AI’s A-List : 30 AI Companies to Know in 2025

Artificial intelligence companies offer diverse services to help businesses adopt new technologies across departments and verticals. Here, we’ll discuss the AI companies’ list of top thirty firms to watch in 2025. Artificial intelligence comes in many forms today. Be it conversational AI, responsible AI, or generative AI(LLM), each has a role in our society and business world. It’s no surprise that many businesses, ranging from startups to multinational giants, invest in AI at some level and use it as a part of their processes. While tech leaders develop their own AI models from scratch, other organizations prefer to hire service providers and use customized tools to achieve their goals and gain a competitive edge.  Statistics show that the global AI market is expected to be valued at $214.63 billion in 2024 and is projected to reach $1,339.1 billion by 2030. As artificial intelligence revolutionizes different industries, the estimated annual growth rate is to be around 36.6% between 2023 and 2030, according to Grand View Research. As per McKinsey, AI adoption has increased by 72% in 2024. India has the highest adoption rate of 59%, followed by UAE (58%), and Singapore (53%).  With such encouraging statistics, it’s evident that businesses are actively investing in AI technologies. They partner with reliable AI companies to find the best way forward for AI adoption. AI companies offer services in various ways, such as end-to-end, strategic consulting, managed services, AIaaS (AI as a Service), etc.  In this blog, we’ll look at the top thirty AI companies to consider in 2025.  Top 30 AI Companies for 2025 DataToBiz DataToBiz is a leading artificial intelligence and digital transformation company with ISO certification. As one of the top AI companies in India, it is a certified partner of Microsoft (Gold), AWS, and Google. The company offers diverse solutions like AI product development, computer vision, NLP (natural language processing), LLM (large language model), and machine learning. As an award-winning service provider, the company adheres to various global data security standards and provides tailored services for startups, Fortune 500 firms, SMBs, MSMEs, MNCs, and large enterprises from around the world.  IBM IBM is a global AI and IT service provider offering adaptive solutions for mid to large-scale enterprises. The company builds safe and holistic AI models to encourage businesses to adopt the latest technologies in their establishments. Its services are customized, flexible, and scalable to suit the diverse needs of a growing and multinational organization. Additionally, it offers proprietary platforms like IBM Watsonx for businesses to streamline their processes and automate workflows.  Google Google is a famous tech giant with a global presence. The company has many tools and apps for personal and business use. Apart from the suite of technologies in Google Cloud, it offers an array of AI and ML solutions for clients from different parts of the world. The company caters to startups as well as large enterprises and has something for everyone. Gemini is Google’s generative AI offering and has many models that can be customized to help businesses overcome various challenges.  Microsoft Microsoft is another technology giant with an international market base. Its cloud solution, Azure, is among the top three platforms used globally. The company has varied services to suit the dynamic requirements of startups, SMBs, and large organizations. It assists businesses in confidently adopting AI solutions in all verticals and enables digital transformation. Be it Teams, Dynamic 365, Power Platform, or GitHub, businesses can customize and integrate these solutions to achieve their objectives.  NVIDIA NVIDIA is a popular company known for developing a wide range of computer components. However, it is also an active service provider offering ready-to-use AI platforms in enterprises. The company builds powerful artificial intelligence models for developers, executives, and general IT services. NVIDIA NIM can be used to instantly deploy generative AI and scale the capabilities with ease. The company ensures data security and provides many cybersecurity solutions. It also provides conversational AI and vision AI services.  Amazon Amazon is a global cloud platform (AWS) offering a diverse suite of technologies, tools, applications, and frameworks for SMBs and large enterprises. It works with varied clients and provides direct and indirect services. Like Google and Microsoft, Amazon also offers certification for third-party developers to provide customized AI development and managed services to businesses. The company builds its AI and ML models from scratch to automate workflows, streamline database management, and simplify complex tasks. DataRobot DataRobot is a data science and artificial intelligence company that helps businesses accelerate their AI adoption journey from ideation to implementation. It has many AI platforms that can be tailored to meet the changing requirements of organizations from different industries. The company follows a value-driven approach and sets high standards for businesses to understand and use AI technologies for numerous purposes. BEACON, LATTITUD, Women@DR, Pridebots, ACTNow, etc., are some efficient AI tools developed by the company.  Machina Labs Machina Labs is an AI and robotics company offering a reliable and customizable platform to encourage agile manufacturing on a large scale. The Robotic Craftsman platform has been developed by the company based on advanced AI models and closed-loop controls. The solutions can be scaled, making the company a worthy partner for large establishments and government agencies. The company’s offerings reduce time and cost by creating prototypes quickly. It provides innovation, design, and engineering services as well.  PwC PwC is a global consulting company offering a range of services to clients from around the world. It provides tailored solutions using responsible AI, generative AI, and other IT business services. The company’s cloud-powered services help organizations build secure, flexible, and scalable platforms/ applications to streamline business processes and enhance output. It provides strategy development and consulting services for businesses to make the right decisions using data-driven models.  General Motors General Motors is an automobile manufacturing company with decades of experience in the industry. The company has extended its presence in the AI field by developing different solutions to enhance the safety of using vehicles. It calls itself the

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Top 20 Data Analytics Companies Shaping 2025

