Data Engineering Archives

Data Strategy for 2026: What Data-Driven Leaders Must Get Right

2026 won’t reward just “Data-Driven” leaders. It will reward the prepared ones… Data has moved from a support function to core business infrastructure. Mid and top-level executives and decision-makers cannot ignore the benefits of data-driven insights to make the most of market opportunities, avoid risks, and ensure customer satisfaction. Moreover, every organization, whether a startup or a multinational company, generates data daily through various activities, both directly and indirectly. This data can be collected and used to understand patterns, trends, correlations, etc., which help in making more relevant and accurate decisions. This data-driven model has gained popularity over the last few years, leading to an increasing adoption of data analytical tools. Statistics show that the global data analytics market size is expected to be $94.36 billion in 2025, with a CAGR (compound annual growth rate) of 33% to touch $345.30 billion by 2030. Cloud and real-time analytics are the fastest-growing segments in the global market, clearly showing that more and more businesses are preferring cloud-based advanced data and analytics systems to build robust data-driven models in their organizations. Data-driven leaders have much to do in this dynamic landscape, where every technology changes often, and data literacy is compulsory across the enterprise for them to be successful. From CTOs to innovation offers, decision-makers in different departments and levels should be involved in building a comprehensive data architecture with a seamless workflow to facilitate access to analytical insights throughout the organization. While this is by no means impossible, it does come with a set of challenges that require technical and domain expertise. That’s why CEOs prefer to partner with third-party and offshore service providers for tailored data analytics consulting services and support solutions. In this blog, we will read about the top factors the data-driven leaders need to focus on in 2026 to transform their operations and achieve success. 9 Factors Data-Driven Leaders Should Focus on in 2026 Having a detailed data analytics roadmap is vital for data-driven leaders and C-level executives to unlock the full potential of their business data and use it to make smart decisions in real-time. Data Infrastructure Foundation The most vital part of data strategy implementation is the foundation you lay for it, the IT infrastructure with various tools, technologies, frameworks, and people. Here, you determine ways to ensure consistent data quality across the systems to derive accurate insights by analyzing large amounts of data in a quick time. You should also focus on establishing data security layers, creating data governance documentation, and so on. Risk assessment and management, budgeting, transparency, etc., are also important. Build Data Culture Data-driven leaders should focus on building a data-driven culture within the organization before revamping and transforming the systems. This involves clear communication with space for discussions (two-way interaction) and employee training programs to generate interest in the new processes and motivate them to embrace data and technology. It is part of the data literacy program, where employees learn how to work meaningfully with data to derive analytical insights and make informed decisions. Ethical and Responsible AI Implementing AI for business leaders is complex, as it also considers the need for an ethical AI framework. Implementing data-driven models and advanced technologies comes with certain challenges about data security, privacy, etc., which, if ignored, can result in legal complications. Hiring data analytics consulting services ensures that you can be mindful of the global regulations and prioritize compliance. It also involves building transparent systems, using accountable processes, and ensuring fairness (by selecting the training data carefully to eliminate inherent bias and prejudice). AI Implementation and Integration How you integrate the new systems with existing ones is also a part of the data science roadmap. For example, a CTO has to be sure that their ideas are tangible and can give the expected results before scaling them throughout the enterprise. This might require prototyping and MVP development. These services are also offered by AI companies. Additionally, the use cases should be prioritized based on the business vision and objectives before the systems are integrated into the IT infrastructure. In some instances, the legacy software has to be modernized or replaced before advanced solutions can be implemented. Scaling the Architecture When you plan to adopt the data-driven model, you should consider the future of the data science roadmap and where you want to be in the next few years. That’s because the systems you implement should be capable of handling the changing requirements. The data architecture has to be scaled to align with your future objectives without the need for a complete overhaul. Furthermore, many modern transformations give a higher ROI in the long run. Agility and scalability have to be a part of the data-driven model development in the enterprise. Centralized Data Systems Data-driven leaders need to make the datasets and insights available to employees across different departments and levels. Instead of using the outdated silos with duplicated data, building a central repository like a data warehouse or a data lake will streamline the data and workflow in the organization and allow employees to have authorized access to quality datasets that deliver real-time actionable insights to make smart decisions. Data Analytics Democratization CTOs and chief data officers should ask the data pipeline development service providers to setup self-servicing systems and democratize data. This allows employees to use the data for analytics and reports without having in-depth technical knowledge. For example, the latest AI-powered analytical tools accept input in human languages rather than technical querying formats. This reduces the load on the IT teams to constantly send queries to the tools on behalf of employees from other departments. Real-Time Insights Real-time insights refer to the process of accessing actionable and graphical reports instantly after sending a query. Data-driven leaders cannot afford to wait for days or even hours for the request to be processed before they can make a critical decision. The market conditions could change by then, leaving the business more vulnerable to losses and missed opportunities. Powerful AI analytical tools support

