data engineering services Archives

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

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

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

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

Manpreet Kaur
October 14, 2024
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Modern Data Engineering 101 – Benefits, Use Cases, Examples!

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

Yashica Chopra
April 26, 2023
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Data Engineering Services: A Modern Business Essential

Data engineering focuses on the practical applications required to establish a flexible, scalable, and agile data infrastructure in an enterprise. It is the most vital part of adopting the data-driven model to make business decisions. Here, we’ll discuss data engineering services and their role in modern businesses. Data is an integral part of the IT industry. Over the years, users have generated large volumes of data, which is being collected by businesses to fine-tune their products/ services and enhance customer experience. Statistics show that an average user generates 1.7 MB of data per second. Around 97.2% of businesses are investing in artificial intelligence and big data, though a study shows that many companies analyze only up to 40% of the data they collect from all sources. Another interesting observation is that companies that actively use big data analytics increase their profits by around 8%. Wouldn’t it be beneficial for businesses to further utilize data to amplify their profits? So, how can it be done? The answer is big data engineering services. In simple terms, data engineering is the process of streamlining data collection, storage, and analytics to get more insights from datasets. However, it is an elaborate process that requires IT infrastructure and expert skills. Data engineering is the foundation of building the data analytical model in an enterprise. Businesses partner with data engineering companies to adopt the data-driven model for effective and faster decision-making. Enterprises find it a cost-effective solution to rely on offshore data engineering service providers to derive actionable insights using AI and big data analytics. In this blog, we understand the process in detail and explain why data engineering is needed for every modern business around the world. What is Data Engineering? Data engineering is the process of collecting and validating data to ensure high-quality datasets are available for data scientists. Data engineering is a vast domain that includes a range of skills, tools, and applications. It is a combination of several modules like data infrastructure, data mining, data crunching, data acquisition, data modeling, and data management. A data engineer should maintain the data infrastructure that supports business intelligence solutions. They should work with programming languages, database software, machine learning, and artificial intelligence algorithms. They can work in small teams that focus only on ingesting data into systems or be a part of large teams offering data engineering services & solutions and collaborate with data scientists and database administrators to streamline the data pipeline in mid and large-sized enterprises. What are Data Engineering Services? Data engineering services are varied and versatile. The top data engineering services companies offer end-to-end solutions to design, build, deploy, and maintain a seamless system that collects, cleans, stores, processes, analyzes, and visualizes data through BI tools. The following are some important services offered by the companies: Data Ingestion Data ingestion is the process of moving or replicating data from sources to the cloud storage platform. It is a prominent step in the modern data stack. It determines the quality and type of data a business uses for analytics. Data engineers have to determine if this process will take place in batch mode or in real-time. Factors like cost and resource allocation play a vital role in finalizing the time frame for data ingestion. Data Storage Data storage management is another crucial part of data engineering services. The data collected from multiple internal and external sources has to be stored in a central database for further processing and analysis. Data engineers have to design the best data storage method that allows employees to access datasets in real-time. Data storage solutions can be on-premises or on the cloud. Businesses can even use a combination of both. Data warehousing and data lakes are two popular methods used to store vast amounts of data. Businesses offer Azure data engineering and AWS data engineering services to build and customize cloud data storage centers. Data Integration Data integration is a key data engineering service as it sets up the necessary connections between different systems, apps, and databases. It is the process of setting up the connection between the central database with the input and output channels. For example, the sources have to be connected with the data warehouse to collect data. Similarly, the data warehouse has to be connected with ERP systems and BI tools to run analytics and share data visualizations with the end user. Data Processing Data processing is the process where large datasets are cleaned and manipulated to derive useful information. Data from the data warehouse or data lakes are retrieved, classified, cleaned, and formatted to make it ready for analysis. This stage helps remove errors and duplicate data to increase the accuracy of the derived insights. It is yet another important part of data engineering services as low-quality data can result in incorrect insights which can lead to wrong business decisions. Business Intelligence Business intelligence is a vital part of the process. This is where data is converted into meaningful information and presented in graphical reports. Data engineers have the responsibility to identify the right BI tool based on business requirements and customize it accordingly. The dashboards also have to be set up and integrated with the rest of the infrastructure to provide data visualizations in real-time to employees across all departments. How Does Data Engineering Help a Business? Data engineering or information engineering is the foundation for adopting and using the data-driven model in an enterprise. Data engineering and analytics go hand in hand and have to be aligned at all times to ensure that the top management and employees can access actionable insights at any given point in time. This allows them to make faster decisions based on reliable reports rather than guesswork. Once data engineers set up the data architecture (systems and connections), data scientists can perform the analytics and share reports. Artificial intelligence tools and machine learning algorithms are used in the process to ensure the seamless and real-time flow of data from one system to another. Typically, data and engineering services help businesses in the following ways: Data engineering companies also offer data analytical

