Data Science Archives

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
October 04, 2024
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11 Top Azure IaaS Companies for Scalable Cloud Solutions

Azure infrastructure as a service (IaaS) companies are third-party service providers that help startups, SMBs, and large enterprises build, deploy, and migrate IT infrastructure to the cloud. Here, we’ll discuss the leading Azure IaaS companies that offer scalable cloud solutions. IaaS stands for Infrastructure as a Service, a cloud solution where a majority (or the entire) IT infrastructure of a business is hosted on a remote cloud server. This reduces the load on the systems used on-premises and speeds up the processes to increase efficiency. Many companies offer IaaS solutions by migrating and building the IT infrastructure on public, private, and hybrid cloud platforms. According to Statista, the public cloud IaaS market is expected to be $180 billion in 2024 and predicted to reach $232.39 billion by 2025. Microsoft Azure, AWS (Amazon Web Services), and Google Cloud form the top three cloud providers offering IaaS, PaaS, and SaaS solutions in the global market. Azure IaaS is a flexible, scalable, and cost-effective option for businesses to host their IT infrastructure on the cloud. In this blog, we’ll read more about Azure data engineering and the top companies that help businesses build scalable IaaS solutions on Microsoft Azure. What is Azure IaaS? Microsoft Azure has grown exponentially since 2010, the year of its launch. It offers public and private cloud solutions and boasts a large ecosystem with over 200 apps, frameworks, tools, technologies, databases, etc. It is a suite of cloud services for enterprises to build virtual machines, networks, storage centers, applications, processes, etc., remotely and access them from any location at any time. Azure IaaS makes it easy to scale the processes as and when necessary and pay only for the solutions used by the business. Additionally, it supports big data analytics in real-time and offers effective capabilities like data security, data backup and recovery, risk management, and much more. What is an Example of Azure IaaS?- Key Components A few popular Azure infrastructure-as-a-service examples are listed below: VMS Azure Virtual Machines are used to deploy and manage Windows and Linux operating systems on a remote cloud server instead of on-premises. The virtual machines can be customized to suit the requirements of the business. For example, CPU, memory space, storage, etc., can be individually selected to customize the VM. This allows businesses to create powerful and super-fast virtual machines capable of processing large apps seamlessly. Networking A network is a set of connections between various components in the IT infrastructure. It also helps in managing resources. Azure’s network services consist of load balancers, virtual networks (VNet), VPN gateways (for greater data security and privacy), etc. Storage Not every business needs the same kind of storage options. Moreover, a single enterprise might use different storage models for different types of data. Azure IaaS solutions include a range of storage solutions like file storage, disk storage, queue storage, blob storage, etc. Security Data and system security is a huge concern in today’s scenario. Microsoft Azure has many options to ensure security and identity management on the platform. Key Vault, Active Directory, and