14 Top Data Analytics Companies Structuring ETL for Customer Analytics

ETL pipelines are a part of data warehousing and data engineering services used to streamline data for analytics. Here, we’ll discuss the fourteen leading data analytics companies structuring ETL for customer analytics for businesses from around the world. Analytics are no longer optional for a business; they are mandatory for using business data to derive meaningful insights for informed decision-making. Whether it is customers, competitors, or the market, you can use data analytics to process large volumes of data to identify trends, patterns, correlations, etc., which give you a competitive edge. Customer analytics, especially in today’s world, where customers have high expectations from businesses and don’t like to settle for less. Setting up analytics is a multi-step process that includes data collection and processing as well as data storage. This requires ETL (extract, transform, load) solutions or pipelines that allow data flow from the sources to the central repository (data warehouse, data lake, etc.), which is integrated with analytical and visualization tools. According to Global Growth Insights, the global ETL market was valued at $582.07 million in 2025 and is expected to reach $ 1,285.22 million by 2034, growing at a compound annual growth rate (CAGR) of 9.2%. The same report also says that more than 68% of organizations use cloud-first strategies, while 66% prefer automation-based ETL for faster integration and analysis. But what is ETL architecture? How do data analytics companies offer ETL development and maintenance services to organizations? Which companies should you hire as your ETL partner? Let’s find out in this blog. What is ETL? Simply put, ETL is a three-step process of extracting, transforming, and loading data, moving it from the source to the destination systems, such as a data warehouse or analytical tools. Many companies offer ETL/ ELT/ data pipeline solutions as a part of end-to-end data engineering services. By partnering with certified service providers, you can set up automated ETL pipelines powered by artificial intelligence and machine learning technologies. Instead of manual processes that take too much time, money, and resources, you can derive meaningful and actionable insights in real-time, building an automated ETL architecture. This is how data analytics companies provide ETL solutions tailored to your industry standards and business objectives. Extract In this phase, data is copied from various sources (internal and external), such as CRM, ERP, marketing tools, website, APIs, files, streaming sources, databases, etc. This is done without disrupting other internal processes. Moreover, the extracted data is unstructured and will be in different formats, types, etc. Transform In this phase, the collected raw data is transformed into useful information. It involves steps, such as data cleaning, where duplicates and errors are removed, standardization, where all the collected data is formatted into a single type with tags, application of business rules and logic, and data integration, where all this data is merged to resolve conflicts and prepared to be loaded into a unified interface. Load In this phase, the transformed data is moved to the destination location, which is usually a data warehouse (on-premises or on the cloud) and stored for regular use. From here, it is sent to analytical tools and dashboards. Data can be loaded into the repository in different ways: incremental load, full-time load, and real-time load. You can select the frequency based on the data volume and budget. Leading Data Analytics Companies Structuring ETL DataToBiz DataToBiz is one of the leading global data analytics companies offering customer data integration, automated ETL and data pipeline, and many other solutions for SMBs, MSMEs, and large enterprises from diverse industries. The award-winning company has ISO and SOC Type II certification and assures data security as well as regulatory compliance. The experts include built-in security layers in the ETL pipelines to mitigate risk and make the entire data architecture secure. The company starts by auditing the existing systems and provides a data health scorecard, and builds systems based on this to strengthen the weak areas and make it more robust and scalable. DataToBiz is a certified partner of Microsoft (Gold), AWS, Google Cloud, etc., and has access to the latest tools and technologies to deliver real-time and consistent customer analytics to clients. SPEC India SPEC India is a software development company empowering businesses to embrace digital technologies and innovation. It offers customized data analytics services and builds ETL frameworks that can be scaled as per the client’s requirements. As an ISO-certified company, it follows strict data security standards and promises high-level security for all its solutions. The company’s ETL development solutions help organizations unlock their true potential and make informed decisions in real-time. SPEC India can handle complex and large-scale requirements seamlessly to deliver actionable insights for customer analytics. From consulting services to implementing the ETL workflow design, the company takes care of all steps in the process. It also handles data warehouse and data lake implementation. GrayMatter GrayMatter is an industry analytics solutions provider with a global client base. It offers simple, structured, and scalable solutions for business intelligence, big data, and analytics across industries. The company’s data pipeline development services are cloud-based and offered as ETL as a service with end-to-end security and compliance. It aims to bridge the gap between ETL tools and actually implementing them in an enterprise to streamline data flow and encourage businesses to embrace self-service BI and analytics. GrayMatter’s service model enables organizations to seek outside help at any point or stage in the process. The internal teams don’t have to waste time and resources on complex ETL tasks. It is an ISO-certified company. Hyperlink InfoSystem Hyperlink InfoSystem is a digital transformation service provider delivering top-tier IT services and tech solutions for several clients. The company believes in agility, accountability, and integrity, and follows a customer-first model to deliver promised outcomes to clients. It has access to the latest ETL and big data tools to build, deploy, and maintain the data architecture in an enterprise. The company’s cutting-edge analytical tools convert data into intelligence reports that enable data-driven decision-making across the business. Hyperlink

8+ Industry Leaders Building Data Engineering Pipelines in 2026

Data pipelines are essential for streamlining data flow within an organization and enabling real-time, data-driven decision-making. We’ll discuss the top industry leaders who empower organizations to build robust data engineering pipelines in 2026. Data is the core of any business in today’s world. You cannot rely on outdated systems, truncated silos, and cost-intensive legacy software to convert raw data into actionable insights. No organization can afford to make incorrect decisions based on poor-quality insights. That’s why, from SMBs to large enterprises, many businesses are adopting data engineering solutions to streamline data and workflow, automate data pipelines using advanced technologies, and make smart decisions in real-time. According to Fortune Business Insights, the global data pipeline market was valued at $10.01 billion in 2024, projected to reach $12.26 billion in 2025, and is expected to grow at a Compound Annual Growth Rate (CAGR) of 19.9% to reach $43.61 billion by 2032. But what exactly are data engineering pipelines? Data engineering is a complex end-to-end solution that combines several processes and technologies. From data collection to ETL pipeline development, data storage and management, and third-party integrations for data analytics and visualizations, it empowers you to unlock the true potential of your business data on a large scale. Data pipelines are a key component of data engineering, as they are the channels for data movement across the enterprise, and where raw data is cleaned and transformed for analytics. In this blog, we’ll read about the ten industry leaders building automated data engineering pipelines for diverse businesses. Benefits of Automated Data Pipelines The primary purpose of data pipelines is to move data from the source to the destination (data warehouse, data lake, etc.) while performing other activities such as cleaning and transforming the datasets. It may sound similar to the ETL pipeline (extract, transform, load). However, ETL/ ELT processes are a subset of data pipelines. Manually handling such activities is time-consuming and stressful. The data could no longer be relevant by the time the insights are derived manually. Hence, the process is automated using AI and ML technologies. Data pipeline automation offers many benefits, such as the following: When you partner with data engineering pipelines service providers, you can revamp the internal operations by building and maintaining a robust data architecture on-premises, in the cloud, or a combination of both. Top Industry Leaders Building Data Engineering Pipelines in 2026 DataToBiz DataToBiz is a leading data engineering and business intelligence company offering tailored data pipeline development services to clients from around the world. It believes in building strong data foundations for organizations to facilitate seamless automation and scalability. The award-winning company starts by auditing the existing business systems to determine their health and then creates a comprehensive strategic plan to build and deploy automated data pipelines. As a certified partner of Microsoft (Gold), Google, and AWS, it has access to the vast ecosystems with countless tools and technologies. DataToBiz is also an ISO and SOC Type II-certified company with regulatory compliance to CCPA, GDPR, etc. The company works with startups, SMBs, large enterprises, expanding businesses, and Fortune 500 firms from varied industries and provides custom solutions with long-term support services. Quantiphi Quantiphi is an AI-first digital engineering company that empowers organizations to transform their businesses and benefit from more market opportunities. It combines cutting-edge technologies, industry experience, and advanced solutions to help clients overcome even the most complex business challenges. The company’s data engineering pipelines are built on robust cloud foundations and are optimized, secure, and automated to deliver seamless outcomes throughout the enterprise. Additionally, the data and cloud solutions developed by the company are pluggable and extendable across different use cases and require only minimal changes to the configuration. As an award-winning company, Quantiphi assists enterprises in reducing expenses, increasing efficiency, and deriving more accurate insights for smart decision-making. It is a partner to various tech giants such as AWS, Azure, NVIDIA, Oracle, Google Cloud, etc. DataForest DataForest is a product and data engineering services provider with a global client base. The company applies data science techniques and the latest technologies to deliver customized data-driven products to clients to optimize the infrastructure and improve data management. It has vast experience in advanced software engineering and large-scale data analysis, as well as business automation. The company’s AI data pipeline services offer automated and optimized pipelines for real-time data processing, streaming analytics, and promoting faster decisions. DataForest orchestrates end-to-end data pipeline movement and automated ETL (extract, transform, load) processes with minimal intervention. It also ensures reliability, transparency, scalability, data quality, and governance frameworks. The company works with extensive data ETL pipeline technologies to provide tailored solutions to clients. Addepto Addepto is a leading AI and data consultancy helping businesses handle increasing data volumes with ease using AI, ML, and other latest technologies. Its data pipeline engineering services streamline and automate the process of collecting, transforming, storing, and converting raw data into actionable insights. The company’s full-stack and end-to-end solutions include artificial intelligence and business intelligence processes that are aligned with each client’s requirements. Addepto follows a tried and tested approach to understanding what organizations want and delivers the expected outcomes. It works with a plethora of tools and technologies, collaborates with leading cloud providers, and offers long-term maintenance services. The company deals with all aspects of data engineering, be it building modern data pipelines, cloud architecture, or data lake implementation. Atos Atos is a global AI-powered technology partner supporting enterprises throughout their digital transformation journey. From creating the strategy to designing and deploying the data architecture, the company takes care of everything. It is also one of the well-known data engineering companies offering end-to-end services for clients to manage their data and systems in critical environments. The company unlocks insights, accelerates AI adoption, and encourages innovation for businesses to be more agile, scalable, and successful. Atos supports organizations to focus on their long-term growth by setting up robust cloud-based data systems. It specializes in deploying technologies in multi-cloud environments and helps clients make the most of

