, followed by Asia Pacific at 30% and Europe at 20%. Another report shows that cloud-based MBSE software will be widely used compared to on-premises solutions by 2033. MBSE reduces errors, increases transparency, and improves system efficiency across the various stages of lifecycle development. It can be implemented in various industries like IT, manufacturing, healthcare (medical devices), automotive, aerospace, defense, electrical and electronics, etc. In this blog, we’ll read more about MBSE and how enterprises can benefit from partnering with data engineering consulting firms to implement model-based systems engineering in their processes. Main Components of Model-Based Systems Engineering Software MBSE makes analyzing, optimizing, and managing complex systems easier to achieve accurate designs and efficient outcomes. The MBSE framework has many components, tools, and languages. The main components are as follows: Modeling Language The modeling language is required to create the system models. Different modeling languages are available, such as SysML (systems modeling language) and UML (unified modeling language). Engineers may also use domain-specific language for better customization and accurate results. Model Management Tools Model management tools are used to create, organize, and manage the system models, view and analyze the results, set up collaborations between different team members and teams working on the project, and give engineers access to make changes to the data and system models. The model management tools allow experts to work together remotely and track developments. Simulation and Analysis Tools Simulations are a big part of MBSE. These tools allow engineers to create simulations for different combinations and record the outcomes. Then, analysis tools are used to understand the best-case scenario to optimize the system’s performance. Additionally, glitches and errors can be identified and eliminated in the early stages. Requirements Management Tools These tools are used to understand, monitor, and trace system requirements in the product lifecycle development process. Digital data sharing for system interaction is one of the model-based systems engineering fundamentals. Requirements management tools ensure data capturing and sharing are seamless and performed in real time. Integration Tools The MBSE software doesn’t exist in isolation. It has to be integrated with third-party tools and applications like a project management tool, configuration management tool, etc. The integration tools and APIs allow automated data flow between systems and create connections between interfaces to encourage better collaborations. Steps in Model-Based Systems Engineering Approach The MBSE process/ approach has a series of steps, where the enterprise or the service provider lists the requirements of the project. The last step is more of a continuous process where the MBSE consulting company provides long-term support to maintain and upgrade the tools whenever necessary. 1. Understand the Requirements Define the system requirements by identifying the needs of the stakeholders (management, employees, investors, customers, etc.). The system requirements should align with the end goal of the business. MBSE tools with built-in ‘requirements view’ can be used to sort and arrange the data for better understanding. Factors like types of resources, budget, timeline, expertise, etc., should also be factored into the requirements. Businesses should determine if they want an in-house team to work on the project or if they wish to collaborate with data engineering and top MBSE companies. 2. Design the System Based on the requirements model, the expert team will create the system design and workflow. This design has to be reliable, scalable, accurate, and cost-effective. It should also align with the long-term business objectives. For example, the design cannot be rigid or fixed. This increases costs over time as the enterprise has to start from scratch every time it needs to upgrade or enhance the model. Create a detailed flowchart with the components, required tools, and workflows. 3. Behavior Modeling For effective model-based systems engineering training, it is crucial to understand how the system will work in different scenarios. The engineers will develop a model to capture the system’s behavior in various conditions and store the data for further analysis. Having a robust data warehouse or a central data repository is essential to collect, clean, and store the data digitally. Typically, it is recommended to build a cloud-based data warehouse that’s compatible with third-party integrations and can adhere to data security regulations. 4. Analyze the Risks Risk analysis is another vital part of the MBSE process. This step should not be skipped. Here, various risks associated with the system (under development) are identified and recorded. The risks are analyzed by experts to find proper feasible solutions for enhancing the safety, accuracy, and efficiency of the system. Risk analysis helps the team to take the necessary measures in fortifying the process with proactive measures to mitigate risky scenarios. Ultimately, this increases the success rate and results in powerful systems. 5. Validate and Verify Once the models are designed, they have to be validated and verified before being implemented in the enterprise. This is done to ensure the model is accurate and aligns with the requirements, is capable of handling the workload, and can deliver the expected outcomes without affecting cost or quality. Different MBSE tools can be used in this step to create diverse environments to measure and validate the model’s performance. Factors like system requirements, model capacity, expected results, actual results, resources consumed, etc., are