Data analytics is vital for organizations from any industry to unlock the power of their data and convert it into actionable insights. Here, we’ll discuss the top twenty data analytics companies worldwide and know their role in helping businesses make data-driven decisions. In a world where a massive amount of data is generated daily, it would be a colossal waste to not use this data to derive meaningful insights, patterns, and trends. Whether you want to understand what customers like or how the market conditions will change over a given period, you can use data analytics to get the necessary insights.  Data analytics is the process of collecting, cleaning, storing, and analyzing datasets from various sources to derive insights that help in making better business decisions. Businesses need to invest in data engineering and data analytics to optimize their processes, improve efficiency, and enhance customer experience. You can build your data analytics model from scratch or partner with a service provider to get managed data analytics services.  Statistics show that the global data management and analytics market is expected to touch $513.3 billion by 2030 at a CAGR (compound annual growth rate) of 16%. Another report indicates that the big data analytics market will be $348.21 billion in 2024 and is likely to reach $924.39 by 2032 at a CAGR of 13%. The estimated growth rates are proof of increasing investment in data analytics.  Naturally, there is a high demand for data analytics companies in India and other countries around the globe. In this blog, let’s take a look at the top twenty data analytics companies offering consulting services and managed analytical solutions for businesses from different industries.  Top Data Analytics Consulting Firms Shaping 2025 DataToBiz DataToBiz is among the leading data analytics companies in India offering an array of services for digital transformation, business intelligence, data engineering, cloud computing, etc., using AI and ML technologies. The award-winning company provides tailored services for predictive analytics, descriptive analytics, customer analytics, supply chain analytics, financial analytics, and many others necessary for businesses to make data-driven decisions in real-time. With clients from numerous industries, DataToBiz has expertise in working with startups, SMBs, MNCs, MSMEs, and large enterprises. It provides data analytics consulting services, implementation solutions, data analytics as a service, and data analytics support and evaluation to help businesses strategize, build, deploy, integrate, and maintain the analytical models in their establishments. Get customized end-to-end data engineering and data analytics services by partnering with the company.  Glassdoor Rating: 4.8 Stars  Accenture Accenture is a global analytics services company offering extensive data services for large enterprises from different parts of the world. It uses artificial intelligence to build data analytics and business intelligence models for clients. By fine-tuning the most suitable models that align with the business requirements, the company ensures clients derive high-quality and accurate insights in real time. Accenture has a presence in different industries and helps organizations migrate data to the cloud, build modern data platforms, scale AI and ML solutions, and revamp the business process using generative AI. The company also helps businesses in reducing failure rates by guiding them with years of experience in the field. This enables businesses to maximize their performance and ROI.  Glassdoor Rating: 3.9 Stars  Wipro Wipro offers data, analytics, and intelligence services for businesses to turn their ambitions into reality. The company uses AI technologies to derive maximum insights from data and help clients use these insights to transform their processes. With clients in many countries, the company has the required experience to combine end-to-end critical capabilities and human expertise to deliver the promised results. Be it strategic advisory services or data engineering and management, Wipro ensures to focus on agility, scalability, and flexibility. It works with organizations from numerous industries. EPM automation and modernization, data-driven intelligence, cybersecurity, cloud computing, and sustainability are some other services offered by the company. It believes in connecting art and science, data and people, and intelligence and creativity to help businesses identify market opportunities and gain an edge over competitors.  Glassdoor Rating: 3.6 Stars  TCS TCS (Tata Consultancy Services) is one of the top data analytics companies offering data management, cybersecurity, IoT (Internet of Things), and enterprise solutions for organizations from several regions. The company helps businesses accelerate growth and results through managed analytics delivered in real time. It builds custom solutions that assist clients in developing new products and services, optimizing internal processes, elevating customer experience, and improving business outcomes. TCS actively manages business data while ensuring the systems are automated to scale as per the client’s needs. It builds a robust data ecosystem for businesses to initiate digital transformation and take advantage of cloud technologies. The company has developed platforms like TCS Datom™, TCS Dexam™, TCS Daezmo™, and TCS business analytics solutions to provide tailored services to enterprises from different sectors.  Glassdoor Rating: 3.7 Stars  Mu Sigma Mu Sigma is one of the reputed data science companies in the global market.  It uses artificial intelligence, machine learning, and computer vision to help clients make data-driven decisions. The company’s intelligent automation models are designed to align with the complexities of the changing market conditions. With experience in many industries, the company has built a model called The Art of Problem Solving System™ for modern businesses. Mu Sigma calls itself a Decision Sciences Company as it goes beyond what most data analytics companies offer. It accelerates the journey from (raw) data to decisions by streamlining the entire process with advanced technologies. All its solutions are suitable for cross-industry applications at various verticals. The company prides itself on offering out-of-the-box solutions to businesses and systemizing decision-making.  Glassdoor Rating: 3.3 Stars  LatentView Analytics LatentView Analytics is an advanced AI and data analytics solution company that transforms businesses and helps them excel in the digital world. By harnessing the power of data and analytics, the company supported organizations from industries like tech, retail, CPG, industrials, and financial services. Apart from data management and data science, the company offers a range of

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Fortune 500 Favorites: Top 10 AI Consulting Services Firms