Security Center are the top three features that help businesses manage user identities and provide/ limit access to data and resources. Leading Azure IaaS Companies for Scalable Cloud Solutions DataToBiz DataToBiz is among the leading Azure IaaS Companies offering tailored data engineering services, data analytics, business intelligence, and artificial intelligence solutions to clients from around the globe. The company has experience in working with SMBs, MSMEs, startups, and established enterprises from various industries like manufacturing, telecommunications, supply chain, transportation, real estate, hospitality, healthcare, finance and banking, education, surveillance, etc. The company is a certified Gold Partner of Microsoft (as well as a certified partner of AWS and Google Cloud) and provides scalable, IaaS solutions for cost-effective prices. Businesses can hire an Azure infra developer or a team of experts to set up their IT infrastructure on the Azure cloud. DataToBiz offers IP protection and guaranteed NDA to ensure confidential data is secured and encrypted. The award-winning company’s end-to-end customized services empower businesses to unlock the full potential of their data and achieve their goals. Clients can choose from the different hiring models offered by the company. Clutch Rating: 4.8 Stars Simform Simform is an AI and ML company offering AWS and Azure data engineering services. It has been listed as the top US software development company by Clutch and has clients from the Fortune 500 Companies list. It also works with startups and progressive enterprises. The company provides serverless development and consulting services to help businesses migrate to the cloud platforms and digitalize their processes. It builds stable, reliable, scalable, and secure solutions for businesses from industries like manufacturing, healthcare, logistics, financial services, retail, etc. Clients can integrate the serverless architecture with Azure, AWS, and Google Cloud to scale up the apps whenever required. Simform also offers serverless storage management, API gateway integration, and management and designs FaaS (financial accounting advisory services) based on client’s specifications. The company’s business-centric approach and delivery-oriented mindset ensure guaranteed results for clients. Clutch Rating: 4.9 Stars Persistent Systems Persistent Systems is a digital engineering and enterprise modernization company offering Azure infrastructure as a service (IaaS) solutions to various clients. It is a certified solution partner of Microsoft and provides diverse services for businesses to adopt digital technologies and invest in cloud solutions. Be it migration and management of M365, data security, modernizing legacy apps, or Azure modernization using generative AI, the company uses its vast experience and access to the Microsoft ecosystem to deliver the promised results. It follows a proven methodology to migrate the IT infrastructure to Azure and build custom-designed frameworks, apps, etc., to leverage the full potential of business data and processes. Enterprises can rely on Persistent Systems for Azure infrastructure diagram and end-to-end innovation and implementation on the cloud. The company has clients from industries like finance, banking, insurance, software, telecom, media, consumer tech, life sciences, etc. Clutch Rating: 4.5 Stars ValueCoders ValueCoders is a software company offering IT staff augmentation