Yashica Chopra
October 07, 2024
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Data Engineering Services vs Warehousing vs Analytics: Pick Your Data Strategy

With data becoming a crucial part of the global industry, it is essential to unlock its full potential to boost your business. Here, we’ll discuss data engineering services, data warehousing, and data analytics to help determine the best choice. Data is the key to a successful business. Instead of storing the data in outdated setups like silos, you can create a central data repository and allow employees restricted access to the datasets. This makes it easier to use the business data for analytics and insights. Employees at all levels can make data-driven decisions by accessing the insights through their dashboards. Data analytics, data warehousing, and data engineering are different yet interlinked concepts used to streamline data collection, storage, and analysis in an enterprise. Statistics show that the global big data and data engineering market is expected to be $75.55 billion in 2024 and predicted to reach $169.9 billion by 2029 at a CAGR (Compound Annual Growth Rate) of 17.6%. However, you may have questions about which service to use for your business. Should you hire data engineering services, or will it be enough to pay for third-party or embedded data analytics solutions? Where does data warehousing fit into the grand scheme of things? Let’s find out in this blog. Is Data Warehousing the Same as Data Analytics? A data warehouse is a central repository or a large database containing massive amounts of business-related data. It can be built on-premises or on the cloud platform. A data warehouse is connected to several internal and external sources as well as third-party applications like business intelligence tools, data analytical dashboards, etc. Data warehousing services include setting up the repository, building data pipelines, streamlining data flow, maintaining the database, and periodically upgrading the systems. Data analytics is the process of converting raw data into actionable insights to make data-driven decisions. It helps see the hidden patterns, trends, and correlations in historical and present datasets. The insights derived are shared with end-users (employees) via data visualization dashboards. Data analytics help shape business processes to deliver better results while consuming fewer resources. It can be used to understand market trends, customer behavior, product performance, employee productivity, etc., and make the necessary changes to achieve business goals. In short, data warehousing is not the same as data analytics. While the data warehouse is used to store and clean data, analytical tools help to understand what the data means and how it can help empower the business. Creating a synergy between data warehouse and data analytics will certainly give you the best results. So, what is the difference between a Data Warehouse Engineer and a Data Analyst? A data warehouse engineer is responsible for managing the entire development lifecycle of a data warehouse. It is a backend process that includes many activities, such as building the warehouse, system connections, ETL, performance management, resource management, dimensional design, etc. A data warehouse engineer works with data scientists, data analysts, and data engineers to ensure the data flow is smooth and seamless across the enterprise. A data analyst uses the data stored in data warehouses and data lakes to review the information, detect patterns, and identify key insights useful for the business. The primary responsibility of a data analyst is to find solutions for various business problems by analyzing historical and real-time data and sharing insights with decision-makers. The data analyst has to collaborate with data warehouse engineers, software developers, and data scientists to run the data-driven model without interruptions or errors. What are Data Engineering Services? Data engineering encompasses various processes like data collection, data storage, data cleaning, and data analysis for large amounts of raw, structured, unstructured, and semi-structured data. It allows data scientists and data analysts to derive in-depth insights using various statistical and analytical methods. Data engineering also includes ensuring that the quality of the datasets is high to prevent inaccurate insights. Data engineering services cover a broader area and include many responsibilities. For example, it can also include data warehousing solutions or a part of the warehousing processes. Typically, data engineering involves the following activities: So, do data engineers do data warehousing? Yes. Data engineers play a role in designing, developing, and maintaining the data warehouse and its connections. However, note that data warehousing services are only a part of data engineering responsibilities. The top data engineering companies provide end-to-end services, right from planning the strategy to maintaining and upgrading the relevant systems, tools, and processes in your business. Data engineers collaborate with other experts like software developers, data warehouse engineers, data scientists, and data analysts to create a robust data model in the enterprise. Which is Better: Data Analytics or Data Engineering? Despite