Artificial intelligence is the key to success, but knowing how to use it makes all the difference. That’s where AI consulting companies help. Here, we’ll discuss the top ten AI consulting services firms that work with Fortune 500 companies around the globe. Artificial intelligence is no longer an option for businesses. It is a must-use technology if organizations want to survive market competition and stay active in this ever-changing global scenario. However, every business, big or small, cannot and may not want to build the AI model from scratch or maintain an in-house team of AI experts.  In such instances, it makes sense to hire AI consulting services and partner with reputed service providers to integrate artificial intelligence with business processes and embrace digital transformation. This resulted in various firms offering AI consultancy solutions for startups, SMBs, MSMEs, and large enterprises. Even some of the Fortune 500 Companies prefer to collaborate with AI consulting firms to achieve their goals.  So, how big is the AI consulting market? Well, according to BCC Research, the global AI consulting services market is expected to touch $64.3 billion by 2028 at a CAGR (compound annual growth rate) of 34.2%. As per Grand View Research, the global AI market is $196.63 billion in 2023 and is estimated to grow at a CAGR of 36.6% from 2024 to 2030.  In this blog, we’ll take a look at the top AI consulting companies that work with Fortune 500 businesses and clients from various other industries. What is AI Consultancy? The biggest barrier to AI adoption is the lack of expertise and the cost of building the models from scratch. Choosing the wrong technologies or approach can lead to more challenges and losses instead of empowering the business. But how can an enterprise overcome these issues without increasing expenses?  That’s where AI consulting services come into the picture. An AI consulting company is a service provider that assists various businesses in understanding and adopting AI technologies to achieve their goals. From creating the strategy to ensuring long-term maintenance and support, the consulting company has many roles and responsibilities. However, not all firms offer the same services. For example, some AI consultancy companies are limited to strategy formation or tech support. Some companies offer end-to-end support, including AI product development services and staff augmentation services. The top four (Deloitte, EY, PwC, and KPMG) cater exclusively to MNCs and large enterprises and have premium charges. Fortune 500 Companies may prefer to work with such service providers or opt for other leading AI consulting companies. Top 10 AI Consulting Services Firms DataToBiz DataToBiz is a leading AI product development company offering end-to-end and tailored services for clients from various industries, such as manufacturing, finance, EdTech, healthcare, telecommunication, surveillance, logistics, retail and eCommerce, IT, energy, hospitality, and many more. The award-winning company offers full-scale solutions for businesses to address and overcome challenges. Generative AI, NLP, computer vision, ML modeling, etc., are a part of the company’s AI services.  Additionally, DataToBiz also offers custom services for data analytics, data engineering, business intelligence, and IT staff augmentation. It is a reliable and cost-effective company providing AI consulting for MNCs, Fortune 500 firms, startups, SMBs, MSMEs, and large-scale enterprises. The company has ISO certification, along with compliance with SOC 2, GDPR, CCPA, DSS, NIST, and HIPPA. From strategy formation to deployment, integration, and maintenance, the experts from the company handle each process with seamless efficiency.  Glassdoor Rating: 4.8 Stars  IBM IBM is among the top AI consulting companies in the world and works with Fortune 500 firms, multinational companies, and large enterprises. It offers adaptive solutions for clients from healthcare, manufacturing, IT, finance and insurance, marketing, etc. The company uses an open enterprise-grade AI approach to provide flexibility and scalability for clients while prioritizing safety and compliance with holistic AI. Using its decades of experience in the field, the company has developed various artificial intelligence solutions like Watsonx, Granite, and others.  IBM’s AI consulting services help businesses redesign workflows and scale the systems quickly using hybrid cloud technologies. Automation, IT modernization, edge computing, data science, ESG reporting, data management, and cloud security are some of the extensive services offered by the service provider. The company’s generative AI services help different departments like customer service, HR, marketing, finance and accounting, etc. It encourages businesses to become more successful using sustainable processes.  Glassdoor Rating: 4.0 Stars  Accenture Accenture is an AI services company helping various large enterprises and Fortune 500 firms reinvent their processes with data and artificial intelligence. It partners with leading tech providers like Google AWS, SAP, Oracle, Salesforce, etc., to help businesses build a comprehensive ecosystem to streamline their internal systems. The company’s generative AI consulting services are much in demand in the global market along with other services like cloud computing, cyber security, supply chain, sustainability, Metaverse, finance and risk management, and many more.  Accenture has a presence in several industries, such as aerospace, banking, capital markets, energy, consumer goods, public sector, insurance, life sciences, etc. It emphasizes responsible AI for businesses to bridge the gap between intention and execution. The company understands the importance of aligning the AI strategy with business requirements and long-term goals. The services are customized to suit each client’s requirements and scaled for large enterprises across all verticals.  Glassdoor Rating: 3.9 Stars  LeewayHertz LeewayHertz provides AI consulting services and development solutions for enterprises to amplify operational efficiency and streamline workflows. The company works with clients from finance and banking, manufacturing, retail, insurance, hospitality, healthcare, startups, consumer electronics, and other sectors. It offers AI and ML strategy development services, custom app development, Copilot development, AI integration, enterprise AI development, fine-tuning LLMs, data engineering, and generative AI development services.  LeewayHertz has expert AI professionals to provide end-to-end development services for Fortune 500 companies, large enterprises, and mid-scale businesses. It prioritizes speedy development and deployment by following agile methodologies. The company’s advanced tech stack makes it easy to complete and deliver projects within the pre-determined schedule without compromising quality or exceeding

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13 Staff Augmentation Service Models to Consider in 2025

