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.
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 that collaborate with data scientists and database administrators to streamline the data pipeline in mid and large-sized enterprises.
Data engineering services are varied and versatile. Data engineering 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 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.
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.
Data integration 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 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 removes errors and duplicate data to increase the accuracy of the derived insights.
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.
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 solutions to ensure that the business can make better decisions and become proactive. Enterprises should look to partner with companies that provide complete services using advanced data engineering technologies.
Many BI and AI companies offer data engineering system design and engineering as a service (EaaS) models to empower modern businesses to use datasets effectively by optimizing resources and reducing costs.
Here are some reasons for business enterprises to invest in data engineering services.
Every modern business needs its own IT architecture, be it on-premises or on the cloud platform. Data engineering services provide a comprehensive solution by designing the best possible architecture to connect data sources, storage, analytical tools, and visualization dashboards. Instead of working with multiple vendors, the organization can get expert services from a single company.
Since data collected is structured, unstructured, and semi-structured, it is essential to store all of it in reliable data centers without increasing the cost of storage. Furthermore, data engineers identify the right tools to compress large amounts of data without affecting its quality. A capable and strong IT system will result in accurate insights.
Data engineering requires working with various APIs to ensure a stable connection between different tools and applications. Data engineers have to use existing APIs and develop new ones that meet the business requirements. These allow large-scale data to flow seamlessly between the systems and ensure continuous productivity at work.
Modern businesses need agile systems that grow and change as the business expands. The responsibility lies with the data engineer to make sure the data architecture is built for the future and can be easily scaled over time.
Data security is one of the most vital aspects to consider when working with large datasets. A business that stores vast amounts of data is more prone to cyberattacks from hackers. The increase in cyber crimes around the globe is highlighting the need for robust data governance and secure IT ecosystems that cannot be breached.
Data engineers ensure that modern businesses have uncompromised data integrity and enable secure data transfers at all times. From determining access control to adding multiple security layers, different measures are implemented to prevent data breaches.
The end goal of adopting the data-driven model is to facilitate effective decision-making at each level. The insights derived through data analytics are not limited to the top management. Businesses can adopt the model across each department and empower all their lower and middle-level employees to make faster and better decisions by relying on real-time reports.
Data engineering services help managers gain an in-depth understanding of the business process, customer requirements, and changing market conditions. Data analysts can use machine learning algorithms to run descriptive and predictive analytics for sales forecasting and targeted-oriented marketing.
Modern businesses have to survive the intense competition in local, regional, and international markets. They need to stay one step ahead of their competitors, and this is possible when businesses effectively use the data collected from various sources.
Data engineering services empower organizations to use more of their data for advanced analytics. Both historical and real-time datasets are processed to get comprehensive reports about markets and customers. From product designing to marketing, businesses can take advantage of various opportunities to increase their market share.
Once data engineers set up the data infrastructure and connections, the visualization reports can be used by managers from all departments in the business. For example,
Though data analytical reports are prominent, these cannot be accurate and reliable unless data engineers build a robust foundation to deliver business intelligence services.
Error-free and agile data architecture will ultimately lead to higher productivity and performance in the business. This will increase the return on investment for enterprises and bring them greater profits. Data engineering services provide comprehensive support by minimizing the risk of losses due to security breaches, incomplete reports, wrong decisions, and market uncertainties.
Given the extensive nature of responsibilities, many modern businesses prefer to hire a team of offshore data engineers and ML engineers to build and manage complicated systems. Small businesses also find it easier to rely on service providers as building the infrastructure from scratch is cost-intensive and takes up too many resources.
Data engineering as a service is crucial for businesses that want to be proactive and take advantage of market opportunities before their competitors. It is the basis to build a strong data-centric model in the enterprise and make decisions based on factual and real-time information.
From reducing costs to increasing data security and empowering employees to be more productive, data engineering is at the core of it. Businesses can track their customers’ preferences and enter new markets when they rely on accurate reports. Data engineers organize data flow and take enterprises closer to their goals. Talk to our team to set up scalable and future-proof data infrastructure in your organization.