2026 Data Pipeline Automation Strategy for the Modern C-Suite

Data pipeline automation saves time, reduces workload, and accelerates processes to provide employees with real-time insights. Here, we’ll discuss the need for a reliable data pipeline automation strategy for a business to streamline its processes and achieve the desired goals in 2026. Data continues to play a prominent role in helping enterprises make informed and smart decisions to boost their business. Every organization generates a tremendous amount of data directly and indirectly. Additionally, you have to collect data from external sources like social media, third-party websites, etc., to understand customer behavior, market trends, and more. All this can be achieved with a comprehensive data pipeline automation strategy. A data pipeline is vital for every data architecture, and automating it allows a business to get quality and relevant insights in real time. According to Grand View Research, the global data pipeline tools market is estimated to reach $48,331.7 million by 2030 at a CAGR (compound annual growth rate) of 26%. A report by Gartner (Market Guide for DataOps Tools) indicates that businesses can achieve 10x productivity improvements by investing in DataOps solutions by 2026 to automate testing, continuous integration, etc., to eliminate bottlenecks and streamline the processes. The pipeline automation strategy is a blueprint that empowers you to design, build, maintain, and optimize the data architecture using the latest tools and technologies like artificial intelligence and machine learning. AI-driven data pipelines are built to increase the efficiency and accuracy of the processes, which enhances decision-making abilities across your enterprise. In this blog, we will read about the strategy and best practices for end-to-end data pipeline automation in 2026. Data Pipeline Automation Strategy for Businesses to Follow in 2026 The pipeline automation strategy is a blueprint for you to revamp your existing systems and make them more powerful, efficient, scalable, and fast. That’s because manual data pipeline management is time-consuming, effort-intensive, and expensive. Businesses cannot afford to waste time or resources in today’s fast-paced world and need to gain a competitive edge over others in the global market. However, it is necessary to have a proper plan and vision before initiating any changes. Consider the following data pipeline orchestration best practices and steps to include in your pipeline automation strategy. Define Scope and Requirements The first step for any project is to know what it is about, what is required, and how it works. You should define the scope of the data pipeline automation project even before you create the strategy, as it helps in gaining a clear understanding of the process and the requirements. This also includes communication between teams, departments, and decision makers, as well as identifying the data sources and selecting the target systems to include in the data pipeline. Factors like budget, timeline, expertise, talent gap, etc., should also be considered. Consult a Service Provider The next step is to hire data pipeline development services from reputable companies. This helps in bridging the talent gap in the organization quickly and cost-effectively. Instead of spending time and resources on a lengthy recruitment process, you can benefit from partnering with third-party and offshore service providers. Make sure the company has the required certifications, industry experience, and a suitable project portfolio. Moreover, such companies offer flexible working models to align their offerings with your requirements. This makes it easier to manage the project and achieve the desired results. Select the Data Pipeline Automation Tool Simply put, automated data pipeline tools are powerful and advanced AI-powered software applications that automate various stages of the data pipeline, such as data collection from multiple sources, data transformation (ETL/ ELT), data storage, etc. The tools reduce the workload on employees by taking over the processes and eliminating the need for human effort. The data pipeline automation tools are broadly classified into different categories, such as real-time data pipeline tools, batch processing tools, on-premises tools, cloud-based tools, open-source tools, no-code platforms, etc. The service provider will recommend the best choice based on your specifications, budget, and objectives. Invest in End-to-End Data Engineering Data engineering is a series of processes and solutions required to set up and run the data architecture in your enterprise. By developing a data engineering automation framework, you can ensure that the entire system is scalable, flexible, agile, and effective in handling large volumes of data and workload seamlessly. Once the framework is developed and implemented, your employees can access the central repository, insights, and reports directly from the dashboards customized for their jobs. Design and Build the Data Pipeline Typically, the automated modern data pipeline includes data ingestion, data processing, data storage (it can be a data warehouse, data lake, data lakehouse, or a combination of these), data workflow orchestration, data visualization, and data analysis. Building an automated data pipeline requires a combination of domain expertise and a team of diverse talent. Most service providers recommend cloud-based infrastructure for greater scalability and flexibility, though you can also use hybrid (cloud and on-premises) solutions. Test and Customize the Data Pipeline Once the data pipeline has been built, it should be tested to identify and eliminate bugs. This is done in different ways, such as end-to-end testing, data quality testing to ensure data maturity, unit and contract testing, etc. More than one type of test is run in different environments. Furthermore, the data pipeline’s key components have to be customized to align with your business needs. This is done to derive meaningful and relevant insights from the data and to share the reports with decision-makers in real-time. Integrate and Optimize the Data Pipeline The data pipeline and the architecture have to be integrated with other systems, applications, and tools used in your business. This streamlines data flow between various platforms and provides access to real-time data and insights. The data analytics services can also be included in this framework so that each part of the data pipeline is automated and optimized to work efficiently. Optimization is the process of eliminating redundancies, lags, excessive loads, etc., for the systems to consume fewer resources but deliver