the overlap in some processes and data being the common factor, there are quite a few differences between data analytics and data engineering services. A business can invest in data analytical tools and derive insights to make important decisions. It can partner with a data analytics company to get embedded analytics through customized dashboards without setting up the IT infrastructure in the enterprise. Data analytics as a service is a cloud-based solution where third-party companies handle most of the backend work and share insights and reports with businesses. Data engineering is much more complex and extensive than data analytics. Data engineering consulting companies build data pipelines, set up system integrations, build data warehouses/ data lakes, connect the necessary data analytics and business intelligence tools, and maintain proper data flow across the IT infrastructure. Programming, database management, and cloud computing are part of the services. In today’s competitive scenario, investing in data engineering services is a better option than limiting your business to data analytics. This empowers you to unlock the full potential of data and gain an edge over competitors. It also keeps you one step ahead and capable of making proactive decisions to grab market opportunities or avoid pitfalls. Data Engineering Services vs. Data Warehousing vs. Data Analytics As you can see, the three aspects are different but interconnected on

Manpreet Kaur
September 24, 2024
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Top Data Warehousing Companies Revealed: 11 Players Shaping the Industry

Data warehousing companies help enterprises capture lots of data from many sources for processing. Among the current excellent market leaders that offer an efficient method of managing data and therefore offering the market a competitive advantage for organizations are Snowflake, DataToBiz, Google BigQuery, and Amazon Redshift. Imagine you are flooded with data from the marketing department, the sales department, the finance department, and even the web analytics department. How would you make any sense of it all? If there were few data sources and the volume of data was small in each, then it is possible to combine them manually. What happens when the amount of data streaming in from all the sources becomes unmanageable, and the volumes are too big? That is where data warehousing services come in handy. This technology consolidates all your information into one point, which allows you to get more complete results from processing all interrelated fields at once, taking into account all the information and basing it on full data. Now, let’s examine what a data warehouse is and learn about the 11 leading data warehousing companies globally. This will help you get an overview of how it could streamline and improve the way you apply information within your organization. What is a Data Warehouse? A data warehouse is a central repository that initially supports and handles tremendous historical data collected from various parts of a company. These systems are designed for digital transformation strategies and enable businesses to define trends and patterns that could thus be of strategic importance in decision-making. This is not to say that data warehouses resemble regular databases; however, the former is more appropriate for analyzing large historical information sets. They are faster than traditional methods, enhance the quality of data, and offer rich information in comparison to other means. This helps businesses in several ways: Analysts estimate that the market for enterprise data warehouses (EDWs) shall grow further; the increase is estimated at $39.23 billion from 2024 to 2028. This growth is a result of the rising volume of data available within the different sectors. The increase in data calls for organizations to adopt state-of-the-art cloud data warehousing toolkits in the market to remain relevant. Top Data Warehousing Companies Shaping The Industry To help you pick the perfect solution for your business, let’s dive into some of the best data warehousing companies around the world: DataToBiz DataToBiz is a data intelligence firm based in India offering data warehousing services to manage, store, and analyze large volumes of data to make informed business decisions. Their team of experts offers advanced services in data warehousing consultation, development, integration, and migration, always prioritizing client needs and delivering tailored solutions. Key Features: Things to Consider: Amazon Redshift Amazon Redshift provides adaptable data warehousing services tailored for the AWS cloud, making it a budget-friendly choice for analyzing extensive datasets kept in S3. It provides a user-friendly interface that’s particularly convenient for those familiar with the AWS environment. Key Features: Things to Consider: Google Cloud Platform Google BigQuery stands out among data warehousing companies with its serverless setup, which eliminates the hassle of managing infrastructure. You only pay for the data processing you use, making it both affordable and efficient. It’s engineered to process extensive datasets rapidly and incorporates machine learning to delve deeply into data analysis. Key Features: Things to Consider: Snowflake Inc. Snowflake provides a cloud-based data warehouse software that scales easily with elastic computing for on-demand processing power. It uniquely separates storage from computing, enhancing cost efficiency. Thanks to Snowflake’s support for SQL queries, users