Yashica Chopra
October 22, 2023
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18 Best Tips to Choose an Azure Consultant

Cloud platforms like Microsoft Azure have a major role in digitally transforming a business. Consulting firms help establishments migrate to the cloud and revamp their systems. Here, we’ll discuss the tips to choose an Azure Consultant for your organization for your organization. Digital transformation is an ongoing business process where the operations and systems are digitized and migrated to the cloud. It helps strengthen the business’s IT infrastructure and ecosystem to make data-driven decisions using real-time insights. Cloud solutions have a vital role in digital transformation. Microsoft Azure is among the top three cloud platforms offering storage, SaaS, and IaaS solutions to businesses from around the globe. The platform recorded 722.22 million users in 2022. According to the statistics, almost 70% of global companies have used Azure one way or another since 2015, and 85% of Fortune 500 companies use Azure Cloud solutions. A majority of these organizations have worked with Azure consultants to migrate their data and systems to the cloud platform. However, choosing an Azure consultant can be confusing and overwhelming without proper planning. Hire the best Azure consulting company to manage digital transformation and offer customized end-to-end services to revolutionize your business. What does an Azure Consultant do? Azure consultants are third-party service providers who share their expertise with various businesses to help with cloud migration, deployment, customization, and integration. Azure consulting services are popular since the engineers bring a fresh perspective and have the expertise to solve your business problems effectively. The consultant has varied responsibilities, some of which are listed below: Partnering with a consulting company gives you access to a team of talented Azure experts who work in tandem to complete the project and deliver the promised results. However, your success depends on how well the consultants do their job. So, how do you evaluate the performance of your Azure consultant? Tracking KPIs (key performance indicators) and metrics is the best way to evaluate your decisions and progress. Consider aspects like the project timeline, problems solved, communication, ease of working, etc. The consultant’s progress should align with your business development. How to Choose the Right Azure Consulting Firm? Find a Microsoft Certified Partner The first factor to consider when choosing Azure data engineering services is partner certification. While many offer cloud migration services, they don’t have access to the platform’s tools and technologies as a certified partner does. Find a Microsoft Azure Certified Partner with the necessary experience, expertise, and skills to handle the project. Industry-Wide Experience Find out if the consulting company has experience in your industry or adjacent industries. This ensures that experts have prior knowledge about the specific standards, requirements, and regulations exclusive to your sector. It allows the company to easily customize the apps and solutions to suit your needs. Project Portfolio What other businesses did the consulting firm work with? Is the project portfolio diverse or limited? Every enterprise has different requirements. When a service provider works with multiple clients, they develop the expertise to solve complex issues that arise during Azure migration. It will be beneficial if the consultants have worked on similar projects. Approach to Azure Migration Enquire about the company’s approach to implementing the cloud migration process. Does it align with your business processes? Can they adjust according to your work environment? Do they offer remote services and collaborations? You can get the most out of your Azure consultant when you have similar business values or have a flexible approach to work. Training and Support Another aspect to consider is whether the consulting company offers to train your employees to use the latest software and applications. Involving the consultants in preparing the training programs will help effectively empower your employees to adapt to the changes in the organization. Similarly, partner with a service provider who offers long-term maintenance and support to manage and upgrade the systems. Communication, Transparency, and Reliability While technical experience is necessary, how the consulting company presents itself is also important. For example, can you easily communicate with the team? Do you understand each other? Are they transparent in their services and pricing? Can you rely on the Azure migration partner to solve complications and provide useful solutions? Other Relevant Services The Azure consulting company doesn’t have a focus exclusively on cloud migrations. In fact, it’s best to choose a firm that offers extensive digital transformation services like business intelligence, artificial intelligence, data science, big data analytics, data engineering, etc. That way, you can partner with a single service provider to manage your entire IT infrastructure and business operations. What to Look for When Choosing the Right Azure Consultant? Scalability Every business has a long-term plan that involves growth and expansion. Choose an Azure consulting engineering company that offers scalable services for cost-effective prices. That way, you can expand your business operations without finding another partner. Flexibility and Agility Your business needs will change over time. You may have to adopt an advanced tool or build a new app exclusively for your establishment. The Azure consulting firm should be capable of handling such changes and accommodating your needs. Data Compliance and Data Security Data is vital in today’s world, and businesses have large amounts of confidential data (belonging to customers, employees, stakeholders, etc.). Ask the Azure consultants about their data compliance policies and how they intend to ensure security for your databases. Billing and Pricing Cloud services are not cheap and can quickly add up as you include more requirements. Discuss the price plans in detail to see if they fit your budget. Small businesses may benefit from pay-as-you-go services where it’s easy to upgrade or downgrade the plan. Cultural Alignment This is another important factor to look for when choosing an Azure consultant. Different businesses follow different types of work cultures. Consulting firms tend to be more flexible to work with varying needs. Still, it’s a good idea to make sure your employees can team up and collaborate with the consultants. Geographical Limitations Geographical boundaries are no longer as restrictive as before. However, they can still play a role in how effective the consultants can be. It’s