Decision Intelligence Platforms: The Ultimate MS Excel Alternative

While MS Excel has its advantages, it is no longer enough for businesses to effectively manage their data and insights. Here, we’ll discuss the benefits of adopting decision intelligence platforms to make informed and smart decisions and gain a competitive edge. For years, Microsoft Excel has been the go-to choice for creating databases and generating reports, graphs, tables, etc. The introduction of business intelligence platforms like Power BI has upped the game to allow organizations to convert Excel sheets into powerful databases. Then, decision intelligence has furthered the development to unlock the full potential of business data to derive meaningful insights and make data-driven decisions. According to Fortune Business Insights, the global decision intelligence market was valued at $16.79 billion in 2024 and expected to reach $19.38 billion in 2025, with a projected growth rate (CAGR) of 16.9% to reach $57.75 billion by 2032. The statistics clearly indicate that businesses are shifting from traditional Excel sheets to powerful decision intelligence platforms, providing valuable insights and analytical reports to inform proactive business decisions. In this blog, we’ll read more about decision intelligence and the importance of revamping the infrastructure to support business intelligence system adoption, and advanced analytics. What is Decision Intelligence? Decision intelligence uses machine learning and automation to provide faster and more reliable insights and enable data-driven decision-making in enterprises. Typically, it combines data analytics, artificial intelligence, and data science to provide a holistic view of the situation and give contextual information required to make the necessary decisions. Decision intelligence platforms are not limited to historical data, but also support advanced analytics like predictive and prescriptive analytical insights to prepare for the future and make proactive decisions. Additionally, it is a part of modern data engineering and can optimize the outcomes to improve all quality, efficiency, and performance. This brings greater consistency to the process and allows you to benchmark higher standards and adhere to the compliance regulations. MS Excel vs Decision Intelligence Microsoft Excel is a spreadsheet tool for basic data analysis and reporting. It works best for small and medium-sized datasets and reports that don’t require complicated analytics. Though Excel can be integrated with AI tools, on its own, the functionalities can be limited, especially for large enterprises that require powerful insights. Excel’s limitations in analysis make it less effective for day-to-day decision-making, also because it requires manual effort. On the other hand, decision intelligence platforms are known for their analytical and automation capabilities. Furthermore, they support descriptive, predictive, diagnostic, and prescriptive analytics in real-time. Transparency, data-centric systems, explainability, flexibility, scalability, and continuous improvement are the key principles of decision intelligence. It is a must-have solution to implement modern data analysis in your organization and benefit from data-driven models to gain a competitive edge in global markets. Decision Intelligence Platforms Benefits Working with massive amounts of business data needs powerful systems that can seamlessly handle complex requirements and share actionable insights in real-time. Decision intelligence platforms are a perfect solution as they offer the following benefits: Seamless Data Integration There’s no need to struggle with collecting and storing data from multiple sources. The DI platforms can be integrated with several data sources to automate data collection and streamline data flow within the systems. This eliminates the need for human intervention and saves time and resources spent on managing the datasets. A centralized data repository is created to store, clean, and convert raw data into actionable insights. Democratized Data Access Data collection and storage are only a part of the process. This data and insights have to be accessible to all decision-makers across the enterprise. That requires providing authorized and restricted access to employees based on their job descriptions and roles. It also reduces the load on the technical team since employees can directly access the required data and reports through personalized dashboards. Additionally, your employees will have more control over the situation. Faster and More Accurate Insights Traditional analytics are prone to biases and blind spots, which are inherent to legacy systems. Additionally, decision-makers may also make biased interpretations and decisions, which can impact the business in various ways. Such risks can be minimized by implementing modern data analytics solutions and decision intelligence platforms that provide a unified and holistic view of the situation. DI eliminates the risk of inaccurate analysis made from low-quality data. Thus, your decisions will be more aligned with your objectives. Uncovering Hidden Patterns When you work with large amounts of data, it is not easy to identify hidden patterns, trends, connections, and correlations between data points. Decision intelligence uses advanced technologies like AI, ML, etc., which can see what humans cannot immediately detect when processing massive datasets. This allows you to get deeper and richer insights about the market, customers, competitors, products/ services, and much more. You can identify the root cause of problems and come up with comprehensive solutions to resolve them permanently. Maximizing ROI Return on investment is necessary for every business. How soon you can start to generate ROI indicates the efficiency of the solutions. In today’s fast-paced world, businesses have to get quick results and returns to generate profit and mitigate risk. Decision intelligence can help with this by accelerating ROI and maximizing it. Instead of making decisions based on outdated and incomplete data, you use reliable and meaningful insights to power your decisions and actions, thus enhancing revenue and profits. Scalability and Adaptability Decision intelligence platforms empower you to future-proof your infrastructure by offering scalability, flexibility, and adaptability. There’s no need to replace the systems with new ones. Instead, they are periodically upgraded to handle your growing needs and support the increased volume of transactions. Furthermore, this is achieved while increasing the quality and efficiency of the systems through automation. NLP (natural language processing) ensures that the DI platforms provide contextual insights with greater accuracy. Demand Forecasting Predictive analytics helps with sales and demand forecasting, which allows you to be prepared for future market changes and opportunities. Decision intelligence empowers different teams to collaborate and come up with

