azure data engineering Archives

Yashica Chopra
May 07, 2025
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Top MBSE Consulting Companies Driving Transformation in the USA

Model-based systems engineering revamps the engineering models through digital modeling and system design. It is an effective solution to simplify systems for making informed decisions. Here, we’ll discuss the top MBSE consulting companies offering expert services in the US. Data and design systems are complex and contain many elements that power the entire setup. As businesses expand, these systems become harder to maintain and track, especially if you don’t use new technology. MBSE (Model-Based Systems Engineering) is a relatively new solution that simplifies engineering systems by introducing digital modeling to improve transparency, increase collaboration, enhance communication, and promote efficiency. While MBSE is useful in most industries, it is actively adopted by enterprises from aerospace, aviation, automotive, manufacturing, defense, government, etc. Statistics show that the global MBSE tool market is expected to be $2.5 billion in 2025 and is projected to grow at a CAGR (compound annual growth rate) of 15%. The same report indicates that the market share is widely divided between top players like Siemens, IBM, and Dassault Systèmes (CATIA Magic), though other vendors also offer comprehensive MBSE software. According to Business Research Insights, the global model-based systems engineering market was $3.31 billion in 2024 and is projected to reach $13.09 billion in 2033 at a CAGR of 16.5%. In this blog, we’ll read more about model-based systems engineering and the top ten MBSE consulting companies in the USA you can partner with. What is MBSE and Why is it Important for Businesses? MBSE uses digital modeling and simulation to design systems, create connections, and build a network of applications, software, and interfaces. This is done to increase its efficiency and maintain clear documentation of the processes. It also reduces the risk of errors and promotes better collaboration between teams. Furthermore, MBSE makes the systems and outcomes more consistent and increases overall quality. Unlike traditional systems, it is easy to upgrade and maintain on-premises and on cloud platforms. Typically, MBSE consulting companies in the USA offer tailored solutions to help enterprises implement model-based systems engineering in their processes. They use the advanced MBSE tools and frameworks developed by tech giants to provide relevant services to clients. With MBSE, you can focus on the core operations and factors like product design, safety standards, efficiency, resource optimization, risk management, etc., instead of managing complex systems and writing lengthy documentation. A few MBSE examples are as follows: Top MBSE Consulting Companies in the USA DataToBiz DataToBiz is one of the leading data engineering companies with a global client base. The award-winning company offers custom MBSE consulting and end-to-end services for businesses from diverse industries like automotive, manufacturing, healthcare, and many more. As a certified partner of Microsoft (Gold), Google, and AWS, the company specializes in building tailored data warehouses to create a single source of truth to power the model-based systems engineering solution. It has a team of experienced and certified professionals to set up and implement the latest MBSE software solutions available in the market. The company can build the infrastructure on Microsoft Azure or AWS to create a reliable, flexible, scalable, and agile cloud-based model to introduce digital engineering in businesses. DataToBiz also provides data analytics, advanced analytics, and data visualizations through powerful AI and ML tools like Power BI, Tableau, etc. The company’s expertise in IaaS solutions has helped numerous enterprises seamlessly adopt MBSE solutions to achieve their objectives. STC Arcfield STC is an Arcfield company working with Model-Based Systems Engineering tools to help clients increase understanding and make informed decisions. Its model engineering and digital engineering practitioners have years of experience in providing advanced MBSE solutions in the government and commercial sectors. The company revolutionizes systems engineering through digital modeling to simplify complex systems and streamline the decision-making process. It provides the required tools, services, and training to clients in the US to drive innovation and growth. STC offers MBSE as a service along with consulting solutions to leverage the digital engineering ecosystem in a range of disciplines. The company provides complete support to clients to use cutting-edge technologies and custom-designed MBSE tools in their organizations to achieve their goals. It also develops next-gen digital engineering solutions for the US Army. Intercax Intercax is one of the well-known MBSE consulting companies in the USA. It is a pioneer global innovator in the field of digital engineering. The experts have worked on creating a robust solution to streamline and link engineering models for varied requirements. The company launched Syndeia, its digital thread platform for integrated digital engineering, in 2014. The software connects, compares, and synchronizes existing engineering tools in a business. It can be integrated using Rest APIs and has many server-based applications along with user interfaces to share the outcomes with end users (employees). The company also provides training services to use the software. Additionally, it offers IT support services and custom software development solutions for clients. Intercax has more products like ParaMagic®, a plug-in for Magic Draw to enable dynamic SysML models. It has a presence in the aerospace, automotive, healthcare, defense, IoT (Internet of Things), manufacturing, and other industries. SSA Systems Strategies and Analysis (SSA, Inc.) is a small business owned by women and minorities. It is a systems engineering and program management company offering cost-effective services and MBSE consulting solutions for clients from aerospace, IT, and other industries. The company helps with designing, building, integrating, and operating enterprise-wide complex MBSE software to handle the ever-changing requirements of a business. Expert engineering can identify potential problems in the early stages of design to reduce the risk rate. The company collaborates with Intercax to provide self-paced online training programs for MBSE consulting. SSA, Inc. empowers clients to increase system performance and quality standards, boost revenue, and get into better collaborative partnerships with other businesses. Its consulting services focus on MBSE engineering best practices and the ways to implement effective tactics to achieve business objectives. ETAS ETAS empowers tomorrow’s automotive software with its next-gen solutions and tailored services for Model-Based Systems Engineering adoption and implementation in the industry.

