Data Engineering

Azure Data Engineering Support: What to Outsource vs Build In-House

Microsoft Azure is a comprehensive cloud solution that enables the building, deployment, and maintenance of data architectures to empower smart decision-making. Here, we’ll discuss what to outsource vs. build in-house for Azure data engineering support in a growing enterprise. Microsoft Azure is a robust cloud computing platform with a vast and powerful ecosystem, offering over 200 services to meet various requirements, including storage, computing, networking, analytics, artificial intelligence, security, and more. It empowers engineers to build, deploy, implement, and integrate large-scale infrastructure on the cloud. Azure is among the top three cloud providers with a revenue of over $75 billion in 2025. Businesses across industries, from startups to large enterprises, can benefit from investing in Azure data engineering to streamline and automate workflows and share real-time insights with C-suites and leaders for proactive decision-making. However, a question often arises about whether you should build an in-house team for Azure data engineering support or outsource the responsibility to a third-party offshore service provider. Many organizations in recent times are opting for Azure data engineering managed services from certified companies to have direct access to global talent and expertise and ensure priority support around the clock. That said, both methods have their advantages and disadvantages, and the choice depends on what is right for your enterprise. In this blog, we’ll find out the responsibilities of a data engineer and compare the pros and cons of outsourcing and building in-house teams for Azure data engineering support. What is Azure Data Engineering? Azure data engineering involves a series of processes, such as data mining, data pipeline development, data integration, data transformation, and data analytics. From collecting data from various sources to consolidating it in a central repository and supporting analytics, data engineering ensures raw data is transformed into actionable insights. These insights are shared as visualization reports with end users (C-level executives, employees, management, investors) for intelligent decision-making. The entire data architecture is hosted on Microsoft Azure cloud with tools and technologies belonging to the same ecosystem. For example, the workflows are created using Data Factory, while Synapse Analytics and Stream Analytics are used for different types of analysis, and the Data Lake Storage is where the vast datasets are stored. By hiring end-to-end Azure data engineering consulting, CTOs and CIOs can create a strategic plan to build a tailored data architecture that aligns with the business vision and objectives to support data-driven decision-making across all levels. The insights can be used by C-suites, department heads, VPs, team leaders, and other employees in the enterprises, whether in the same office or different branches worldwide. Responsibilities of an Azure Data Engineer A data engineer has certain roles and responsibilities, irrespective of whether they are an internal employee or a consulting and managed service provider. Data Management and Storage Azure data engineers have to handle data and systems in various forms. This involves selecting the data sources, setting up the connections, building the data storage, and ensuring seamless data flow. Continuous Azure data engineering support ensures that high-quality data is used to derive insights by building robust storage solutions. Data Pipeline Development and Integration Azure data pipeline development is another vital responsibility, as it is the key step where the collected data is cleaned, transformed, and loaded into the storage and integrated into the rest of the architecture. The data pipelines can be automated using AI-powered tools to keep the cycle moving continuously and enable real-time insights. Big Data and Analytics Data engineers are expected to handle vast datasets with new data being added at high velocity. This requires the use of Azure Databricks and Apache Spark for big data analytics. Data processing workflows are created and automated to support real-time analytics and visualization reports. For this, data engineers have to collaborate with data scientists and data analysts. Data Governance Data governance is a part of data management dealing with the security, quality, and availability of datasets. It is a framework of guidelines and regulations to ensure that the data architecture is aligned with global data laws and is built to support high-quality analytics. Authorized access is provided to make data easily available to decision-makers, but it is safe from cyber threats. What Azure Data Engineering Support Should Be In-House vs Outsourced Typically, it is recommended to outsource the entire project end-to-end and ask for knowledge transfer through training, mentoring, documentation, etc., as it is a cost-effective solution in the long run. That said, building an in-house team for Azure data platform support has certain benefits, as listed below. Pros and Cons of In-House Azure Data Engineering Support Pros: Full Control An in-house team has complete control over the data architecture, processes, and systems. This allows CTOs to make quick changes whenever required and to ‘own’ the infrastructure and make intelligent decisions in real-time. Knowledge and Alignment An in-house team has more knowledge about the enterprise, making it easy to align the processes with business values and objectives. CTOs, CIOs, and chief data officers can personally ensure proper alignment to derive contextually relevant insights. Better Data Compliance Measures When access to data is provided only to employees, management, C-suites, and stakeholders, it simplifies data compliance and reduces the risk of outsiders using confidential data for nefarious purposes. Stronger Data Security When there are fewer third-party providers and vendors between your enterprise and Azure data platform services, it reduces the risk of security breaches and weak points that can be targeted by cybercriminals. Long-Term Investment An in-house data engineering team is a long-term investment to build a knowledge base in the enterprise, which can be useful to train more employees. CEOs can be confident in their talent and can take calculated risks. Cons: Expensive Overheads and Lower ROI Hiring experienced data engineers is expensive, as there is high demand but not enough talent to meet it. Top management often has to pay premium salaries to retain skilled professionals while also investing heavily in building systems and processes from scratch. Scalability Issues Scaling an in-house team can