Azure Data Engineering: The CTO’s Key to Hybrid and Multi-Cloud Wins

Cloud is the future for every business as it streamlines internal processes and optimizes resources while ensuring control and quality. Here, we’ll discuss the hybrid and multi-cloud strategies technical or IT executives should adopt to implement Azure data engineering in their enterprises. A growing business requires a robust IT infrastructure that is flexible, scalable, and secure. While on-premises solutions are effective, cloud-based software, platforms, and infrastructure can offer more diverse results and better performance. That said, it is vital to choose the right cloud strategy for your business. That’s why many organizations are shifting to hybrid cloud or multi-cloud solutions, which enable them to strengthen their IT infrastructure and leverage advanced technology to unlock the full potential of data, making data-driven decisions in real-time. Statistics show that by 2025, 94% of businesses will utilize a cloud service in some form, and the global cloud market is projected to reach $732 billion by the end of the year. AWS and Azure are the top two cloud providers with a combined market share of over 50%. From startups and small businesses to large enterprises and multinational organizations, every business can benefit by following the cloud-first approach. However, this requires clear objectives and a comprehensive strategy, which is why most organizations partner with third-party offshore service providers to streamline cloud-based data engineering and maintain the systems in the long term. In this blog, we’ll first look at what hybrid and multi-cloud platforms are and discuss the strategies executives should adopt to implement Azure data engineering in their businesses. Hybrid and Multi-Cloud Platforms A hybrid cloud setup is a combination of cloud and on-premises infrastructure. The on-premises infrastructure is private and likely hosts the legacy systems belonging to the business. The cloud is a public service, offering SaaS (Software as a Service) or PaaS (Platform as a Service) solutions, which are integrated with the software/ systems on-premises. This combination ensures that important data is safeguarded internally while eliminating extra overhead costs by relying on public cloud platforms. A hybrid cloud strategy for Azure helps revamp the existing combination to automate recurring tasks, increase data security, and optimize resources for greater ROI. A multi-cloud setup is a combination of two or more cloud platforms belonging to different vendors. For example, a business may run its IT infrastructure on AWS, Azure, and Google Cloud, with the systems distributed across all three and connected through third-party integrations. This also helps in cost optimization and prevents vendor lock-in. Multi-cloud data integration also allows you to select the exact tools and technologies required for your operations and increase overall efficiency. Azure data engineering solutions can be implemented on hybrid and multi-cloud systems, based on your specifications, objectives, and budget. The right strategies maximize flexibility, scalability, performance, and cost-effectiveness. However, this shift also comes with certain challenges, which can be overcome with expert support and guidance from experienced data engineering consulting companies. When you hire a certified Microsoft partner, you have access to their expertise, new perspectives, and the extensive ecosystems offered by the cloud provider. Hybrid & Multi-Cloud Strategies for Azure Data Engineering Clearly Define the Goals and Objectives The most important part of any strategy is to know why you want to make the changes or adopt something new. If you want to set up multi-cloud or hybrid cloud AI analytics, first define your goals and objectives. What do you wish to achieve by migrating the IT infrastructure or certain processes to a cloud platform? How will it affect different departments, teams, etc., across the business? What kind of data security measures do you already have, and what additions should be made? By when do you want to see the results of these changes? What ROI do you expect? Top-level executives should also involve middle managers, team leaders, etc., in the discussions to get a realistic picture and understand the practical limitations. Select Service Providers and Vendors With many cloud vendors and several service providers to offer end-to-end or consulting solutions, you should take time to select the right partners for your business. For example, the extent of vendor flexibility for cloud services determines whether you can rely on the cloud platform in the long term and if the costs will align with your budget. When selecting a service provider, factors such as experience, industry expertise, certification, project portfolio, pricing, transparency, communication, agreement terms, etc., should be considered. For example, you can hire a data warehousing company with certified partnerships with leading cloud providers like Azure, AWS, Google Cloud, as well as with data security certifications. Establish KPIs for Hybrid and Multi-Cloud Adoption It is equally important to determine the metrics for measuring the progress and effectiveness of hybrid and multi-cloud systems. Set three or four KPIs and make them time-bound. This helps in defining success and also highlights areas for change or improvement. Additionally, the KPIs, business objectives, and services should be aligned to get the desired results. For example, if optimizing costs is a top objective, the KPI could be measuring infrastructure utilization. A clear increase in the latter would indicate that the objective is being achieved. Build a Unified Data Architecture Azure data engineering gives the best results when there is a unified data architecture with a central repository with the entire business data in a single location. This eliminates data silos and outdated datasets scattered in the enterprise and allows all departments to access the latest and updated information from a centralized location. Data estate unification also simplifies security and governance measures by making it easier to provide authorized access to employees based on their job requirements, titles, etc. Leverage Cloud-Native Tools Cloud vendors, especially the top ones like Azure, offer several cloud-native tools, features, apps, frameworks, APIs, etc., to integrate with the data architecture and build robust systems to derive data-driven insights in real-time. The tools help with automation, simplification, optimization, monitoring, scaling, and much more. For example, cloud modernization with Fabric is easier when you work with the tools available in