Staff augmentation is useful, cost-effective, and a scalable solution for businesses to expand their teams whenever necessary. Here, we’ll discuss the different types of staff augmentation services and how these can help in tailoring the IT staffing strategy in a business. There’s a high demand for information technology (IT) services in various industries. Businesses from around the globe have been adopting new technologies to revamp their processes and compete in changing market conditions. Statistics show that the IT market size has grown from $8508.63 billion in 2023 to $9039 billion in 2024 at a CAGR (compound annual growth rate) of 6.2% and is expected to reach $12417.21 billion by 2028 at a CAGR of 8.3%.  In such instances, the demand for IT professionals from various domains has also increased. Programmers, engineers, data analysts, data scientists, software developers, AI and ML experts, etc., are also being hired by non-IT businesses. However, not all enterprises follow the same hiring patterns. Instead of recruiting professionals permanently, many organizations are opting for outsourcing, managed services, or staff augmentation services.  In this blog, we’ll talk about staff augmentation and the various types of staff augmentation services available in the market. What are Staff Augmentation Services? Simply put, staff augmentation is the process of temporarily using professionals from outside the establishment to work with your existing teams on a particular project. They augment your internal team capacity and capabilities to help you achieve your goals. IT companies offer staff augmentation services to help other IT and non-IT businesses access talent and technology through cost-effective means. Rather than spending money on full-scale recruitment for temporary jobs, you can sign a contract with a staff augmentation company to quickly hire as many experts as you want and pay them only for the project/ duration. What is an Example of Staff Augmentation? For example, a manufacturer wants to digitally transform the systems and set up IoT (Internet of Things) devices with factory equipment for data collection. The manufacturer can hire IT staff augmentation services to build a team of programmers and data analysts to handle the project. The selected external experts will collaborate with the enterprise managers to understand the requirements and deliver the results. The manufacturer will have to pay salaries only for the duration of the project (or choose a relevant plan to pay for the services). Most staff augmentation service providers can build teams in 48-72 hours and they start working on the project in around a week. This reduces the time to market and eliminates unwanted expenses for the manufacturer.  Types of Staff Augmentation Services Staff augmentation services can be truly diverse and tailored to suit your business requirements at different times. This variety has made it a popular staffing method in the global market. Traditional Staff Augmentation Traditional staff augmentation involves hiring experts based on the problems faced by in-house teams. The experts usually work on-site and sign a contract to remain part of the team until the issue is sorted. It’s pretty much like a contract job, except the access to talent is limited to the region.  Offshore Staff Augmentation Offshore staff augmentation is almost the opposite of the traditional model, as you can access experts from around the world to work on your project. The temporary employees belong to another country (which is usually across the ocean, hence the term offshore) and work from their locations remotely. Managing time zones and cultural differences can be a concern if you don’t partner with reputed service providers in offshore staffing.  Onshore Staff Augmentation Onshore staff augmentation is similar to the traditional model except that the experts can belong to neighboring countries as well. You have access to a wider talent pool but not to the extent of the offshore model. For example, US companies can hire external employees from Canada, Mexico, or Latin American countries.  Remote Staff Augmentation As the name suggests, the experts will work remotely and collaborate online to interact with your in-house employees. They can belong to the same region or live on the other end of the world. The services are offered remotely so that they don’t have to visit your office to get the job done. This method is cost-effective, though it needs a reliable internet network and wider bandwidth.  Agile Staff Augmentation Agile staff augmentation involves building a team of experts from various domains to manage a project from beginning to end. For example, in analytics staff augmentation, the team will consist of a programmer, AI engineer, data analyst, data engineer, data scientist, business intelligence analyst, etc., to build, deploy, integrate, and maintain the data architecture. The teams are created based on your requirements.  Resource Augmentation Resource augmentation combines staff augmentation and other resources. It is a model developed to help businesses bridge the resource gap in their organizations without spending too much money on building everything from scratch. The resources offered by the service provider include talent, technology, infrastructure, etc. While it is similar to outsourcing, resource augmentation provides more flexibility and can be for the short term.  Temporary Staff Augmentation As the name suggests, temporary staff augmentation services are time-bound and used for short-term requirements and simple projects that can be completed quickly. It is a great model to address the immediate needs and concerns in an establishment. Sometimes, external experts are brought in during the last phase of a project to sort out the issues and release the product into the market.  Project-based Staff Augmentation Project-based staff augmentation involves hiring experts for the entire project. Typically, team augmentation services are project-based since complex projects require more team members and diverse talent. You can easily scale the teams without incurring extra overhead costs or delaying the project to wait for new additions. The project-based model can be for the short-term, mid-term, or long-term.  Long-term Staff Augmentation Long-term staff augmentation involves hiring external talent to work with your businesses for an extended period, which could be several months or a few years. Organizations prefer the long-term

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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 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 connectivity

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Manufacturing Transformation: 11 Leading Staff Augmentation Companies

The manufacturing industry is going through a revolution with new technologies revamping processes to increase efficiency. Here, we’ll discuss the top staff augmentation companies that work with manufacturing enterprises from different parts of the world.  The manufacturing industry has been thriving after many hurdles in the last couple of years. Statistics show that the manufacturing market output is estimated to be $34.8 trillion in 2024 and is expected to touch $36.7 trillion by 2029. With such progress, it’s no surprise that many enterprises are actively adopting digital transformation and investing in IT solutions to streamline their internal processes.  From automation to AI-based manufacturing, data analytics, robotics, and much more, all these can help a manufacturer in many ways. But how can the enterprise set up and use new technology? Who will continue to manage the IT systems?  While outsourcing and managed services are some options, another method is IT staff augmentation. Simply put, the manufacturer hires a temporary team to work on the project and deliver the results. This team comes from reputed companies and has the necessary expertise to handle complex situations. With IT staff augmentation services, an enterprise can gain access to top talent without wasting too much time and money on the recruitment process. Additionally, these teams can be hired for short-term or long-term and through flexible pricing models offered by staff augmentation companies.  So, what are the top staff augmentation companies with experience in the manufacturing industry?  Read on to find out!  Top Staff Augmentation Companies for Manufacturing Enterprises DataToBiz DataToBiz is an AI, BI, and data analytics company offering staff augmentation in manufacturing, logistics, transportation, surveillance, and other industries. It has a team of certified and experienced data analysts, AI and ML engineers, software developers, BI developers, and dedicated project managers with a proven track record. The company offers tailored services for staff augmentation, data science, business intelligence, data engineering, big data analytics, data warehousing, and many more solutions. Being a certified partner of Azure, Google Cloud, and AWS makes this award-winning company a suitable IT staffing partner for manufacturing enterprises. DataToBiz is one of the leading staff augmentation companies and takes care of all aspects from screening potential candidates to deploying them, sharing reports, and delivering the project as per the terms. The IT staffing services are categorized into project-based hiring, end-to-end staffing, dedicated development teams, and specialized skill acquisition. The pricing varies based on the chosen staff augmentation model. Use the company’s estimator tool to get a free quote for your staffing requirements.  Clutch rating: 4.8 Stars  Cyntexa Cyntexa is an IT service provider and a logistics staffing agency helping various businesses bridge the talent gap and achieve their goals. The company focuses on leveraging technology through its people to optimize the clients’ business processes, create innovative solutions, and enhance customer experience. The IT staffing solutions by the company help bring world-class talent to manufacturing enterprises in less than a week. Be it consultants, developers, architects, engineers, or analysts, businesses can create a team of experts from different domains. Cyntexa specializes in Salesforce, Google Cloud, AWS, Azure, ServiceNow, and nintex technologies. Enterprises can build a team in just four steps by sharing their requirements with the company. With transparent pricing and flexible scaling options, the company converts its in-house experts into on-premises and remote teams for various clients.  Clutch rating: 4.8 Stars  Insight Global Insight Global is one of the best staff augmentation companies as it offers much more than finding the right talent for its clients. The company supports businesses from end to end and even afterward. With access to talent in around fifty countries, the company has a global presence and works with businesses from manufacturing, automotive, construction, education, and other industries. Additionally, it also caters to professionals who want to become a part of IT staff augmentation teams and advance in their chosen career paths. Insight Global’s team augmentation services follow a five-stage process, starting with the client submitting its requirements. The company continues to offer support services after the onboarding is successful. Over the years, it has worked with more than a thousand Fortune Companies. Enterprises can opt for remote teams as well as temporary employees who work on-premises. The company offers three staff augmentation models – short-term contracts, long-term contracts, and permanent hires.  Clutch rating: None yet   Yalantis Yalantis is an IT company offering software engineering services, staff augmentation, and accelerated innovation for businesses from different industries like manufacturing, transportation, FinTech, real estate, etc. Be it data science, IoT (Internet of Things), business intelligence, or IT infrastructure development, the company delivers stable, scalable, high-quality, and functional digital products to its clients. IT staff augmentation services by the company cover dedicated developers, frontend developers, backend developers, etc., and build quality teams with diverse talent. Yalantis also offers IT courses and training programs to help professionals from the industry enhance their skills and update their knowledge. The company’s five-stage staff augmentation services focus on providing customized tech teams for businesses based on their requirements and budgets. It also provides support services for continuous improvement, reviews the teams’ performances, and makes recommendations to enhance business success.  Clutch rating: 4.8 Stars  Azumo Azumo is a nearshore software development company offering IT staffing solutions for manufacturing enterprises and business organizations from other industries like FinTech, gaming, blockchain, healthcare, etc. It emphasizes teamwork and shared values to amplify the results for clients. It is a remote-first company and helps businesses set up their processes without making huge investments to build the infrastructure on-premises. As one of the popular staff augmentation companies, Azumo staffs mid-career software developers, engineers, etc., to offer various services in fields like artificial intelligence and machine learning, app development, data engineering, cloud computing, etc. The company’s dedicated teams are a part of its staffing services. It handpicks experts and carefully creates a team based on each client’s requirements. The IT staffing teams can be scaled up and down based on the changing project needs, timeline, budget, etc. The company is