already familiar with SQL will find it straightforward to perform data analysis. Key Features: Things to Consider: Microsoft Azure Azure Synapse Analytics, previously known as Azure Data Warehouse, is a modern cloud-based data warehouse software that works closely with other Azure services. This integration forms a cohesive data environment, simplifying the management of data across different platforms. Key Features: Things to Consider: IBM IBM Db2 Warehouse is a dependable and secure data warehousing platform tailored for seamless integration with the wider IBM analytics ecosystem. It’s built to support demanding data workloads with its scalable design and top-tier performance capabilities. Key Features: Things to Consider: Oracle Oracle Autonomous Data Warehouse provides a highly automated data warehousing platform hosted on Oracle Cloud. This platform utilizes machine learning to optimize workloads and efficiently allocate resources, ensuring seamless integration with Oracle’s suite of services. This advanced, self-managing architecture reduces the complexity of data warehouse product operations for businesses leveraging Oracle Cloud. Key Features: Things to Consider: Teradata Teradata stands out among data warehousing companies as a high-performance solution designed for crucial applications. It’s particularly known for its solid security measures, ensuring data safety and adherence to compliance standards—ideal for businesses handling sensitive information. Key Features: Things to Consider: SAP SAP HANA is particularly a data warehouse product that is designed to process data in-memory which makes it much easier for it to provide data as well as analysis faster. This makes it a perfect asset for organizations that require real-time access to information for quick decision-making. Key Features: Things to Consider: Cloudera Among the data warehousing companies, Cloudera has been noted as the best because it provides an open-source data platform capable of customization appropriately for the needs of its clients. It supports virtually any type of data format and data source and while it is relatively easy to use it does require some degree of technical skill to install and maintain. Key Features: Things to Consider: Firebolt Firebolt is a cutting-edge cloud data warehouse that’s highly favored by engineers due to its remarkable speed. It’s crafted to deliver an exceptional user experience through its intelligent storage solutions and efficient query handling. For those familiar with SQL, Firebolt will feel like second nature as it adheres to standard SQL protocols. Key Features: Things to Consider: Conclusion The applicability of the data warehousing system is most appropriate for companies that are interested in going deeper than just filtering information for

Manpreet Kaur
September 12, 2024
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Top 10 Reasons to Prioritize Data Warehousing Services

Data warehousing is like building an organized library for datasets in your organization. It integrates data from various sources, improves data quality, and makes it easy to analyze and make smart decisions upon it. Investing in data warehousing services ensures you can make well-informed decisions that keep you ahead of the competition. As decision-making moves towards relying on data and analytics, the demand for data solutions has increased. Incidentally, 31% of respondents are eager to quickly scale up their analytics spend to support more users and handle bigger data. Additionally, 28% are aiming to focus on improvement in the infrastructure which would support the cloud, on-premise as well and regional computing. But how can you efficiently handle petabytes of data? The answer is the data warehouse. Data warehouses are a kind of central repositories designed for storing and processing huge quantities of information from various segments of an organization. When approaching the question of investing money in data warehousing services, you should be acquainted with what gets you the maximal profit. In this blog, let’s try to break down what is important to know but might be too obvious to think of when getting started with data warehousing services. But first, let’s start with the basics. What is a Data Warehouse? A data warehouse, often called an enterprise data warehouse (EDW), is a central system where businesses keep important information like customer and sales data. This information is stored for analysis and reporting. Data warehouses are essential for generating insights and supporting decision-making through business intelligence (BI). They typically hold both current and historical data that has been collected, transformed, and loaded (ETL) from various sources, including internal and external databases. Essentially, a data warehouse serves as a business’s single source of truth (SSOT) by consolidating data into a stable, standardized system that relevant employees can easily access. These systems are designed for online analytical processing (OLAP) and enable fast, efficient analysis of data from multiple angles. Data warehouses can store vast amounts of summarized data, sometimes reaching several petabytes. Why Should You Have a Data Warehouse? The main benefit of a data warehouse is that it brings together data from various sources into a unified format. This consistency ensures that the data is accurate, leading to well-informed decisions. When data is standardized across the business, every department can generate consistent and reliable results. Traditionally, data warehouses were located on servers within a company’s premises (on-premise). However, nowadays, many data warehouses have moved to the cloud, where they can store and analyze vast data sets. Some popular cloud-based data warehousing platforms include: Data warehousing services often serve as a single, reliable source of truth for businesses, centralizing big data in a secure, stable, and standardized system that’s accessible to the right teams and employees. Reasons To Invest in Data Warehousing Services It is important for an organization that requires information in real time to make the right decisions to use a data warehouse. Still, these reasons can help you determine whether, in your case, data warehousing services are worth the investment. 