Data Governance in Self-Service BI: Managing Risks Without Data Gatekeepers

Self-service BI is more efficient and reliable when you have a robust data governance framework to streamline and standardize the process. Here, we’ll discuss how data governance in self-service BI helps with risk management. Business intelligence is a collection of processes that convert raw data into actionable insights. A traditional BI setup is highly technical and requires data analysts, data scientists, statistical analysts, and BI experts with relevant skills and knowledge. This team manages the processes and shares the insights with other employees to help them make data-driven decisions. However, there’s a branch of business intelligence that has simplified the process for non-technical employees and end users. This is known as self-service BI. According to The Business Research Company, the self-service BI market was $10.02 billion in 2024 and is expected to grow at a CAGR (Compound Annual Growth Rate) of 17.3% to reach $22.42 billion by 2029. Self-service BI tools enable users to sort, analyze, derive insights, and generate data visualizations without requiring extensive technical expertise. Be it frontline employees or executives, they don’t have to contact the tech team with queries and wait for the insights/ reports to be sent. With self-service BI, they can perform the activity on their own and make data-driven decisions. While this made self-service BI popular across industries, it also led to certain challenges and issues, especially with data management and governance. That’s because self-service BI also requires BI consultants to work on the backend and ensure that the data quality is as it should be to derive accurate insights. In this blog, we explore the challenges of self-service BI and how data governance plays a crucial role in managing risks when data gatekeepers step back. Challenges without Data Governance in Self-Service BI The major challenges of using self-service BI deal with data. While most businesses know the importance of data in deriving insights, not many have a clear picture of how to handle data or ways to ensure its quality, compliance, etc. This results in a mismatch of expectations and outcomes. It turns self-service BI into a frustrating tool, resulting in employees sending emails to the BI with their queries and requests. Data Inconsistency and Trust Issues It’s no surprise that a business has vast amounts of data to deal with. Transactional data, data from social media and websites, data brought by stakeholders, customer data, etc., are all important and should be used for analytics. However, this raw data has duplicates, incomplete information, and other errors. Ensuring data consistency is a big challenge as low-quality data can result in incorrect insights. Complexity Instead of Simplification The market has several BI tools with extensive features and capabilities. Vendors promise flexibility, interactive features, and access to numerous data visualizations. While these sound great in theory, the practical application can be confusing and overwhelming. Which visualization should an employee use for which report? What happens if the wrong type of graph or chart is created? BI risk management is also about ensuring that the customized dashboards don’t complicate things when they should be simplifying the process. Report Sprawl Interactive dashboards are easy to use. Hence, employees can generate reports with a couple of clicks. Over time, this results in too many reports created by employees from across the organization. Quality, relevance, and accuracy can take a backseat without a proper understanding of why these reports are generated and how they are used. Repot sprawl leads to confusion and miscommunication, which can result in wrong decisions. Lack of Standardization Consistency in how your employees use self-service BI tools is vital for a business to be efficient and achieve its goals. This requires standardization of processes – the data used for insights, the types of reports generated, the validation process, when to use data-driven analytics, etc. This is more of a strategic plan than a series of operations or actions. A business cannot afford for each employee to follow a different standard or process when making data-driven decisions. Absence of Governance Data governance has to be a priority, but some businesses ignore it. When you don’t manage data and the analytics process with a proper framework, it can complicate the operations, lead to unverified reports, and may even attract lawsuits from outsiders or stakeholders due to various reasons. Data governance is not optional. It is mandatory even for self-service BI. That’s why many enterprises hire business intelligence consulting services to add a robust governance layer to their data-driven models. What is Data Governance? We mentioned data governance a few times. What does it actually mean? Data governance is a collection of principles, practices, and tools that help manage the data assets of a business throughout the lifecycle. Aligning data requirements with business vision, mission, objectives, and strategy is important for seamless data management. It also includes data security and data compliance, where the data used for analytics is safe from unauthorized access and adheres to the global data privacy regulations, like GDPR, CCPA, etc. The data governance framework empowers you to leverage your data assets to unlock their true potential and derive meaningful and accurate insights for proactive decision-making. From optimizing resources to reducing costs, increasing efficiency, and standardizing processes, data governance plays a crucial role in protecting your organization’s data and reputation. How Data Governance Helps Manage Risks in Self-Service BI Data governance is the solution to managing risks and challenges of using self-service BI tools in your business. Third-party and offshore BI consultants can help implement data governance practices. Clear and Measurable Goals The easiest way to complicate things is to be vague and directionless. You need clear and measurable goals when implementing business intelligence in your organization. The same applies to building the data governance framework. In fact, your goals and strategies should be aligned at all times to get the expected results. Be specific about the outcomes you expect, such as reducing the request rate by a certain percentage, increasing meaningful dashboard activity by X times, and so on. Make data compliance