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Artificial Intelligence in Insurance – The Future of Risk Management

The intersection of artificial intelligence in insurance creates a new era of innovation. AI-powered technologies disrupt traditional insurance models, leading to more personalized, efficient, and affordable services. AI is currently being integrated at a soaring rate with Insurance. Around 77% of insurers incorporate AI in their operations this year compared to 61% in 2023. This is the case because AI presents the opportunity to bring radical improvements to the operational model that insurers use to design, price, distribute, and service their products. For example, the current use of robust AI solutions, such as LLMs, is planned by 67% of insurance organizations; the same can be said about future implementation. Opportunities for using generative AI are estimated at $15 billion in the insurance and finance industry by 2025 and at $32 billion by 2027. McKinsey thinks that, at most, Artificial Intelligence could contribute up to $1.1 trillion US dollars to the overall contribution of insurance to the annual global GDP. Insurers are just starting to identify insights and trends as digitalization speeds advance and the amount of data insurers potentially analyze increases. Artificial intelligence insurance companies are not just a trend in insurance but the reality of the business in the foreseeable future. But, why exactly is the topic of Artificial Intelligence in Insurance works? Let’s find out! How Artificial Intelligence in Insurance Works? Digital transformation in insurance is undergoing with the adoption of the Internet of Things (IoT), artificial intelligence (AI), robotics, and other advanced technologies, significantly changing operational methodologies. Here’s the AI is involved in the insurance process: Customer Services Traditionally, the insurance industry relies on human expertise and manual procedures, particularly for processing claims and issuing new policies. The growing use of consumer-connected devices—like cars, fitness trackers, home assistants, and smartphones—generates extensive data, enabling insurers to more accurately assess and understand current and potential customers’ needs. Claims AI in Insurance claims processing reduces time and costs for insurers. By quickly analyzing data and images, AI helps agents with accurate estimates and frees them to focus on more complex tasks. Underwriting and Pricing Automated underwriting solutions with AI help insurers set competitive rates and speed up pricing updates, leading to more personalized and efficient pricing. Sales AI boosts sales by creating new digital channels and integrating with sales tools, simplifying the process for agents and brokers and enhancing customer outreach. Fraud Detection AI insurance in fraud detection enables insurers to analyze vast amounts of data from diverse sources, assess risk factors, and spot anomalies. Advanced algorithms can identify suspicious activities and highlight claims that need further scrutiny, potentially catching issues that might be overlooked in human-only reviews. Risk Prevention AI analyzes historical data and market trends to effectively predict and manage risks. By examining IoT data and past claims, AI provides insights to prevent future issues and tailor risk management strategies. New Products and Channels Insurance products are developed faster with the introduction of AI usage-based products like ‘pay as you drive,’ which change depending on driving behavior and conditions. Moreover, AI enables the delivery of innovative insurance solutions that can be specifically created for an individual based on his/her risk factors. Factors Driving Adoption Of Artificial Intelligence in Insurance AI-driven solutions are boosting insurers’ market share and profitability, driven by several key factors. Advancements in AI and Machine Learning Technologies  Rapid progress in AI/ML development services is unlocking new data value. Large language modeling (LLM) allows insurers to streamline AI for insurance claims processing and enhance fraud detection. Generative AI, although in its early stages, combines data, tools, and reasoning to provide valuable insights, promising significant benefits for the insurance sector. Increased Availability of Diverse Data Sources  The surge in diverse data sources offers more material for value extraction. Insurers increasingly use third-party data, including consumer credit, marketing information, social media activity, purchasing behavior, criminal records, past claims, and weather data. This supplemental data improves underwriting, risk modeling, claims processing, and marketing practices. Growing Demand for Enhanced Customer Service  AI insurance chatbots and virtual assistants enable insurers to deliver prompt, personalized support around the clock. Natural language processing (NLP) powers these front-line solutions, providing continuous basic support and allowing human agents to handle more complex issues. Capabilities of Modern Cloud Data Platforms  Modern cloud data platforms like Snowflake offer efficient, cost-effective data storage and processing. Supporting structured, semi-structured, and unstructured data, these platforms allow insurers to use diverse datasets for AI model training and refinement. Scalable compute power ensures effective management of insurance workflow automation. Benefits of Implementing Artificial Intelligence in Insurance Artificial Intelligence in the insurance sector offers benefits like reduced biases and streamlined processes but also faces challenges. It helps personalize coverage and detect fraud, yet lacks transparency and may inadvertently introduce new biases. 1. Reduction of Biases through AI Traditional insurance rates often consider personal factors like credit scores, income, education, occupation, and marital and homeowner status, which can disadvantage low-income buyers despite being unrelated to collision risk. Artificial intelligence insurance models can be trained to exclude these factors, thereby reducing biases. 2. Streamlined Insurance Processes with AI Intelligent automation in insurance enhances efficiency in the insurance sector by swiftly detecting fraudulent claims and expediting the underwriting process, which assesses potential customers’ risk levels. Utilizing historical data, Artificial intelligence insurance models can process new customer information and claims more quickly and cost-effectively than human employees. 3. Flexible Insurance Options Enabled by AI Wearable technology allows insurers to monitor driver behaviors for companies like Uber and Lyft. Safer driving habits can lead to lower premiums, and devices can activate insurance coverage only when drivers are active, reducing costs and providing coverage for service workers who would otherwise need personal policies. 4. Promotion of Safer Driving Habits Artificial intelligence and machine learning in the insurance industry can analyze data from connected devices to identify patterns in driving accidents or mishaps. Insurers can then offer recommendations to companies to reduce the frequency of accidents and costly claims. 5. Lack of Transparency in AI Models AI-based risk models,