Data Localization in MENA Explained: What it means for Modern Data Platforms?

With various governments setting up data privacy laws, enterprises have to be innovative in ensuring compliance and efficiency. Here, we’ll discuss what data localization means for businesses in the MENA region and how it affects modern data platform implementation. Data is the core of all business operations in today’s world. From human resources to finance, marketing, production, and customer care, important decisions in all departments are based on the insights derived from relevant business data. The enterprises in the Middle East and Africa are no exception. They use data analytics to make data-driven decisions that align with the market trends and customer preferences. According to Grand View Research, the data analytics market in the Middle East & Africa (MENA) regions is expected to grow by a CAGR (compound annual growth rate) of 16.8% to reach $15,714.4 million by 2030. While analytical tools and technologies are one part of the process, the data and its management are another crucial aspect. Where is the data collected from? Where is it stored? How is the data accessed from cloud servers? Why is it important to own your business data and safeguard it? Such questions have become prominent in recent years, with data centers being located only in certain regions and businesses losing access to their own data due to various reasons. The Middle East has taken it seriously and has been working on its data protection laws to set up regulatory frameworks that prevent data misuse while supporting its local businesses. In this blog, let’s find out what data localization is and how it impacts the implementation of modern data platforms, especially in the MENA region, and how cloud engineering companies can help enterprises achieve this. What is Data Localization? Data localization is the practice of storing data in the same region where it has been generated. Simply put, the data generated in the Middle East (by its residents, businesses, etc.) is stored in the same region instead of being transferred to remote cloud servers in the US or the UK. For example, Saudi Arabia’s Personal Data Protection Law (PDPL) was enforced on 14th September 2024 to emphasize data localization and protect users’ rights. The law ensures that sensitive user data is not transferred across borders without explicit approval. The UAE has a federal data protection law, just like Jordan and Qatar. Kuwait and Oman are also introducing new data privacy frameworks to safeguard their data. Data Localization vs. Data Residency Though the terms are sometimes used interchangeably, data residency is not the same as data localization. Data residency is about where the data is stored, the place where data resides. In data localization, complying with the local data regulations and privacy laws is mandatory. Some countries have data residency laws that demand data localization and do not allow cross-border data transfer without certain restrictions or compliance. Violating these laws could lead to lawsuits and financial losses. Such data localization challenges can affect multinational organizations from using data freely across different regional offices. However, it also creates a space for more innovative solutions that balance data privacy with business growth. It builds trust in the market and ensures greater transparency. Furthermore, businesses don’t have to rely on cloud providers to access their own data stored globally. With localized data centers, organizations will have easy and immediate access to data as well as more control over their operations. Cloud vs. On-Premises Data Residency Data localization means that CEOs and CTOs must decide how to store data within the region. Should you build a scalable on-prem data center or use a sovereign cloud service? A sovereign cloud is an effective solution for organizations to ensure data localization while storing data on cloud-like servers. It can either be a part of your business’s data center (but acts like a cloud platform) or be hosted by the cloud provider to ensure data residency, privacy, and security laws are followed as per the local compliance requirements. In-country hosting and digital sovereignty are two major focal points of a sovereign cloud. Access restrictions, compliance, organizational control, support for internal operations, high-end encryption, and a dedicated network capacity make sovereign cloud the best choice for many organizations. CTOs and data leaders can partner with data engineering services providers to develop a tailored strategic plan for compliance with data localization while implementing a robust modern data platform to make intelligent decisions in real-time. Impact of Data Localization on Building a Modern Data Platform A modern data platform is essential in converting raw data into actionable insights to help with smart decision-making across the organization. However, the data localization labyrinth can create some hurdles that complicate the design of the data architecture and governance frameworks to align them with the laws and regulations of the region. However, it also offers many benefits to enhance your systems and build a better brand image. Greater Data Sovereignty With data localization laws, countries can have greater data sovereignty, where their citizens’ data is stored within the region instead of being controlled by foreign countries or organizations. Access to this data and its usage will also be governed by local laws, thus protecting people’s interests and privacy. Organizations, too, can ensure that their sensitive business data is subject to their country’s laws. It reduces the risk of cloud providers blocking businesses from accessing their data due to unclear reasons. This allows data teams to use their data for analytics without depending on several third-party vendors. Less Risk of Cyberattacks Statistics show that there has been an 18% increase in cyberattacks from Q1 2024 to Q1 2025. Additionally, the average cost of a data breach increased from $4.45 million to $4.92 million. In such instances, CEOs need to ensure that their IT infrastructure has robust security layers that cannot be breached easily. Furthermore, with localized data warehousing services for data storage and management, you can reduce surveillance by other countries and enterprises, as well as the threat of cyberattacks. Strengthening the business systems with multi-layered security solutions

