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.
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:
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 (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.
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 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 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.
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 (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 (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.
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.
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 and data flow, making it easy for employees across the business to access updated information at any time. The data warehouse is connected to data marts. These can be independent (standalones that function without data warehouses), dependent (use data warehouse as a primary source of information), or hybrid (a combination of both types and other operational systems).
Like data warehousing services, data engineering also includes data lakes to effortlessly handle big data and provide advanced analytics using AI and ML models. Business giants like Google, Microsoft, Amazon, etc., use big data engineering to handle the vast volumes of data generated every day. A data lake is where the data is stored in its native or unprocessed form. It is bigger, more flexible, and more scalable than a data warehouse and uses the ELT pipeline to share real-time insights. Data scientists and machine learning engineers work with data lakes to run predictive and advanced analytics.
OLAP stands for Online Analytical Processing and is a computing process where users can analyze multidimensional data. While traditional methods can process only two-dimensional data like tables, OLAP and OLAP cubes are suitable for modern data that comes in varied forms. OLAP data analytics consulting is an efficient method to accurately process large amounts of data and share the insights with the end users (employees, top management, stakeholders, etc.)
Streaming analytics is a process where datasets are continuously analyzed to derive insights instead of being processed in batches. This is useful for data sources that send data in small portions and when the flow is continuous. Azure, AWS, GCP, etc., have specific tools to set up the connections and provide streaming analytics to the end users (employees) through customized dashboards. You can also use third-party tools like Apache Storm, Flink, etc.
Enterprise Data Hubs (EDHs) are termed as the next-gen data architecture that helps share managed data systems and solve the problem of handling data in data lakes. If the data lakes are not properly maintained, they can turn into data swamps where the collected data is no longer usable (and doesn’t provide accurate insights). Unlike data warehouses, data hubs can support all types of data and can integrate with most systems. EDH is also known for better data management capabilities. A data warehousing company offering end-to-end services for large enterprises can build and integrate EDHs into your IT infrastructure.
The four V’s of data engineering are the same as the four V’s of big data.
Strategic and tailored data engineering solutions can redefine how a business handles its data, systems, workflows, and operations. Many components come together to form the fundamentals for efficient data engineering and management in an enterprise.
Find a dependable data engineering partner to understand your business needs and create a comprehensive strategy that aligns with your long-term goals and objectives. Be it data warehousing, data analytics, or end-to-end data engineering, make sure to hire a certified and experienced service provider to see results.
Data engineering services are essential to streamlining your data systems and effectively managing the data infrastructure in the enterprise. From setting up the data pipelines to delivering real-time data visualization reports, data engineering encompasses a series of complex responsibilities and operations. Partnering with an experienced data engineering company will help unlock the true potential of your business data and derive actionable insights. Make proactive business decisions to boost revenue and enhance customer experience.
Learn more about the importance of data engineering services from the links below.
A data engineer is expected to have a varied and extensive skill set as his job includes many responsibilities. A data engineer should be experienced in the following:
Additionally, interpersonal skills like communication and managerial skills like crisis management and problem-solving are also important.
Data Engineering Foundation is a certification course for data engineers to pursue a career in the field. It is a specialization from IBM and an online learning program for interested professionals who meet the eligibility criteria.
Data engineers are broadly classified into three types – generalist, database-centric, and pipeline-centric. While the generalist works with small teams and offers end-to-end data engineering consulting services, the data-centric data engineers work in large enterprises and take care of setting up extensive analytics. A pipeline-centric data engineer works with mid to large businesses and teams up with data scientists to handle the complex requirements of the establishment.
No. Data engineering is much more than ETL. ETL (Extract, Transform, Load) is the process of extracting data from various sources, transforming it to suit the operations requirements, and loading it to the data warehouse (or other data storage systems). ETL developers focus on ensuring data accuracy, quality, and consistency so that the derived insights are reliable and help in making proactive business decisions. ETL is a part of data engineering services and has a narrower scope.
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