1. Unlock Data-Driven Potential Decisions are no longer hove on guesswork or instinct – at least they shouldn’t be. Indeed the modern-day leader has the great fortune of having a basis for decision making informed by current data and this is made possible through a data warehouse. For information power to be realized optimally, there must be a loss of data ownership where one department usually owns most of the data. A data warehouse can rectify this scenario, and those in need of some specific details do not need to go through all sorts of hoops or even other departments. When it is set up as a single source organizing all other material, data warehousing services ensure that information seekers are well-equipped to find what they require and use it decisively to chart the destiny of the organization. 2. Harness the Power of Automation Data warehousing services open up opportunities for businesses to explore automation. Automating different parts of operations is gaining traction, especially as people see how it can help avoid costly errors and speed up processes. Market studies suggest that the global industrial automation market could reach $265 billion by 2025, a significant jump from $175 billion in 2020. Data warehouses play a crucial role in supporting these automation efforts. Businesses can use software-driven workflows to automate tasks like data access and transfer, reducing the time needed to gather information for auditors, investors, or other stakeholders. Automation can also speed up data analysis, helping uncover insights much faster. Additionally, it’s possible to automate error detection and logging, making it easier to spot potential issues and address them quickly. By understanding how data warehousing services are used within an organization, businesses can identify the best areas to implement automation. 3. Keep Your Data Secure When data is scattered across multiple locations, makes your security more challenging. Many leaders don’t even know how much data they have or where it’s stored. With data warehousing services, everything is stored in one place, making it easier to track and secure information. Plus, most data warehousing platforms come with built-in security features. Some can block harmful SQL code from outside attacks, while others limit how much data someone can view at once, reducing the risk of unauthorized use. Organizations can also control who accesses the data warehouse and why. This ensures that people only see what’s relevant to their job. Additionally, some data warehouses lock out users who try to log in from unusual locations, making it harder for hackers to take advantage. To effectively implement data warehousing services in your company, it is advisable to leverage digital transformation consulting services for a well-guided and strategic implementation. 4. Enhance Data Quality and Consistency Sustaining high quality and consistency of the data is an essential requirement for your organization. Worse the cloud data is unstructured or inaccurate and hence will not be of much use. A data warehousing system can aid in

Manpreet Kaur
February 28, 2024
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How to setup a data warehouse for manufacturing data?

Data warehouses store data and facilitate quick analysis and reporting for actionable insights. With effective data warehouses, you can gather data from different data sources. In this blog, we’ll find out how manufacturing analytics companies can build a data warehouse for manufacturing data and gain relevant insights. Manufacturing organizations are undergoing transformational changes owing to the exponential growth of data. According to the IDC forecast, the global data sphere is expected to expand by 175 zettabytes by 2025. This massive growth indicates a data-driven world characterized by constant tracking and monitoring. Data plays an important role in highlighting areas for improvement, whether it’s inventory management, production, logistics, and warehouses. The challenge lies in collecting data in real-time and using it efficiently. By leveraging a data warehouse for manufacturing data, companies can store and process vast amounts of data with the help of manufacturing analytics solutions. What is data warehouse for manufacturing ? A data warehouse for a manufacturing company is a digital repository of disparate data sets. It gives a consolidated view of data from different systems such as operational and transactional data management. Manufacturing organizations collect information across different stages of their processes, such as product and process design, assembly, maintenance, and recycling. A data warehouse aggregates structured data from multiple sources, giving accurate data analysis. How to create a data warehouse setup? Here is a quick overview of