From BI to AI: The Data Engineering Gaps Plant Managers Face in 2026

Artificial intelligence-based tools can be used to derive advanced analytical insights that enable smart manufacturing in various industries. Here, we’ll discuss the data engineering gaps manufacturers face when implementing AI technologies for business intelligence. The manufacturing industry has been embracing digital transformation and Industry 4.0 by adopting various advanced tools and technologies, including business intelligence, analytics, artificial intelligence, and digital twins. For instance, the AI market in the manufacturing sector is projected to reach $68.36 billion by 2032, growing at a compound annual growth rate (CAGR) of 33.5%. BI is just as popular, and the combination of AI with business intelligence to derive powerful insights in real-time is changing how manufacturers look at data and decision-making. Statistics show that the global business intelligence market is expected to be $54.9 billion by 2026. Over 78% of businesses worldwide have adopted at least one analytics or BI platform in 2025. Furthermore, AI-driven analytics take a 40% of the total BI investment in 2025. These numbers clearly indicate the accelerated adoption of analytics and business intelligence solutions across industries and regions. However, the challenge persists in the form of a data engineering gap, which affects efficiency, ROI, and overall performance. That’s because data is the core of the BI system, and mishandling of datasets can lead to various complications and inefficiencies. Implementing BI tools is just one part of the process. The actual work starts with streamlining data and building a robust data architecture, training employees to use the new systems, and optimizing the infrastructure. In this blog, we’ll read more about data engineering gaps plant managers face and how to overcome these to derive meaningful and accurate insights for smart decision-making. BI vs. AI Analytics Manufacturers generate a lot of data. However, what you do with this data matters more. That’s why many enterprises have been investing in manufacturing BI solutions to convert raw data into insights that can be shared through customized dashboards and help managers make swift decisions. Business intelligence is a suite of processes, tools, and technologies for collecting, transforming, analyzing, and visualizing data. Earlier, many of these processes were performed manually, which led to expensive systems, time-consuming recurring tasks, and outdated insights. If it takes a month to analyze a small dataset, imagine how much data would have been added during that time. Moreover, manual operations have a high risk of human error. The popularity of artificial intelligence resulted in the adoption of AI in manufacturing analytics to automate such recurring tasks, streamline data flow, and accelerate analysis. Instead of waiting for weeks or days, or even hours, plant managers can derive insights in a few minutes. Furthermore, with cloud-based AI solutions and edge computing, manufacturers can access analytical insights in real-time. In fact, you can also derive advanced analytics, like predictive analysis, to predict future outcomes. It is also used for predictive maintenance, which ensures that the equipment is in good health and contributes to a higher OEE (overall equipment effectiveness) score and less unplanned downtime. Upgrading BI to AI-powered analytics systems requires domain expertise, skills, and knowledge provided by artificial intelligence consulting companies. That’s why many manufacturers partner with AI third-party offshore service providers to revamp their existing setup and embrace digital transformation. 