Yashica Chopra
October 11, 2023
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How to Ensure a Smooth Azure Migration?

Microsoft Azure is a powerful cloud environment with various services, applications, and tools. Businesses can build, deploy, maintain, migrate, and upgrade IT infrastructure on the cloud. Here, we’ll discuss the steps on How to ensure a smooth Azure Migration. Microsoft Azure is a cloud platform that offers a range of cloud-based services, technologies, and tools to various business organizations from around the globe. Businesses use Azure to build, deploy, and host their applications and IT frameworks on the cloud. It offers scalability, security, and tailormade services. While there are many other cloud service providers, Amazon (AWS), Google (Cloud), and Microsoft (Azure) continue to be the top three vendors, owning a combined market share of 65% in the first quarter of 2023. Microsoft has steadily gained its market share over the last three years to touch 23% in Q1 of 2023. The company has generated $22.1 billion through Intelligent Cloud sales for the same period. This includes Azure’s services and other cloud computing offerings. Business enterprises can rely on Azure consulting service providers to migrate their data and IT systems to the cloud platform. The consulting firms offer end-to-end solutions to streamline the process and minimize disruptions. In this blog, we’ll find out how to seamlessly migrate your data to Azure. What is the Azure Migrate Service? How would you ensure the smooth migration of applications into the cloud? According to the latest forecast by Gartner, the global end-user spending on public cloud services is estimated to grow by 21.7% from 2022 to 2023 to touch $597.3 billion. The report also says that 75% of organizations will adopt a digital transformation model and use the cloud as their primary platform by 2026. Additionally, the SaaS spending in 2023 is projected to be $197 billion, registering a growth of 17.9% from last year. Azure Migrate is the process of migrating, optimizing, and modernizing your data and systems to host them on a cloud platform. It has an extensible framework to facilitate third-party integrations and supports varied use cases. It includes pre-migration stages like discovery, evaluation, sizing, etc., for data, applications, and infrastructure. The Azure Migrate hub has a range of tools to speed up the process and minimize the risk of errors and data loss. Azure experts with Microsoft certification have access to these tools to complete the process successfully. Ensuring a smooth Azure migration – The strategy behind Migrating data and infrastructure to Azure is a step-by-step process that involves different tools and applications. It can be categorized into the following: Evaluate The first step is to identify the databases ready for migration to Azure. Here, businesses need to create the Azure Migration Strategy after evaluating the benefits and challenges of the process. What aspects to consider during migration? Which challenges to overcome? Migrate This is the migration stage where experts use Azure Migrate or the Azure Database Migration Service offered by Microsoft to automate the process. It saves time and minimizes the risk of interruptions or errors when the task is performed manually. Optimize Once the databases are migrated to Azure, they should be optimized to deliver better results. It is a post-migration process necessary to increase operational efficiency, reduce costs, and scale resources. This allows businesses to fully use Azure’s capabilities to streamline their processes. Monitor Though monitoring is considered the final step, it is a continuous process to measure the health of the databases, applications, systems, etc. This gives organizations more control over the performance of their IT infrastructure and regulates the operations in the Azure environment. How to ensure a smooth Azure Migration? Businesses are moving from on-premises databases and storage centers to remote cloud solutions for greater agility, flexibility, scalability, and efficiency. However, cloud migrations are complex and should be handled by experts. Hiring Azure consulting services for data migration will save time, reduce risk, and increase overall performance. Examine Workloads Start by examining the on-premises workloads and databases to determine which ones need priority migration to the cloud. Contrary to the general opinion, moving your entire IT infrastructure and outdated databases to the cloud may not be a smart move. It could also lead to greater costs. For example, migrating virtual machines to Azure is expensive. Building one directly on the cloud and setting up integrations will be a better option. Assess every database, application, and tool used in the enterprise and shortlist the ones that can be migrated. Prepare for Data Transfer The next step is to determine the amount or volume of data that needs to be transferred to Azure. The transition duration will depend on this as large databases take more time and cause give rise to certain complications. Azure experts will find the best method to migrate the data to minimize downtime and glitches. Prioritize business-critical data necessary for everyday use so that employees won’t face delays or have to pause daily operations. That way, businesses can prevent losses and ensure customers are not affected by the process. Categorizing Data – Hot vs. Cold An easier way to prioritize and prepare for data transfer is by categorizing data. It helps optimize the storage strategies and manage Azure workloads. Basically, data is classified into two types – hot and cold. Hot data refers to the databases and information employees need access to for their day-to-day work. It is business-critical information necessary for everyday decision-making. This data is prioritized and migrated at the earliest. Cold data is historical and other relevant information that is not vital for daily work but is still important for the business. This data can be migrated over time after choosing more affordable cloud storage options. Finalize Data Transfer Strategy It’s time to choose and decide on the best data transfer strategy to migrate it on-premises to Microsoft Azure. One method is to use the Azure Backup Service to secure cold data and migrate the applications to the cloud. Then restore the data in the new cloud infrastructure. Another method is to use the Azure Import/ Export service to move large volumes of data by sending the disk drives

Yashica Chopra
September 05, 2023
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Top 13 Data Engineering Trends and Predictions 2023