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Is Azure Infrastructure as a Service The Future of Cloud Computing?

Microsoft Azure is one of the top three cloud computing platforms used by various business organizations. Here, we’ll discuss the basics, use cases, benefits, and examples of Azure infrastructure being the future of cloud computing. Microsoft Azure is a popular cloud platform with an extensive ecosystem of tools, technologies, applications, storages, frameworks, etc., useful for diverse requirements. It is among the top three cloud solutions in the global market.  According to statistics, Azure’s market share reached 24% in 2024, and the customer base grew by 14.2% from 2023. Since its launch in 2010, Azure has been a tough competitor. Azure, AWS (Amazon Web Services), and Google Cloud continue to be the top three cloud platforms for SaaS, PaaS, and IaaS solutions. The 2024 Azure Market Report states that Azure has 350,000 customers for cloud computing services.  Azure infrastructure as a service (IaaS) can streamline business processes across all verticals and reduce the pressure of maintaining and upgrading the systems on-premises. But what are Azure infrastructure services? Where do data engineering services come into the picture? How can Azure IaaS help a business?  Let’s find out in this blog. What is IaaS on Azure? Infrastructure as a service (IaaS) is a cloud computing service where the entire IT infrastructure (storage, networking, backup, applications, virtual machines, etc.) is hosted on a remote cloud server. It allows businesses to save money through the pay-on-demand pricing model. Businesses can reduce the expenses of maintaining the data silos in each department and upgrading the hardware periodically. With IaaS, organizations also gain access to real-time insights and can quickly embrace advanced technologies.  Azure infrastructure as a service encourages flexibility, scalability, and reliability of the IT system in an enterprise. From a startup to an established enterprise, any business can invest in Azure IaaS and build a robust cloud-based IT infrastructure. Existing setups can be migrated to the cloud, or a new infrastructure can be built and deployed on the Azure cloud. This depends on various factors like business requirements, timeline, budget, legacy systems, long-term objectives, etc. Testing, implementation, integration, storage, data backup and recovery, web app development, etc., are a part of the services. Since it is a complex process, most organizations prefer collaborating with certified Microsoft Azure partners to handle the task. This ensures complete access to the tools and apps in the Microsoft marketplace and the necessary expertise to keep things running seamlessly. A certified partner has the necessary experience and skills to customize Azure cloud infrastructure to suit the business needs. What is Azure Data Engineering? Data engineering is the process of designing, building, and maintaining data systems to collect, store, and analyze large datasets and derive meaningful real-time insights. It combines many responsibilities and the core part of the data-driven model. Azure data engineering services are provided by certified data engineers who offer end-to-end support in managing data and data systems on the cloud.  An Azure data engineer will integrate, transform, and consolidate data from multiple sources to make it possible to derive insights. From building data pipelines to handling structured, semi-structured, and unstructured data in large quantities and helping stakeholders understand the analytical reports, a data engineer has much to do.  Data engineering companies also offer Azure IaaS solutions and help businesses build the data warehouse/ data lake on the cloud platform. The experts create the necessary system connections to make the insights accessible to employees through customized dashboards. This helps in making proactive data-driven decisions.  Benefits of Azure Infrastructure as a Service (IaaS) Enhanced Data Security and Encryption  Azure infrastructure encryption offers built-in security features and capabilities to keep the business data and systems safe from unauthorized access. It also helps organizations adhere to data privacy regulations based on geographical location and industry standards. With Azure, businesses can reduce the risk of cyber threats and protect user data.  Centralized and Cloud-Based Infrastructure  Maintaining individual IT systems with data scattered throughout the enterprise is not only cost-intensive but also stressful. This reduces data quality and can result in outdated or incorrect insights. With Azure infrastructure as a service, organizations can build a unified and centralized IT infrastructure that anyone in the enterprise can access. It is a simplified and efficient way to run the business processes.  Fewer Hardware Maintenance Costs Maintaining legacy systems can be a costly exercise for businesses as they become outdated over the years and will no longer be compatible with new technologies. Organizations have to periodically invest in new hardware and pay for maintenance services to make sure they can access the latest tools in the market and gain a competitive edge. By switching over to Azure infrastructure as a service, most business hardware can be eliminated. Employees access the virtual machines from their devices and can work remotely. Streamlined Operations  One of the biggest advantages of data engineering services and IaaS is automation. Instead of wasting time and resources on manually performing repetitive actions, businesses can automate even complex tasks. This reduces the workload on employees and minimizes the risk of human error. Additionally, the workflows are streamlined into an order that maximizes efficiency without compromising quality or control.  Remote and Restricted Access  Remote working has become a norm in recent times. Employees have to have access to business systems, data, tools, and dashboards irrespective of their location. At the same time, people without authorization (hackers, scammers, etc.) should not be allowed to gain control over the business processes. Azure IaaS balances these two aspects with ease. It encourages remote collaboration between teams but also provides restricted access to confidential data.  Standardized Applications  Azure infrastructure as a service encourages the standardization of business processes and applications by developing a unified platform to manage all tasks and systems. Furthermore, the third-party apps and tools belong to the Microsoft ecosystem and follow the same standards. This results in improving consistency in performing day-to-day activities and achieving the desired results every time.  Flexibility and Scalability  Another benefit of Azure infra developer is the flexibility it offers to businesses. The