Yashica Chopra
December 18, 2024
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Your 10 Step Guide to Data Domination in 2025

Data domination allows businesses to make informed and data-driven decisions using real-time actionable insights. Here, we’ll discuss the guide to data domination through tailored data engineering services for your business. Data domination is the process of streamlining and effectively managing datasets to benefit from the data-driven model and make proactive decisions. It is a blueprint to implement data engineering and management solutions in your enterprise. So does it mean data engineering necessary is in 2025? Absolutely! Statistics show that the global big data and data engineering market will be $75.55 billion in 2024 and expected to reach $169.9 billion by 2029 at a CAGR (compound annual growth rate) of 17.6%. It is evident data engineering services are not only necessary for 2025 but will continue to play a prominent role even afterward. Of course, data domination is easier said than done. You should consider many factors like data collection methods, data ingestion, safe and secure data storage, long-term maintenance, troubleshooting, etc. Not addressing these concerns can lead to failed data management systems. That would be counterproductive, isn’t it? Luckily, you can overcome these challenges and more by partnering with a reliable data engineering company. Hire experts from the field to mitigate risks and increase your success rate. Let’s check out the detailed guide to data domination in 2025. Before that, we’ll find out how to overcome the challenges in data engineering. Challenges for Data Domination and How to Overcome Them As per Gartner, poor data quality leads to a loss of $15 million annually for businesses around the world. Avoiding this and many other pitfalls is easy when you make informed decisions. By overcoming these challenges, you will be several steps closer to data domination and gain a competitive edge. Data Ingestion Data ingestion refers to feeding data from multiple sources into your systems. It is one of the initial steps of data engineering solutions. The data ingested is then cleaned, processed, and analyzed to derive insights. A few challenges you might face are as follows: These issues can be sorted by in-depth planning. Instead of immediately connecting the data sources to your systems, take time to identify the right sources and set up data validation and cleaning processes (ETL and ELT). Automate the process to save time and reduce the risk of human error. Determine your budget and long-term goals when deciding the data ingestion method. Migrate to cloud platforms for better infrastructure support. Data Integration Data integration depends on how well the various software solutions, applications, and tools used in your enterprise are connected to each other. Naturally, data will be in different formats and styles depending on the source. A few more challenges are listed below: For seamless data integration, you should first create a data flow blueprint. Then, identify software solutions that are not compatible with others (legacy systems) and modernize or replace them. Since you have to integrate different data types (structured, unstructured, and semi-structured), you should invest in data transformation tools. Azure data engineering services cover all these and more! Data Storage The biggest concern about data storage is scalability. With so much data being collected in real time, where will you store it? Moreover, how much can your data storage centers handle the load? What to do with old data? How hard will it be to retrieve data from the storage centers? Here are more challenges to consider: Choosing the wrong data storage model can adversely affect the entire data engineering pipeline. Migrating to cloud servers is an effective way to overcome these roadblocks. For example, Azure, AWS, or Google Cloud platforms offer flexible, scalable, and agile data warehousing solutions. You can set up a customized central data warehouse that can be upgraded whenever necessary. A data warehouse is capable of handling large datasets and can quickly respond to queries. Data Processing Traditional data processing tools cannot handle diverse data. They also cannot process large datasets quickly. Processing data from silos can lead to data duplication and reduce the accuracy of the results. There are more data processing concerns, such as: Modern problems require modern solutions. Instead of struggling with traditional tools, switch over to advanced technologies and AI-powered data processing tools. Similarly, data silos have to be replaced with a central data repository like a data warehouse or a data lake. Partnering with AWS data engineering companies will help you identify the right tools and technologies to process data in real time and share the insights with employees through customized data visualization dashboards. Data Security and Privacy Data brings more challenges with it. After all, you are using data that includes confidential information about your customers, target audiences, competitors, and others. How to ensure this data is safe from hackers? How to avoid lawsuits from others for using their data for your insights? Common data security concerns are: Data security should be included as a part of data warehousing services. Data encryption, data backup, disaster recovery management, authorized access to stakeholders, security surveillance, security patch management, and employee training (to create awareness about cyber threats), etc., are some ways to overcome these challenges. The service provider will also create a detailed data governance guide to provide the framework for regulatory compliance. 10-Step Guide to Data Domination in 2025 Step 1: Define Business Goals Always start at the beginning. Lay the foundations clearly and carefully. What do you want to achieve through data domination? How will your business improve through data engineering? What are your long-term objectives? Be detailed in defining the business goals so that your stakeholders and service providers understand the requirements. Step 2: Hiring a Data Engineering Company Data domination is not an easy task. It’s a multi-step and continuous process that requires expertise in different domains. While you can build a team from scratch by hiring data engineers, it is cost-effective and quick to hire data engineering or a data warehousing company. Make sure it offers end-to-end services and works remotely. Step 3: Create a Data Domination Strategy