the steps of building a data warehouse for manufacturing companies Step 1: Understand business requirements Note down the functional and non-functional requirements of your business according to their priority. For example, if your business will expand and grow in the immediate future, scalability must be your top priority. Figure out departmental goals and align them with the project. Assess the existing tech stack and data to get an idea of the current and future needs. Step 2. Investigate source data Define all the data sources and identify the primary sources of record to prevent unnecessary data loading, since specific datasets might be present across multiple storage systems. For example, you can transfer the sales order information from the order management system to logistics software. However, the OMS serves as the single data source, since the logistics software may alter data, compromising the quality of insight. Step 3. Develop conceptual, logical, and physical data models Once you have delineated all the business requirements, you need to create a preliminary enterprise data warehouse model to visualize and represent key business processes and their interrelationships. Make sure you build these models in collaboration with the domain experts to account for industry-specific subtleties. Conceptual data models help to set up relationships among core business entities and outline the information needs of an organization. For instance, a supply chain company might identify entities such as products, customers, shippers, carriers, suppliers, orders, and manufacturers. Logical data models have more elaborate details such as attributes (columns) associated with each business entity. For example, the product’s price Physical data models include primary and foreign keys. A primary key works as a unique identifier within a table, while a foreign key is inserted from one table to another to establish a relationship between tables. Since business operations evolve continuously, it’s imperative to ensure data models remain adaptable. Step 4. Define and create a data warehouse schema Now you need to structure the final version of data modeling into a data warehousing schema. Select the most suitable schema from different schema types, consulting a software architect. Step 5. Deploy a data warehouse architecture gradually When you have a data warehouse schema in place, create a data warehouse architecture. Focus on factors such as cost, security, performance, and scalability to choose a flexible architecture according to business requirements. What are the applications of data warehouses in manufacturing? Manufacturing production and distribution organizations centralize their data using a data warehouse, giving a comprehensive analysis to determine existing patterns and trends, forecast market shifts, pinpoint growth opportunities, identify areas for development, and facilitate strategic decision-making. They face critical decisions regarding in-house production and outsourcing that impact the industry. By using OLAP (Online Analytical Processing) tools within data warehouses, businesses can analyze trends, detect early indicators of potential challenges, and enhance decision-making. Data warehouses monitor product shipments and portfolios, allowing companies to identify product lines and evaluate underperforming ones depending on customer feedback and historical performance metrics. Characteristics of a Data Warehouse The main characteristics of data warehousing in the manufacturing industry typically include: Subject-oriented In a data warehouse, decision-makers (stakeholders, executives, and leaders) analyze data by focusing on specific subject areas, by narrowing relevant data sets. This ensures a clear understanding and streamlined analysis by limiting unnecessary information. Data warehouses are organized on specific subject areas such as customer data and inventory to facilitate analysis. Integrated Data warehouses from disparate sources within an organization are consolidated and standardized in a data warehouse to ensure consistency and coherence across complete datasets. Time-variant Data warehouses store historical data over time, including a temporal element and spanning an extensive time horizon. The immutability of time elements is a crucial aspect of time variance and record key displays time variance. Non-volatile Once data is uploaded in the data warehouse, data is updated to protect it from temporary changes. The data is in read-only form and allows only access and loading functions. What are the four phases of data warehouse design? Manufacturing analytics companies implement the below phases to design data warehouses to ensure the effectiveness of infrastructure. Offline operational database: In this first stage, data is transferred from operating systems to servers. This separation prevents any impact on the performance of the OS, enabling easy data loading, processing, and reporting. Offline data warehouse: During this stage, data is updated periodically since the data is refreshed from the operational database. Real-time data warehouse: At this stage, data warehouses are updated in real-time as transactions occur in the operational database. It involves event-based triggers that send notifications to update records accordingly. Integrated data warehouse: All the transactions are updated

Vaishali
February 22, 2020
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A Complete Guide To Data Warehousing – What Is Data Warehousing, Its Architecture, Characteristics & More!