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Top 13 AI Implementation Partners to Consider in 2025

As businesses increasingly recognize the importance of AI in driving growth and efficiency, choosing the right AI consulting services partner is important. This blog highlights the top AI implementation partners based on their expertise and industry presence.  According to Grand View Research, the global artificial intelligence market was valued at USD 196.63 billion in 2023 and is expected to grow at a CAGR of 36.6% in 2023. As businesses are adopting AI across different industry verticals, it is essential to partner with experienced AI implementation firms that offer the necessary expertise and resources. In this blog, we’ve compiled a list of the top AI implementation partners you must partner with to develop remarkable AI solutions. Top Companies Offering AI Consulting Services in 2025 DataToBiz DataToBiz is a leading AI implementation partner that helps you develop innovative products to solve your business challenges and achieve great results. Their team of experts offers robust AI consulting solutions, such as NLP, product development, and machine learning. The company uses the latest technology stack and offers real-time operational intelligence. Partnering with DataToBiz for AI development ensures you get a business solution that aligns with your objectives. The experts understand your queries, build strategy, collect data, and create models that fulfill the objectives, ensuring efficacy and efficiency.  Client Collaborations: Flipkart, ICICI Bank, McDonald’s, NeilsenIQ, NPCI Industries: Manufacturing, healthcare, retail, ecommerce, and IT Average Ratings: 4.7 (Clutch) DataRobot DataRobot has been named a 2024 Gartner Magic Quadrant winner for data science and machine learning platforms. The company specializes in generative and predictive AI solutions and offers a comprehensive suite of tools designed to streamline the AI lifecycle, from data preparation to model deployment and governance. DataRobot offers tailored solutions for various industries like finance, healthcare, retail, and manufacturing. It integrates AI workflows into a single platform, enabling organizations to build, govern, and operate AI solutions efficiently with due consideration to government and compliance.  Client Collaborations: MARS, Boston Children’s Hospital, Warner Bros, and Tokio Marine Kiln Industries: Food industry, healthcare, mass media, retail, and financial services Average Ratings: 4.6 (Gartner)  Hugging Face Hugging Face Inc. is an American company that offers AI consulting for MNCs. Its main offerings include the Transformers library, which provides a vast collection of pre-trained models optimized that you can use for NLP tasks such as text classification, translation, and summarization. The company offers cloud-based services through its Hugging Face Hub platform, which allows users to host, share, and deploy AI models in the cloud. The platform integrates with machine learning frameworks such as TensorFlow and PyTorch, making it easier for developers to include models in their workflows.  Industries: Text generation and classification  Average Ratings: 4.0 on Gartner DataTech DataTech is an AI product development company that offers advanced solutions in AI and data analytics. It is popular for its notable work in machine learning, deep learning, and natural language processing. It uses a platform where developers can create contextual chat and offer customizable chat solutions tailored to specific business needs.  Datatech is a great partner if you are looking for someone if you are looking for a company to handle image processing and social listening tools. The experts use features such as facial recognition and people counting to process images, making it easy to make valuable decisions.  Client Collaborations: IT Motif Inc, Zydus Hospira Oncology, QX KPO Services, Meghamani Dyes and Intermediates  Industries: IT, healthcare, BPO, manufacturing  Average Ratings: 5.0 (Clutch) InData Labs InData Labs is a data science and analytics consulting firm that offers AI-powered solutions to businesses. It offers advanced and reliable solutions for AI, NLP, machine learning, generative AI, and data engineering solutions.  The company offers AI solutions for different sectors, such as advertising, financial services, entertainment, retail, and ecommerce.  InData Labs is a certified AWS Partner. The company builds and scales cloud solutions within the AWS ecosystem, solving data and analytics challenges. It offers a collaborative partnership with clients, ensuring easy alignment and communication throughout projects. Client Collaborations: GSMA, Entrance, Naexas, Asstra  Industries: Telecommunications, IT, logistics, retail, and ecommerce  Average Ratings: 4.9 (Clutch)  Markovate Markovate is a generative AI consulting services company specializing in AI and digital transformation solutions.  It is known for its modern AI-driven solutions tailored to specific business needs, such as predictive analytics and machine learning applications. The company also offers development services for decentralized applications and blockchain solutions. Markovate’s team has experienced professionals with expertise in mobile technology, AI, blockchain, and digital marketing. They follow a collaborative approach and implement agile methodologies to adapt to changing project requirements. Client Collaborations: Synervoz, Nown, Hawaii Revealed, Aisle24, Trapeze Industries: Software development, IT, travel and tourism, retail  Average Ratings: 3.6 (Glassdoor)  BrainPool.AI BrainPool.AI is an AI services company that provides comprehensive AI solutions tailored to various industries such as construction, finance, healthcare, real estate, retail, and marketing. It uses AI to empower businesses by using artificial intelligence to enhance operational efficiency and drive innovation. The developers tailor strategies to integrate AI into business operations, create prototypes, and provide expert advice on AI implementation. Some of its other services include custom GPT development, back-office automation, data structuring, design process optimization, data migration, data governance, and design architecture. Client Collaborations: Stair Craft Group, Ocula Technologies, Crown and Paw, Nvidia  Industries: Construction, technology, Artificial Intelligence, retail  Average Ratings: 4.8 (Clutch)  EY (Ernst & Young) EY (Ernst & Young) is an AI implementation giant that offers a wide range of AI consulting services to help organizations use AI to drive growth. The company offers consulting and advisory services to organizations planning to integrate AI into their operations.  Some of its core services include strategy development, use case identification, and implementation support for AI solutions. EY also focuses on RPA (Robotic Process Automation) and combines AI with RPA to automate complex business processes, making it easy to make data-driven decisions. It also offers cybersecurity solutions that detect threats, analyze data for unusual patterns, and improve threat response times.  Client Collaborations: MNCs, NGOs, and startups  Industries: Technology, healthcare, manufacturing, and financial services 