Yashica Chopra
November 03, 2024
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9 Building Blocks of Data Engineering Services – The Fundamentals

Data engineering is the key for businesses to unlock the potential of their data. Here, we’ll discuss the fundamentals aka the building blocks of Data Engineering Services, and the role of data engineering in helping businesses make data-driven decisions in real time. Data engineering services are gaining demand due to digital transformation and the adoption of data-driven models in various business organizations. From startups to large enterprises, businesses in any industry can benefit from investing in data engineering to make decisions based on actionable insights derived by analyzing business data in real-time. Statistics show that the big data market is expected to reach $274.3 billion by 2026. The real-time analytics market is predicted to grow at a CAGR (compound annual growth rate) of 23.8% between 2023 and 2028. The data engineering tools market is estimated to touch $89.02 billion by 2027. There’s no denying that data engineering is an essential part of business processes in today’s world and will play a vital role in the future. But what is data engineering? What are the building blocks of data engineering services? How can it help your business achieve your goals and future-proof the process? Let’s find out below. What are Data Engineering Services? Data engineering is the designing, developing, and managing of data systems, architecture, and infrastructure to collect, clean, store, transform, and process large datasets to derive meaningful insights using analytical tools. These insights are shared with employees using data visualization dashboards. Data engineers combine different technologies, tools, apps, and solutions to build, deploy, and maintain the infrastructure. Data engineering services are broadly classified into the following: Azure Data Engineering Microsoft Azure is a cloud solution with a robust ecosystem that offers the required tools, frameworks, applications, and systems to build, maintain, and upgrade the data infrastructure for a business. Data engineers use Azure’s IaaS (Infrastructure as a Service) solutions to offer the required services. Finding a certified Microsoft partner is recommended to get the maximum benefit from Azure data engineering. AWS Data Engineering AWS (Amazon Web Services) is a cloud ecosystem similar to Azure. Owned by Amazon, its IaaS tools and solutions help data engineers set up customized data architecture and streamline the infrastructure to deliver real-time analytical insights and accurate reports to employee dashboards. Hiring certified AWS data engineering services will give you direct access to the extensive applications and technologies in the AWS ecosystem. GCP Data Engineering Google Cloud Platform is the third most popular cloud platform and among the top three cloud service providers in the global market. From infrastructure development to data management, AI, and ML app development, you can use various solutions offered by GCP to migrate your business system to the cloud or build and deploy a fresh IT infrastructure on a public/ private/ hybrid cloud platform. Data Warehousing Data warehousing is an integral part of data engineering. With data warehousing services, you can eliminate the need for various data silos in each department and use a central data repository with updated and high-quality data. Data warehouses can be built on-premises or on remote cloud platforms. These are scalable, flexible, and increase data security. Data warehousing is a continuous process as you need to constantly collect, clean, store, and analyze data. Big Data Big data is a large and diverse collection of unstructured, semi-structured, and structured data that conventional data systems cannot process. Growing businesses and enterprises need to invest in big data engineering and analytics to manage massive volumes of data to detect hidden patterns, identify trends, and derive real-time insights. Advanced big data analytics require the use of artificial intelligence and machine learning models. 9 Building Blocks of Data Engineering Services Data Acquisition Data ingestion or acquisition is one of the initial stages in data engineering. You need to collect data from multiple sources, such as websites, apps, social media, internal departments, IoT devices, streaming services, databases, etc. This data can be structured or unstructured. The collected data is stored until it is further processed using ETL pipelines and transformed to derive analytical insights. Be it Azure, GCP, or AWS Data Engineering, the initial requirements remain the same. ETL Pipeline ETL (Extract, Transform, Load) is the most common pipeline used to automate a three-stage process in data engineering. For example, Azure Architecture Center offers the necessary ETL tools to streamline and automate the process. Data is retrieved in the Extract stage, then standardized in the Transform stage, and finally, saved in a new destination in the Load stage. With Azure Data Engineering, service providers use Azure Data Factory to quickly build ETL and ELT processes. These can be no-code or code-centric. ELT Pipeline ELT (Extract, Load, Transform) pipeline is similar but performs the steps in a slightly different order. The data is loaded to the destination repository and then transformed. In this method, the extracted data is sent to a data warehouse, data lake, or data lakehouse capable of storing varied types of data in large quantities. Then, the data is transformed fully or partially as required. Moreover, the transformation stage can be repeated any number of times to derive real-time analytics. ELT pipelines are more suited for big data analytics. Data Warehouse A data warehouse is a central repository that stores massive amounts of data collected from multiple sources. It is optimized for various functions like reading, querying, and aggregating datasets with structured and unstructured data. While older data warehouses could store data only tables, the modern systems are more flexible, scalable, and can support an array of formats. Data warehousing as a service is where the data engineering company builds a repository on cloud platforms and maintains it on behalf of your business. This frees up internal resources and simplifies data analytics. Data Marts A data mart is a smaller data warehouse (less than 100GB). While it is not a necessary component for startups and small businesses, large enterprises need to set up data marts alongside the central repository. These act as departmental silos but with seamless