8 Data Engineering Gaps Plant Managers Face and How to Bridge Them Correctly identifying the data engineering gaps is a crucial part of an AI readiness assessment. This step ensures that C-level executives and plant managers are clearly aware of the strengths and weaknesses of the existing system and how the weaknesses can be overcome to build a comprehensive AI-powered data architecture to generate business intelligence reports in real-time. Here are some common data engineering gaps experienced by many enterprises, and ways to overcome them: Poor Data Quality and Fragmented Architecture Data architecture and data quality are top concerns as they determine the accuracy of the insights. Poor quality data leads to incorrect insights, which result in wrong decisions. For a manufacturing enterprise, it can be fatal in terms of money, brand name, and legal stance. Poor data quality can be because of fragmented data stored in silos rather than in a central repository. Plant managers can overcome the challenges and implement smart factory analytics by automating data collection and cleaning using AI and ML technologies. Data engineers can build automated data pipelines and set up a data warehouse or a data lake to ensure seamless workflow and meaningful insights. Complex Data Transfers and Storage Industrial data analytics is complex as you deal with massive datasets. Collecting, storing, and transferring such huge volumes of data can be complicated and lead to errors if the architecture does not support scalability. Delays, broken data, missing tags, duplicates, etc., become a concern, widening the data engineering gap between your existing systems and your objectives. This gap has to be carefully bridged, building a comprehensive cloud-based data architecture for manufacturing analytics that is flexible, scalable, and agile. It requires AI and ML-powered tools, data lakes and lakehouses, and other advanced solutions to streamline the workflow across the factory and throughout the enterprise. From plant managers to top management, all decision makers should have access to these insights. Data Pipeline Development Data pipelines automate data flow from multiple sources to the destination systems so that the collected data can be cleaned, transformed, and analyzed. This involves various processes, such as data ingestion, transformation, storage, etc., which are necessary when implementing a modern manufacturing data platform. Traditional data pipelines cannot handle the complexity of large data movement. AI-powered data pipeline automation is vital to share analytical insights in real-time to facilitate data-driven decision-making in the manufacturing units. By hiring Data Pipeline Consultants from experienced and certified companies, you can build automated data pipelines and seamlessly integrate them into your IT infrastructure. Data Security and Access Control The strategic plan for data engineering in manufacturing should also involve data security and access control management. This is where many enterprises struggle to find a balance. How do you ensure system and

Data Lakehouses vs. Data Warehouses vs. Data Lakes: A Decision Framework for C-suites

