The volume of data in our world has grown unprecedentedly, and managing this abundance of information is daunting. However, collecting data is only the first step; making sense of it is the real challenge. Data engineering is the discipline that unlocks business insights from vast amounts of data by identifying trends and finding practical applications for the data they collect.
The role of data to shape business decisions has become increasingly important; the enterprise needs to become a data company. However, collecting, organizing, and analyzing all this data can be overwhelming without the proper tools and expertise. With the right data engineering systems, companies can collect and analyze raw data from various sources and formats, empowering them to make informed decisions about everything from customer feedback to sales performance and improve their operations. To succeed in a world of data, embracing data engineering to manage data and prepare them for the future proactively is essential.
Why is data engineering necessary?
Most companies today deal with a lot of data, and making sense of it can be challenging. Data engineering is a field that has grown to address this bottleneck by combing through data easily and securely. Analyzing the data can be especially difficult because disparate data are managed by different technologies and stored in various structures. Nevertheless, companies can access and analyze all their data rapidly with data engineering.
For example, consider a brand that collects multi-source customer data:
- One system contains billing and shipping information.
- A different system retains order history.
- And other systems store customer support, feedback, behavioral information, and third-party data.
Collectively, these data provide a comprehensive customer view. But data stored in disparate places makes answering specific questions difficult—for example, what types of orders yield high customer support costs?
Data engineering unifies independent data sets and enables you to find answers to your questions quickly.
Data should be processed through data engineering. Why?
Data engineering plays a pivotal role in the ever-evolving landscape of modern data analytics. These professionals craft and support complex data environments, which enable organizations to derive insights from vast amounts of data.
In the past, they focused on shaping carefully structured data warehouse schemas with finely-tuned table structures and indexes to ensure adequate performance for high-demand queries. With the emergence of data lakes, the role has expanded to manage and structure unformatted data for analytics. This entails careful attention to detail and deep knowledge of data management principles to deliver reliable and accurate data to downstream consumers. With the critical contributions of data engineers, organizations can harness the full power of their data.
After thorough data engineering processes, data sets become more understandable and less time-consuming to analyze. To keep up with the never-ending data influx in businesses, it is crucial to deploy automation tools such as Azure Databricks that simplify data cleaning and formatting. With the proper software stack, businesses can expect many benefits in the form of streamlined end-to-end data journeys, also known as “data pipelines.” As data passes through these pipelines, it undergoes multiple transformations, enrichment, and summarization, from which can be extracted immense value.
What do data engineers do?
The demand for data engineering professionals is rising, and with good reason. These individuals possess the expertise and knowledge to create systems that combine scattered data sources and provide efficient navigation.
Irrespective of your business background, partnering with Azure data engineering service provider—such as PreludeSys—can give you a critical edge in today’s data-driven marketplace. We design and implement data management solutions enabling organizations to maximize their data value. Our skilled data engineers perform the following tasks using Azure services:
Acquire: Find different data sets around the business.
Cleanse: Identify and correct data errors.
Transform: Standardize data formats.
Disambiguate: Interpret data and remove ambiguities.
Deduplicate: Remove duplicate data copies.
Azure data engineering process
As businesses grow and expand, extracting valuable insights and turning that data into meaningful business knowledge is a challenge. Data engineers build high-performing data infrastructures designed to optimize your data and prepare it for your organization’s analytics needs.
1. Generate and store raw data
Organizations gather data across different business areas, from internal (payroll, production, sales, marketing, and logistics) and external sources (partners, customers, and potential leads). Traditionally these data were stored on-premises using servers such as Oracle, Microsoft SQL Server, and PostgreSQL.
Fortunately, modern cloud computing offers more flexible, cost-effective, and large-scale storage solutions such as Microsoft Azure, Cloudera Data Platform, and AWS and cloud-based tools for different functions such as Salesforce, Power BI, Azure Synapse, and Marketo.
Data engineering collects data from disparate sources and stores them securely in cloud data warehouses such as a data lake or data lakehouse, which makes it easy for the modern BI platform to perform further analysis and generate insights
2. Extract, transform, and load (ETL)
Raw data is the first step in a long journey toward proper analysis. To be usable, it should be transformed into a more manageable format capable of integration with other sources. Data engineers perform ETL, which extracts data from various sources, converts it into a uniform format, and then loads it into a repository. The result is a streamlined database that is ready for proper analysis.
The extraction process involves identifying the relevant data sources, while the transformation cleans, sorts, and standardizes the data. Finally, the data is loaded into a repository for database building. Cloud-based storage solutions are the most effective repositories now as they can store all kinds of data, from structured to unstructured data and everything in between. They offer a cost-effective and labor-saving solution for businesses with flexibility and scalability.
3. Model data
Once the data is stored in the warehouse or lake, data engineers organize it into tables and objects, making it easier to find and analyze. They create relationships between different data and create tables to ensure that the information is joined seamlessly when a query is initiated. This process sets the foundation for business analysts and BI users to develop analytics from the data.
Why PreludeSys for data engineering service?
Do you want to avoid dealing with scattered data across your organization?
If you’re looking for an expert data engineering service provider to handle all your data estate needs, PreludeSys is the way to go. As a certified Microsoft Gold Partner, we offer end-to-end solutions to explore, prepare, manage, transform, and serve data at scale. Our engagement model covers all aspects of the process, from consulting to implementation, support, and optimization services. We have a proven track record of empowering mid and large-scale companies to manage their data needs and improve their business operations. Trust PreludeSys to be your partner in achieving data management through effective data engineering.
Learn more about building a successful data architecture using data engineering