Data engineering is the process of building, deploying, and integrating data pipelines to streamline data flow within an enterprise. It is the foundation for business intelligence processes to run and deliver actionable insights. Here, we’ll discuss the top data engineering trends and predictions for 2023. Data engineering is a growing discipline in the global market. It involves the process of designing and building data pipelines to collect, transform, and transport data to end users (data analysts and data scientists) to derive actionable insights. The data pipelines have to connect all data sources to the central data warehouse or data lake. The success and accuracy of data analytics depend on how well data engineers set up the foundation. This requires high-level data literacy skills. Unfortunately, there is a gap between the demand and supply of qualified and experienced data engineers in the market. It’s one of the primary reasons many SMBs and large enterprises partner with offshore data engineering companies to adopt advanced data-driven technologies and processes for effective decision-making. Many experts feel that 2023 will be a vital year for data engineering. In this blog, we’ll take a detailed look at the various big data engineering trends and predictions that will transform the industry at different levels. 13 Top Data Engineering Trends and Predictions in 2023 1. Increase in Cloud Management Cloud has become a favorite for many businesses around the world. Small, medium, and multinational companies are moving their data and IT infrastructure from on-premises to cloud servers. Data Engineering in AWS (Amazon Warehouse Services), Microsoft Azure, Red Hat, etc., are in high demand. While some companies are directly building data pipelines on the cloud, others are migrating their existing systems to cloud servers. 2. Greater Budget Allocation for FinOps Another trend is the need for data cloud cost optimization. Top vendors like BigQuery and Snowflake are already talking about ways to optimize the data cloud cost and make cloud services more cost-effective to businesses from various industries and markets. Financial managers are becoming a part of data teams to ensure that their data engineering strategies and processes will deliver the necessary returns. While there aren’t enough best practices in the industry (data engineering is still in its early stages), data teams are finding ways to overcome the challenges and make their cloud-based data architecture more agile, flexible, scalable, and future-proof. The cost of ownership is also a crucial topic of discussion. 3. Usage-Based Data Workload Segmentation In the current scenario, companies are focusing on using a unified cloud-based data warehouse. For example, AWS data engineering is popular for offering data warehousing services to several business enterprises. However, the same type of database cannot be suitable for all kinds of data workloads. Experts predict that organizations will shift from data warehouses to data lakes where different databases and tools are individually organized and grouped into a unified setup. This can make the data architecture cost-effective and increase its performance. 4. Data Teams with Higher Specializations Though data engineers are in short supply due to the complexity of the job, data teams will continue to expand and include professionals with more specializations. For example, the data teams will have data engineers, data analysts, data scientists, analytical engineers, etc., to handle different aspects of establishing and using the data architecture in an enterprise. DevOps managers, finance managers, data reliability engineers, data architects, data product managers, etc., are other specializations we will see in future data teams. 5. Metrics Layers in Data Architecture In traditional data pipelines, the metrics layer (also called the semantics layer) is in the middle, between the ETL (extract, transform, load) layer and the cloud data warehouse. It defines the metrics for the values in the data tables and ensures consistency to eliminate errors during business analytics. Experts predict that the metrics layer will have an addition of a machine learning stack that has its own infrastructure. The ETL layer will continue to do its job, but the data will flow through the machine learning stack, which will help data scientists choose the right metrics for the given data. One day, the metrics layer and the ML stack will be combined to work as a single automated unit. 6. Data Mesh The concept of data mesh is one of the emerging DE trends discussed by many top companies. This new architectural model is said to help organizations overcome the limitations of traditional data warehouses and centralized data lakes. Date mesh is the decentralization of data governance and ownership. As discussed in the previous trends, domain-specific data platforms, tools, and databases will be established for greater efficiency. The idea is to build resilient, dynamic, and agile data pipelines that offer more autonomy, interoperability, and control to every member of the data team. However, establishing a data mesh requires more skills and tools. However, centralized data warehouses will continue to exist until enterprises can successfully build and deploy data mesh architecture. 7. Increase in Success of Machine Learning Models In 2020, a report by Gartner shows that ML models had only a 53% success rate. That too when they were built by companies with strong AI foundations and prior experience. It means even three years ago, only half the machine learning models could be deployed accurately and effectively. However, the success rate has been increasing over time. Soon, a greater percentage of the ML models can be successfully deployed by organizations. Of course, this will be possible when businesses overcome challenges such as misalignment of needs and objectives, overgeneralization, testing, validating issues, etc. 8. Changes in Cloud-Premises Architecture The architecture for data flow within an enterprise usually combines three different software applications. Databases from different departments (CRM, CDP, etc.) are connected to the data warehouse. The business intelligence and data visualization tools are connected to the other end of the data warehouse. Data flow occurs only in one direction. However, in modern data engineering, the data flow will occur both ways. The next-gen cloud data architecture will be bi-directional and allow data

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

Pawan Chabra
November 21, 2022
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AWS Data Engineering Guide: Everything you need to know