Data lakes and data lakehouses are storage solutions built to support real-time analytics and business intelligence for smart decision-making. Here, we’ll compare data lakehouses vs. data warehouses vs. data lakes to determine which framework is the best for C-suites. Can you run your organization without data? The answer is no. Data is the core of any establishment and can provide in-depth and hidden insights to make better and more effective decisions. With so much data being generated daily, businesses struggle to store and utilise it without exceeding their budget. Moreover, the traditional method of department silos is no longer useful in modern businesses. Manual processes are time-consuming, costly, and effort-intensive. With the right technology, you can not only store large datasets but also derive actionable insights in real-time. That’s why data warehouses, data lakes, and data lakehouses have become popular in recent times. According to Mordor Intelligence, the data lake market is expected to be $18.68 billion in 2025, with a CAGR (compound annual growth rate) of 22.62% to reach $51.78 billion by 2030. While North America is the largest, the Asia Pacific region is the fastest-growing in the global market. Microsoft, Amazon, Oracle, Teradata, and Capgemini are the major players, offering robust data lake solutions. Cloud-based data lakes occupied 65% of the market share in 2024, indicating the high demand for cloud services. But what exactly are they? As a CTO or a CEO, how do you choose between them? Does it matter what storage solution you implement in your business? What is the best choice for your organization to seamlessly manage data and analytics? What do data engineering services companies recommend? In this blog, we’ll find answers to these questions and more. Let’s first understand the similarities and differences between data lakehouses vs. data warehouses vs. data lakes. Data Lakehouses vs. Data Warehouses vs. Data Lakes Modern data storage solutions are robust and fast-evolving, designed to be scaled as required. However, they can be expensive if you don’t choose the right option based on your business requirements (current and future). Data Warehouse Data warehouses support structured datasets and deliver accurate analytical insights and business intelligence reports. They can be hosted on-premises and on cloud platforms with different security settings. However, data warehouses are not great at handling unstructured data or running AI-powered analytics on raw data. Data Lake A data lake is a storage center for all types of raw data—structured, unstructured, and semi-structured. It can support big data analytics and AI tools to derive insights from raw data. However, without proper layering and data architecture, a data lake can turn into a data swamp, which consumes too many resources and gives weak insights. It is preferably hosted on cloud platforms due to the extensive size and capacity demands. Data Lakehouse The data lakehouse definition clarifies that this is a combination of a data warehouse and a data lake intended to give better results for everyone. It is a cloud-based modern data architecture that blends the strengths of data warehouses and data lakes. It is scalable, flexible, reliable, and delivers great performance. From unlimited storage to accurate insights, data lakehouses sound like they are the best choice for every business. However, when choosing between data lakehouses vs. data warehouses vs. data lakes, you should consider various other factors and plan for the long term. Additionally, the strategy to implement data engineering services will also influence your decision. Hence, many organizations partner with service providers to audit existing systems and create a comprehensive blueprint to integrate, customize, and maintain the right data storage solution. Data Lake vs. Lakehouse Architecture: What Should the C-Suites Choose? Be it a startup, a growing business, or an established enterprise, every organization has to evolve periodically, revamp its internal operations, and adopt new technologies to survive in competitive markets. In today’s world, this deals with digital transformation and implementing data-driven models to make intelligent decisions in real-time. Naturally, IT directors, CTOs, and CEOs have to ensure that their teams have access to the right technological solutions to achieve business objectives and keep customers happy. For that, it is important to know the similarities and differences between data lakehouses vs. data warehouses vs. data lakes. Design and Architecture Data lakes support raw data in all formats and is hence structured to have a flat architecture. While this allows for great flexibility, it doesn’t really help in optimizing the query performance. Due to this, each query could consume a lot of resources and end up being an expensive project. A data lakehouse builds on a data lake by adding a metadata and governance layer before it is integrated with AI, BI, and analytical tools. The difference in Data lake vs. lakehouse architecture is this extra layer, which ensures the central repository is flexible but can also support query performance optimization to reduce resource consumption. Schema Schema is the process of structuring data for analytical insights. It determines how the data is read and processed to answer the query or provide an outcome. Data lakes use schema-on-read, in which the relevant data is structured only when it has to be analyzed. While this ensures a quick data ingestion, it increases the time taken to process each query. Data lake services help in optimizing this to an extent, but the issue can persist in large enterprises, resulting in increased expenses. A data lakehouse uses both schema-on-read and schema-on-write for flexibility as well as for structuring the data. By following a mixed approach, the processing time can be optimized, and the insights derived will also be of better quality. Data Type As we discussed earlier, data lake vs. warehouse and lakehouse work with different data types, making it crucial for organizations to understand their data before selecting a solution. Data lakes are highly flexible as they support unstructured, semi-structured, and structured data. That means videos, images, text, tables, charts, logs, audio, etc., can be stored in their existing formats. Lakehouses also offer this flexibility and enhance it by

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