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Modern Data Engineering 101 – Benefits, Use Cases, Examples!

This blog talks about modern data engineering 101 and how organizations are using it to their advantage to extract the full potential of their data. We’ll discuss its benefits and relevant examples of how data engineering services have transformed various industries. Data engineering plays an important role due to the large data volumes and increasing dependence on data-driven decision-making. The global big data analytics market size was valued at USD 307.51 billion in 2023 and is expected to grow from USD 348.21 billion in 2024 to USD 924.39 billion by 2032 at a CAGR of 13%. “Data as a product is very different from data as an asset. What do you do with an asset? You collect and hoard it. With a product, it’s the other way around. You share it and make the experience of that data more delightful.” – Zhamak Dehghani, author of Data Mesh, Delivering Data Value at Scale. In this blog, we’ll discuss modern data engineering and how organizations are using it to make the most out of their data. What is Modern Data Engineering? Modern data engineering includes building, managing, and optimizing scalable data pipelines to handle large volumes of data from multiple sources. It processes data in real-time and uses cloud-based architectures and tools. These tools support data integration, transformation, and storage for advanced analytics and decision-making. Importance of Modern Data Engineering Data engineering helps organizations handle and organize data so that data analysts and scientists can easily analyze it. Here’s why data engineering services are important: The main part of data engineering involves managing data pipelines and ETL (Extract, Transform, and Load) processes. Data engineers build and maintain these pipelines to ensure clean and valid data is available to data analysts. This helps teams access data easily, gain insights, and make informed decisions, enhancing business growth and output. Benefits of Modern Data Engineering Imagine you’re trying to get the most out of your data, but it’s scattered all over the place. That’s where data engineering comes in. Now let’s understand some benefits data engineering solutions bring with them. Use Cases of Modern Data Engineering Some of the potential use cases of data engineering I’ve seen are: Personalized recommendations Subscription-based streaming services such as Netflix and Amazon Prime offer personalized recommendations to their viewers. These companies collect and organize user data and use machine learning to offer personalized recommendations. Fraud detection Banks and financial institutions use data engineering to prevent fraud. They gather vast amounts of transaction data, and with the help of advanced algorithms, they can spot suspicious patterns in real-time, preventing fraud before it even takes place. Predictive maintenance Manufacturing companies use data engineering to keep machines running smoothly. The sensors on equipment collect data continuously, and engineers use these insights data to predict when a machine may fail, preventing breakdowns. Customer behavior analysis eCommerce store owners can track customer purchases, their preferences, and browsing behavior. Further, they analyze these trends with the help of data engineering to create personalized marketing campaigns to offer personalized recommendations. This is the reason why you often see ads for things you’re interested in since the companies already have relevant data to target you. Real-time data analysis Businesses can collect, clean, and verify data through automated data pipelines. This makes it easy for data analysts to centralize large volumes of data by breaking down silos and making informed and strategic decisions. Businesses can detect trends, respond to market changes, and optimize their operations for better returns. Machine learning Machine learning uses large amounts of data to train artificial intelligence (AI) models and make them more accurate. Data engineers use data pipelines to transport data across different sources, ensuring it’s clean and ready for analysis. These data models are used in various applications, from personalized recommendations to fraud detection, and much more. Skill Set of Modern Data Engineer The skill set of a modern data engineer includes the following data engineering tools, technologies, programming languages, and frameworks: Database management systems: Data processing frameworks: ETL tools: Cloud platforms: Data warehousing solutions: Programming languages: Frameworks and methodologies: How do Data Teams Implement Modern Data Engineering? Data engineers integrate all your data into models that support operations and analytics, enabling your company to extract data-driven insights. Further, they understand existing infrastructure and data needs and offer personalized solutions and services to help you make the most of your data. They use different data engineering tools to consolidate data from multiple sources to manage data efficiently. The data teams create data models and algorithms that ensure these models are fully functional and work smoothly. How Does Modern Data Engineering Work? Data engineering involves designing and building data pipelines that convert raw, unstructured data into organized datasets. These pipelines are important elements for a reliable data system, built to meet specific needs of businesses. Data engineers manage data pipelines to ensure users get accurate and reliable data.  The data integration pipeline has the following steps:  Best Practices for Modern Data Engineering Conclusion Data engineering is no longer optional today, it has become a necessity.  By converting data into meaningful actionable insights, data engineering helps businesses to make data-driven decisions. This not only enhances operational efficiency but also helps you deliver customized experiences quickly. Data teams not only add more value but also facilitate the development of the right solutions for various problems. People Also Ask (FAQs) What is modern data engineering? Modern data engineering includes designing, building, and maintaining scalable and efficient data systems. These systems support business intelligence, analytics, and data-driven decision-making by using advanced tools and practices to process vast amounts of data from various sources in real-time. What is the latest in modern data engineering? Some trends in data engineering that are popular now and will continue include: What is the salary of a modern data engineer? According to Glassdoor, the average salary for a data engineer in the United States is $1,31,939 per year. They typically get additional cash compensation, averaging $27,346 and ranging between $20,509 and

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