Top 12 Azure Data Engineering Companies Driving Innovation in Finance

Data engineering is an end-to-end solution to manage and analyze large datasets and derive meaningful insights. Here, we’ll discuss the top Azure data engineering companies in the finance industry and why to hire their services to streamline your business operations. Data engineering is a complex process involving the designing, building, and managing of IT infrastructure to effectively collect, store, transform, and analyze data to derive meaningful insights. It deals with converting raw data into actionable insights using various tools and technologies. According to Mordor Intelligence, the big data engineering services market is expected to be $91.54 billion in 2025 and predicted to grow at a CAGR (compound annual growth rate) of 15.38% to reach $187.19 billion by 2030. Data engineering can also be offered as a cloud-based service through platforms such as Microsoft Azure, Google Cloud, AWS, etc. Another report says that data engineering as a service is predicted to reach $13.2 billion by 2026. Every industry benefits from data engineering services. Hence, it is no surprise that Azure data engineering in finance has gained prominence in recent times. The finance industry is actively adopting new technologies to streamline its operations, reduce risk, prevent fraud, and enhance customer satisfaction. Financial institutions and banks can achieve their goals by investing in Azure for financial services solutions. Many data engineering companies offer customized solutions to help businesses implement Azure data engineering systems and benefit from real-time data-driven decision-making. In this blog, we’ll look at the top companies offering Azure data engineering in finance. Top Companies Offering Azure Data Engineering in Finance DataToBiz DataToBiz is among the leading Azure consulting companies in the global market. As a certified Microsoft Gold Partner, the company’s finance cloud solutions are tailored to suit the diverse requirements of each client in the industry. Be it seamless cloud integration or enterprise-grade security, the company has certified experts who can deliver the promised results and transform your data architecture into a robust, scalable, and flexible system. It works with startups as well as large enterprises and multinational firms to automate reporting, risk checks, and fraud detection, scale analytics (including predictive analytics), share real-time reports through the interactive, customized dashboards, and replace legacy systems with highly secure and compliant Azure architecture. DataToBiz is an award-winning company that follows a structured approach to providing data engineering services and prioritizes data security, thanks to its ISO and SOC certifications. Dataforest Dataforest is a product and data engineering company offering customized services to clients from different parts of the globe. The company’s financial data engineering services help businesses use technology innovatively to automate processes, improve the delivery of financial services, and generate real-time reports. From financial data migration to integration, big data analytics, internal process automation, predictive analytics, fraud and anomaly detection, and performance management, the company takes care of everything on the client’s behalf. It combines AI and other advanced technologies to build a reliable and scalable infrastructure in the organization. Dataforest offers Azure data engineering in finance, with end-to-end data pipeline management for ETL automation, and to enhance the decision-making speed in the business. Innowise Innowise is an IT consulting company offering innovative digital solutions to clients to help them achieve their objectives. It creates high-quality infrastructure to optimize data flow in the organization. The company’s services for Azure data engineering in finance include building data architecture, implementing data lakes and data warehouses, migrating data to the cloud and managing finance cloud data, compliance, DataOps, data analytics, and visualization. The company is a Microsoft solutions partner with experienced data engineers who can work with complex systems and streamline the internal processes to increase productivity without compromising quality or increasing overhead costs. Innowise empowers banks with its banking data engineering, software development, and other digital banking solutions to increase customer retention and generate greater revenue. Addepto Addepto is an AI and data consultancy company that helps businesses convert raw data into actionable insights by creating solutions that align with their business needs. The company’s services for data engineering in the banking and finance industry involve various processes, such as data ingestion, data collection, storage, and analysis. The team develops advanced data pipelines and integrates customized data platforms into the data architecture. The company leverages cutting-edge technologies to make data accessible and actionable to empower financial institutions and other organizations to make data-driven decisions proactively. Addepto’s Azure data engineering in finance services ensures modern data solutions for businesses to overcome various data-related challenges and benefit from cloud technologies. It assists clients in improving customer service and reducing the risk of fraudulent transactions. Prioxis Prioxis is a software development and digital transformation company offering Azure data engineering in finance services to clients from different regions. Its data engineering solutions are specifically designed to simplify complex systems and promote growth and innovation in the clients’ businesses. The company works with startups and trusted global brands to provide effective solutions by leveraging the latest technologies. It also focuses on future-proofing the systems to increase the value of business data and enhance operational efficiency. Prioxis has a team of experienced and certified developers with diverse domain experience. They assist organizations in strengthening their data strategies and building a robust data architecture for real-time business intelligence reports to make smarter decisions. The company’s services are tailored to align with each business’s requirements and long-term objectives. DAMCO DAMCO is a trusted technology partner for businesses from around the world to turn their data and IT systems into invaluable assets. The company works with AWS and Azure for financial services for data management and compliance. From creating a data strategy and the enterprise data roadmap to setting up data processing solutions, such as advanced data pipelines, ETL and ELT services, etc., building and customizing data storage systems on cloud platforms, and implementing the data governance framework for businesses to transform raw data into meaningful insights. DAMCO helps organizations build resilient and data pipelines for greater accessibility, security, and automated reporting. It provides tailored solutions for Azure data engineering in

7 Innovative Firms Aiding Data Pipeline Automation in 2026

Data pipelines are a vital part of the data architecture and simplify the collection and transformation of data to derive meaningful insights. Here, we’ll discuss the leading innovative firms aiding businesses with data pipeline automation in 2026. Data is a crucial part of any organization in today’s world. Every business, whether big or small, utilizes data in various ways, including analytics, business intelligence, and more. This allows data-driven decisions at different levels and for diverse requirements or purposes. For this to be possible, you should first streamline and manage your data systems. This requires a robust data pipeline. In today’s world, data pipeline development, deployment, and maintenance can be automated using advanced and powerful solutions. It enables organizations to optimize the process and reduce expenses without compromising quality. Statistics show that the data pipeline tools market is expected to be $14.76 billion in 2025 and projected to reach $48.33 billion by 2030. A data pipeline is a series of processes dealing with data collection, processing, and delivery. The tools collect data from multiple sources (internal and external), process it (clean, format, structure, store, etc.), and deliver the data to the required destinations (third-party software, unified interfaces, etc.). This is done to create proper data flow within the organization. However, when this is done manually, it can be time-consuming, effort-intensive, and expensive. It can also lead to human error and delays. Data pipeline automation simplifies and streamlines processes to eliminate risks and increase efficiency. In this blog, we’ll read more about automated data pipelines and the top companies providing tailored data pipeline automation services. What is Data Pipeline Automation? Data pipeline automation is where the entire process of data extraction, transformation, and loading (ETL) is automated using advanced tools to ensure consistent and reliable results. This data is then sent to analytical or business intelligence tools to derive insights, which are shared with end users through data visualization dashboards. The automated data pipelines can be simple or complex, depending on your existing systems and long-term requirements. Data pipeline automation is a part of data engineering services, which are a collection of various processes, tools, databases, and technologies. You can opt for end-to-end data engineering or hire service providers only to automate the data pipelines. Either way, the solutions are tailored to align with your specifications and focus on delivering long-term results. Automating data pipelines also helps future-proof the architecture and ensure its scalability. Recurring tasks are automated to reduce the risk of human errors, eliminate unexpected delays, and increase consistency in results. It also accelerates the entire process of handling large datasets in less time. This is particularly helpful since most businesses have a lot of data to work with, and more is added to the database every day. Top Firms Offering Data Pipeline Automation DataToBiz DataToBiz is a leading data engineering consulting company with a global client base. The award-winning company is a Microsoft Gold Partner and offers tailored and automated end-to-end data pipeline development services to manage large datasets effectively and derive reliable insights in real-time. It works with MSMEs and large enterprises alike to provide customized solutions for optimizing data flow, streamlining data storage and transformation, workflow automation, and much more. As an ISO-certified service provider, DataToBiz ensures that its solutions comply with global data privacy and data security regulations. Clients have achieved 80% faster reporting times, a 30% boost in utilization hours, and 95% improvement in data accuracy. The company offers a free data pipeline audit for businesses to understand their current systems accurately and create a robust strategic plan for their data pipeline project. Rain Infotech Rain Infotech is an AI and Blockchain company that helps clients implement innovative AI solutions across their organizations. Its data pipeline automation services are custom-designed to move, transform, and process data at scale and share meaningful insights with the end users. With multi-source data integration, ELT/ ETL automation tools, cloud-native deployment, different processing methods, and governance support, the company knows how to ensure high-quality end-to-end data pipeline automation. Rain Infotech follows a several-stage approach to build, deploy, and implement the new and innovative automated data pipeline in the client’s enterprise. With its services, clients have achieved up to 91% reduction in data prep time and 80% fewer errors. Growth Acceleration Partners Growth Acceleration Partners, also called GAP, is a consulting and technology company that acts as an engine for business growth. Its AI-powered solutions are aimed at streamlining data engineering, modernizing data systems, and providing reliable analytics solutions. The company’s automated data pipelines can be scaled to meet the client’s requirements while being flexible and agile to handle the workload. From data quality assurance to data security and compliance, third-party integrations, resource management, and more, the company takes responsibility for every part of the process. GAP follows a five-step approach to implement automated data pipelines in businesses and monitor them to prevent errors. The company helps clients turn their ideas into profitable tech solutions. DATAFOREST DATAFOREST is a product and data engineering company that applies data science techniques to improve data management and optimize the IT infrastructure. It has won many awards and is a certified partner of AWS and Databricks. Businesses that want a scalable data infrastructure, enterprise pipeline architecture, and data transformation automation can achieve their goals by partnering with the company. From identifying and validating data sources to running data quality checks, monitoring the systems, and ensuring data pipeline reliability, the company follows a tried and tested process for data pipeline automation. DATAFOREST helps clients overcome various data-related challenges to increase data quality and scale the architecture seamlessly. Soft Kraft Soft Kraft is a software development company that follows the business-first approach to help clients achieve their goals by offering various technological services. It builds automated modern data pipelines, offers data compliance and governance services, and implements cloud data warehousing solutions. As an ISO-certified company, it also optimizes the processes to reduce costs for the business. Clients can partner with the company for managed services or