Enterprises are increasingly moving towards cloud platforms to achieve business objectives and optimize their business operations including data management. Not only these services have transformed the game of managing data and applications, but many Cloud Services have also dished out brilliant user experience at an inexpensive cost with the addition of Data Analytics for research. It further leads to the simplification of processes, allowing organizations to focus more on business growth. Several Data Engineering processes have come into the picture for the seamless management of Cloud Services. Names like Google Cloud, AWS, and Microsoft Azure have designed proper Cloud Infrastructure for organizations and individuals. To provide a seamless experience to users, these Cloud Platforms use several solutions such as Data Migration, Data Engineering, and Data Analytics. AWS Data Engineering encompasses one of the core elements in the AWS data platforms which provide a complete solution to users. It also manages data pipelines, transfers, and storage. For instance, to transform data into a uniform schema, AWS Engineering utilizes AWS Glue to give out all the functionalities. Moreover, it handles the Data Catalog that poses as a central repository of metadata. AWS Glue is capable of handling tasks completed in weeks rather than months. On the other hand, Data Visualization, with an apt representation of data using interactive charts, graphs, and tables, plays an important role in AWS Data Engineering. All the information from the Data Warehouse and Data Lakes poses as inputs for the tools to generate reports, charts, and insights from AWS data tools. Simply explained, data warehouse stores and uses structured data that is ready for strategic analysis while a data lake uses and stores both structured and unstructured data for use in the future. Advanced BI tools powered by Machine Learning provide deeper insights from data and help users find relationships, compositions, and distribution in data. What is Data Engineering? For us to understand Data Engineering, we have to understand the “engineering” part better. What do Engineers do? They design and build things. Therefore, data engineers can be thought of as people who design and make pipelines that change and help transport data in a format. This format helps the data to reach the Data Scientist or other users in a highly usable state. These solutions collect data from several sources and accumulate them in a single warehouse that holds all the data as a single source of truth. Over the years, the definition of Data Engineering has not changed much even though the technology and the tools have changed drastically. In simple words, Data Engineering is the foundation that holds data science and analytics together with the use of technology and data processing. Moreover, while conventional technologies like relational and transactional databases still have a place in big data architecture, fresh tools and technology have created innovation in the space. What is AWS? AWS, short for Amazon Web Services, is an on-demand cloud service provider that has various offerings under its umbrella. The organization is a subdivision of Amazon that can provide infrastructure, distributed computing facilities, and hardware to its customers. The various offerings from the organization are known as Infrastructure as a service (IaaS), Software as a service (SaaS), and Platform as a service (PaaS). AWS competes with names like Microsoft Azure, Alibaba Cloud, and Google Cloud. All these organizations are focused on improving the performance of an organization and reducing costs at the same time. Most of these platforms charge their users on a per-use basis. In comparison, an organization need not invest in setting up and maintaining complex IT infrastructure for its requirements at its premises. AWS data centers are located in various parts of the world and the customer has the choice to select the data center that is closest to their target customer. The various services offered by AWS include Security, Data Warehouse, Data Analytics, Cloud Computing, Database Storage, etc. AWS data management allows auto-scale with which a user can scale up or down the requirements for storage and computing capabilities based on the requirements of the business. What is AWS Data Engineering? There has been a phenomenal increase in the volume of data that is being generated by businesses and consumers. Organizations are looking at solutions to help manage, process, and optimally utilize this data. As a result, AWS Data Engineering came into the picture which can package and handle all the requirements of the customers as per their needs. An AWS Engineer is expected to analyze the customer requirements and propose an integrated package that can provide an optimal performance ecosystem to the organization. AWS Data Engineering is also used to ensure that data presented to the end users are in an analysis-ready form and can deliver the right insights. AWS Data Engineering Tools In recent times, we have seen several changes because of different tools designed by AWS for specific needs. The various tools used in the AWS ecosystem can be explained as follows: Data Ingestion Tools These tools are used to extract various types of raw data like text from multiple sources, real-time data, logs, etc which are then used to store in a storage pool. The data ingestion tools provide solutions with which users can collect data from multiple sources. It is one of the most time-consuming processes in the AWS Data Engineering cycle. The data ingestion tools provided by AWS are as follows: Amazon Kinesis Firehose The Kinesis Firehose tool from Amazon can deliver real-time streaming data to the S3 tool. It also can configure the data transformation before it is stored on the S3. Kinesis Firehose supports encryption, compression, and data batching features The scalability and volume depend on the data streaming yield. Kinesis Firehose is used in the AWS ecosystem to provide a seamless transfer of encrypted data. AWS Snowball Snowball from AWS is an amazing tool that can handle enterprise data from on-premise databases to the S3 tool. To avoid data and effort duplication, AWS used a snowball technique that can be

Yashica Chopra
October 20, 2022
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Why Should You Hire A Data Engineering Consultant – Full Guide