With the aid of an in-depth and qualified review, the study extensively analyses the most crucial details of the global data warehousing industry. The study also provides a complete overview of the market based on the factors that are expected to have a substantial and measurable impact over the forecast period on the market’s growth prospects. Specific geographical regions such as North America, Latin America, Asia-Pacific, Africa, and India were evaluated based on their supply base, efficiency, and profit margin. This research report was examined based on various practical case studies from different industry experts and policy-makers. It makes use of various interactive design tools such as tables, maps, diagrams, images, and flowcharts for readers to understand quickly and more comfortably. Global Data Warehousing Market Report contains highly detailed data, including recent trends, market demands, supply, and delivery chain management approaches that will help identify the Global Data Warehousing Customer Industry’s workflow. This Report provides essential and comprehensive statistics for research and development estimates, row inventory forecasts, labor costs, and other funds for investment plans. This sector is enormous enough to build a sustainable enterprise, so this Report lets you recognize opportunities for each area in the global data warehousing market. What is Data Warehousing? Data Warehousing (DW) is a process for collecting and managing data from diverse sources to provide meaningful insights into the business. A Data Warehouse is typically used to connect and analyze heterogeneous sources of business data. The data warehouse is the centerpiece of the BI system built for data analysis and reporting. It is a mixture of technologies and components which helps to use data strategically. Instead of transaction processing, it is the automated collection of a vast amount of information by a company that is configured for demand and review. It’s a process of transforming data into information and making it available for users to make a difference in a timely way. The archive of decision support (Data Warehouse) is managed independently from the operating infrastructure of the organization. The data warehouse, however, is not a product but rather an environment. It is an organizational framework of an information system that provides consumers with knowledge regarding current and historical decision help that is difficult to access or present in the conventional operating data store. Characteristics of data warehousing Here is the list of some of the characteristics of data warehousing: 1. Subject oriented A data warehouse is subject-oriented, as it provides information on a topic rather than the ongoing operations of organizations. Such issues may be inventory, promotion, storage, etc. Never does a data warehouse concentrate on the current processes. Instead, it emphasized modeling and analyzing decision-making data. It also provides a simple and succinct description of the particular subject by excluding details that would not be useful in helping the decision process. 2. Integrated Integration in Data Warehouse means establishing a standard unit of measurement from the different databases for all the similar data. The data must also get stored in a simple and universally acceptable manner within the Data Warehouse. Through combining data from various sources such as a mainframe, relational databases, flat files, etc., a data warehouse is created. It must also keep the naming conventions, format, and coding consistent. Such an application assists in robust data analysis. Consistency must be maintained in naming conventions, measurements of characteristics, specification of encoding, etc. 3. Time-variant Compared to operating systems, the time horizon for the data warehouse is quite extensive. The data collected in a data warehouse is acknowledged over a given period and provides historical information. It contains a temporal element, either explicitly or implicitly. One such location in the record key system where Data Warehouse data shows time variation is. Each primary key contained with the DW should have an element of time either implicitly or explicitly. Just like the day, the month of the week, etc. 4. Non-volatile Also, the data warehouse is non-volatile, meaning that prior data will not be erased when new data are entered into it. Data is read-only, only updated regularly. It also assists in analyzing historical data and in understanding what and when it happened. The transaction process, recovery, and competitiveness control mechanisms are not required. In the Data Warehouse environment, activities such as deleting, updating, and inserting that are performed in an operational application environment are omitted. What are the Basic Elements of Data Warehousing? The following are some of the basic elements of data warehousing that should be considered by the data engineering team. ETL Toolkit with Screens ETL is to extract, transform, and load data to the DW. Quality screens are not always used as they are an additional requirement. But these screens process and validate data and the relationship between different data columns or sets. External Parameters Table Using an external parameters table will make it easy to add/ delete/ modify the parameters without affecting the configuration table in the data warehouse or changing the code. Team Roles and Responsibilities The team includes builders, maintainers, miners, analysts, and others who take care of data cleansing, data integrity, metadata creation, and data transportation. Warehouse administration, loading and refreshing data, information extraction, etc., are some functions performed by the team. Data Connectors The data connectors need to be updated and linked to external data sources. Legacy systems may not work with the latest software. Every connection and integration has to be checked and updated regularly. Architecture Between Environments The development environment, production environment, and testing environment should be in sync and align with each other. Differences in this could lead to defective results and loss of time and money for the enterprise. DDL Repository Having a backup is considered essential, at least during the initial phase. However, it is important to carefully consider the structure of the DDL (Data Definition Language) repository for the long term. Tests Building a test environment in advance will help in running a test, even before the data warehouse is fully functional. This helps catch errors and