Top 11 Data Engineering Consulting Project KPIs You Need to Track

“Data engineering does not have an end state, but it’s a continual process of collecting, storing, processing, and analyzing data.”- Heather Miller. A data engineering consulting project usually involves refining and adapting analytics patterns regularly. From ensuring data freshness and uptime to managing quality and cost, you need to measure various aspects of data systems. When done right, data engineering helps you derive meaningful insights and make faster decisions. “Data is the new oil. It’s valuable, but if unrefined, it cannot really be used. It has to be changed into gas, plastic, chemicals, etc., to create a valuable entity that drives profitable activity; so, data must be broken down and analyzed for it to have value.” – Clive Humby. Today, having data isn’t enough. You need to clean, organize, and make sure people can use it to derive business value. According to Gartner, organizations lose an average of $12.9 million every year due to bad data. “Data quality is directly linked to the quality of decision-making,” says Melody Chien, Senior Director Analyst at Gartner. “It leads to better leads, better customer understanding, and stronger relationships. In short, it’s a competitive advantage.” It’s not just about moving data, it’s about making data work. That’s why measuring and improving the performance of your data systems is important. Data engineering KPIs help you track system health, data quality, and their business impact in real time. Top KPIs You Must Track for Data Engineering Consulting Projects If you notice one or two issues, a quick fix can help. However, as more issues arise, plan a comprehensive review to determine how each issue affects report accuracy and decision-making. In a data engineering consulting project, you not only deliver pipelines but also scalable, cost-efficient systems that work in production. These 11 KPIs help you measure performance, spot issues early, and build trust with clients and stakeholders. Data Pipeline Latency The time it takes for data to move from its source to the destination (e.g., a warehouse, dashboard, or API) is known as data pipeline latency. To calculate data pipeline latency, use the following formula: Latency = Timestamp (Data Available) – Timestamp (Data Generated). Data pipeline latency makes it easy to determine how fresh your data is for reporting or ML use cases. You can use it for streaming data products in real-time. If latency is high, it indicates that your reports are stale and have bottlenecks, making this an important consideration for teams supporting SLAs tied to data freshness. System Uptime System uptime refers to the percentage of time when your data platform (pipelines, APIs, jobs) is operational and accessible to users. To calculate system uptime, use the following formula: (Actual Uptime / Total Scheduled Time) × 100 frequent downtime impacts business insights and SLA compliance. Since clients expect business continuity, it is important to monitor availability across pipeline schedulers, data APIs, and storage systems to ensure reliability and build client trust. Data Quality Score Data quality metrics measure how clean, complete, and reliable your data is. It includes components such as the percentage of missing or null values, duplicated rows, schema mismatches, and validation rule failures. “Data matures like wine, applications like fish.” – James Governor A high data quality score means the data is clean, accurate, and reliable. This leads to fewer complaints from analysts, fewer bugs in apps that use the data, and a better reputation for your platform. In data engineering consulting projects, this metric proves that your team has done a great job. Error Rate Error rate tracks the percentage of pipeline runs or batch jobs that fail due to issues like schema drift, connection timeouts, or missing dependencies. A high error rate is a red flag and signals bad architecture or insufficient testing. The lower the error rate, the less time your team spends firefighting, and the more time it spends delivering. A high error rate is a warning sign, and it indicates the system isn’t built properly or wasn’t tested enough. A low error rate means your pipelines run smoothly, your team spends less time fixing issues and more time building and improving things. Data Ingestion Rate Data ingestion rate measures how quickly you can pull in raw data to your platform from APIs, databases, logs, or external files. This metric is important for evaluating whether your system can handle increasing data loads. A good ingestion rate ensures that batch jobs start on time and that data isn’t delayed by bottlenecks during extraction or transport layers. If this rate drops, it indicates issues in the upstream system or ingestion pipelines. Processing Throughput Processing throughput refers to the volume of data your system can transform per unit of time. It indicates how fast and efficient your pipelines are, whether it’s dbt jobs, Spark tasks, or SQL-based ETL. If throughput is low, it can lead to delays, missed deadlines, or wasted compute resources. These data engineering KPIs help teams meet daily SLAs and cut down on cloud costs by avoiding over-provisioned infrastructure. It also makes it easy to test how well new architectures perform under load. Cost per Terabyte/Job This metric shows the average cost taken to process one terabyte of data or to run a single pipeline job, depending on how your billing works. This KPI helps you understand how much it costs to process each part of your data. In cloud platforms such as Snowflake, Databricks, or BigQuery, where costs depend on usage, costs can add up quickly. Data engineering companies can use this metric to show clients that they’re aligning things on budget and using resources optimally. Change Failure Rate Change failure rate shows how often code or infrastructure changes cause problems after being deployed. It could be due to pipeline breaks, job failure, or release rollback. Data engineering consulting teams use the change failure rate to understand how stable your release process is. A high failure rate indicates that something is not working, such as missing tests or poor CI/CD pipelines. You need to pay attention