Shashank Dharasurkar
October 18, 2022
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Azure Data Engineering Services : Adapt to Changing Data Needs

AWS and Azure data engineering services are offered by top data engineering services companies to build, develop, deploy, and maintain a customized IT infrastructure on the cloud. Know more about them! Businesses today can collect enormous amounts of data. Analytics, traffic monitoring, and everything else depend on data. For handling such big data, businesses need an infrastructure that trains their personnel to sort and analyze this amount of data. That’s where data engineering services come into action. AWS and Azure data engineering services are offered by top data engineering services companies to build, develop, deploy, and maintain a customized IT infrastructure on the cloud. Businesses can partner with the service providers to streamline their data, systems, and processes to adopt the data-driven decision-making model. But what does data engineering mean? What is the role of a data engineer? Let’s find out. What are Azure Data Engineering Services? The term data engineering is the process of creating systems for almost all industries that collect and manage information. In other words, data engineering is the process of sourcing, transforming, and managing data from different sources. Data engineers mine data for insights. Their skill set allows them to construct architectures for extracting value from data, which are then applied to benefit a company. As a result, data is accessible and useful. An essential aspect of data engineering is the practical use of collected and analyzed data. Thus, data engineering uses different methods to gather and authenticate data, ranging from data integration tools to artificial intelligence. The same applies to data engineering services; sophisticated processing systems get designed and monitored to put found data in realistic situations. Essential Data Engineer Skill Set for Azure Data Engineering Services SQL A data engineer must be proficient in SQL as a foundational skill. The SQL language is essential for managing RDBMS (relational database management system). To achieve this, you will have to go through practicing many queries. To learn SQL, you don’t need to memorize a query. Learning how to optimize queries is crucial. Data Warehousing Understanding how to build and use a data warehouse is an essential skill. Using data warehouses, data engineers can collect unstructured data from several sources. After that, the information gets compared and evaluated to improve a company’s efficiency. Data Architecture For businesses to build complicated database systems, data engineers must have the necessary knowledge. Data engineering services & solutions include data architecture as a core offering. The term refers to data operations, which handle data in motion, data in rest, and datasets, with the relationship between applications and data. Programming Skill It is essential to improve your programming skills if you want to link your databases and work with different types of applications such as web, mobile, desktop, and IoT. To achieve this, you will need to learn a language that is suitable for enterprise use, such as Java or C#. Both are useful as part of open-source tech stacks, and the latter is helpful in Microsoft-based stacks for data engineering. Python and R, however, are the most important ones. Python can be used for various data-related operations with an advanced amount of knowledge. Data Analysis Data science is mostly associated with machine learning. A data engineer will be in a better position to excel if they understand how data can be used to analyze and model data. Having an understanding of the basic concepts will help you to better understand data scientists’ needs. Who are Azure Data Engineering Services Experts? With the help of data engineers, companies can replace their in-house data infrastructure with a robust information pipeline and transform their data into insights for business analytics. Across industries and businesses, data engineering services are now gaining popularity as a tool to extract valuable data. Not just Microsoft Azure, but data engineering services in AWS are also in high demand. In fact, Azure, AWS, and Google Cloud form the top three cloud platforms in the global market. With these services, you can ensure that valid data will be available at the right time, in the appropriate format, and in the right place. Azure Data Engineering Services: Roles and Responsibilities The following are some of the roles and responsibilities Data Engineers need to perform: Work on Data Architecture Data architects use a systematic approach in planning, creating, and maintaining data architectures while aligning them with business needs. Collect Data Getting the appropriate data from valid sources is the first step in building a database. The storing process of optimized data begins after data engineers plan a set of dataset processes. Conduct Research Data engineers conduct research in the industry to find a solution to a business problem. Improve Skills Theoretical database concepts aren’t enough for data engineers. They must have the knowledge and expertise necessary for successful development. Furthermore, they need to keep up with various machine-learning algorithms. They should have expertise in analytics tools like Tableau, Knime, and Apache Spark. These tools allow businesses to generate valuable business insights. Furthermore, a data engineer should also offer big data engineering services to handle vast amounts of data in real-time. Create Models and Identify Patterns In order to extract historical insights from data, data engineers use a descriptive data model. They use forecasting techniques to gain actionable insights about the future while developing a predictive model. Additionally, they provide recommendations for different outcomes using their prescriptive model. Why Do Modern Businesses Need Azure Data Engineering Services? Data Science tends to be the only way organizations can gain meaningful insights from their data. Companies can, however, build large, maintainable data reservoirs through Data Engineering. Data Science and Data Analytics can obtain useful results from these design data processes that are scalable. In order to enhance the efficiency and effectiveness of data analytics, accurate and reliable insights must be provided. Using AI and ML, companies are able to achieve higher efficiency, become agile, tap into new market opportunities, launch new products faster, and provide better service to their customers. Yet, according to an MIT Tech Review survey, 48% of

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