Data engineering is the process of establishing the data architecture to perform data analytics and create visualization reports. Data engineers offer a range of services for enterprises to use real-time insights for decision-making. We’ll discuss the need to hire a data engineering consulting company for a business. Businesses need a competitive advantage in today’s market. Technology plays a major part in empowering an organization to be ready for market opportunities. Data is another vital and integral part of a business. Combining both aspects makes an enterprise capable of using data to make better decisions. However, raw data cannot be of much use to end-users and decision-makers. It has to be cleaned, formatted, structured, and formatted to prepare for analytics and business intelligence. That’s the responsibility of data engineers and data engineering consultants. These are the ones who ‘engineer’ vast amounts of data and prepare it for data scientists to perform advanced analytics using AI and ML algorithms. According to Zippa, there’s a high demand for Data Engineers in several parts of the US. From Silicon Valley in California to New York, Chicago, Virginia, Miami, Dallas, Washington, and many more, almost every US state has businesses looking for Data Engineers to help with digital transformation. The Data Engineering market in India is also growing rapidly. Indian Data Engineering is expected to grow at a CAGR (compound annual growth rate) of 36.7% by 2027 and touch 86.9 billion USD. The banking and insurance sector has employed the highest share of data engineers in India at 37.7%, compared to other non-IT sectors. Let’s find out why you should hire a data engineering consultant and what you need to consider when choosing a consulting company for your business needs. What is Data Engineering? Data engineering is a critical part of the data science hierarchy. It is the process of preparing and managing big data in an enterprise for data scientists to perform analytics and drive actionable insights. Data engineering ensures that the data used for business intelligence is of high quality and delivers accurate results. Data engineering involves the building of data models and connecting them to data pipelines. It includes ETL (Extract, Transform, and Load) of big data to avoid redundancy and filter poor-quality data. As the demand for business intelligence and actionable insights grows in the market, the need for qualified data engineers has also rocketed. According to a survey conducted by Interview Query, the demand for data engineers grew by around 40% in 2020. Data engineering is the foundation on which date scientists build their AI and ML models. What Does a Data Consultant Do? A data consultant creates the base work to collect data and process it for analytics to drive insights. They make use of the latest tools and technologies to clean, format, and structure large volumes of data in a quick time. The main job of a data engineering consultant is to identify the right technologies, processes, and strategies to manage data in an enterprise. In 2017, Gartner reported that 85% of big data projects don’t succeed. Two years later, in 2019, Gartner reported that 80% of the projects continued to fail. The lack of proper big data processing infrastructure is considered the reason for this failure. A data engineer or a data engineering consulting company works to set up and streamline the infrastructure. They use a plethora of technical skills to achieve this. SQL, programming languages (python, java, etc.), cloud computing, etc., are just a few things a data engineer should work with. Data engineering is further classified into three types- generalist, database-centric, and pipeline centric. While a generalist data engineer works with startups and small enterprises, the other two deal with mid-sized and large enterprises with multiple data warehouses and complex data flow. You can hire a data engineering consultant from a consulting company or a solution provider to handle data pipelines and infrastructure within your enterprise. They work with various tools like: What Do Engineering Consulting Firms Do? Data engineering firms offer end-to-end solutions to collect, store, process, analyze, visualize, and manage big data in an organization. The consulting firms provide one-stop solutions to adopt and implement the data-driven model in a business. They help the employees and top management understand how to use data and insights in real-time to make faster and better decisions. The role of data engineers has changed over the years. The companies no longer limit themselves to providing ETL services. Managing and scaling the data infrastructure is also a job for data engineers. They support data teams in your enterprise to improve performance and efficiency. Data engineer consulting firms constantly work on improving, scaling, and customizing the infrastructure to suit your business needs. They help reduce the cost of projects in the long run by creating a reliable and strong foundation to build new models without starting from scratch every time. The firms also offer off-the-shelf solutions to help enterprises develop their own algorithms and data flows. Airbnb and Netflix are two examples of companies that took this approach. In short, data engineering consulting companies have a versatile role to play in the current scenario. They provide primary services for businesses that rely on available technology and scalability (plus management) services for organizations that have self-servicing internal models. Either way, the data engineer is a must-have on the team. Why Hire Data Engineering Companies? You need a data engineer to establish the groundwork for big data analytics. Business intelligence is used at all levels in an enterprise and in different departments. From production to quality testing to accounting, recruiting, logistics, sales, marketing, and customer service, insights derived from data analytics are used to make the right decisions for your business. But why do you need a data engineering company? Why can’t you simply hire a data engineer on the payroll? The answer is simple. A consulting company has a team of qualified and experienced data engineers working together to offer tailor-made solutions for your business. The increasing demand for data engineers makes it difficult to find

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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