The IT systems around us are becoming more complex and generating vast amounts of complex data to interpret. In addition, companies have large amounts of data stored on various media, which can be extremely difficult to explore without using the possibilities offered by artificial intelligence. Hence, the emergence of an essential and indispensable field of science, data engineering, enabling the building of pipelines that transform this data into a form accessible to data scientists. Data engineering and, therefore, data engineers are consequently just as important as data scientists.
Data engineering is the first step in transforming data into knowledge and business intelligence for companies worldwide. The variety of this profile and its functions makes it indispensable for any company wishing to be competitive in today’s digital economy, where the viability of economic models depends on the intelligent use of data. So let’s find out what are the benefits of using data engineering services.
What is data engineering?
The explosion of the data volume (big data) prompts companies to recruit teams specializing in big data processing and analysis. The data is a raw material that companies need to translate into useful information to usefully. Data scientists thoroughly analyze data, build predictive models, and communicate their results to their stakeholders. In this way, data becomes a contextual instrument at the service of decision-making. However, before we make the data understandable, we need to start by making it worthwhile. That is where data engineering comes in to build the necessary foundations for intelligent data analysis.
The role of the data engineer in the company is to design platforms (data warehouses) that expedite the processing of massive volumes of data. That ensures that data flows are transparent and secure enough for data analysts and data scientists to analyze them under the best possible conditions.
Data engineers’ missions
The primary mission of the data engineer is to design tools and solutions that enable the processing of large data sets. In addition, data engineers are responsible for designing and building systems called data warehouses for data scientists to do their job in good conditions. These repositories are to collect large amounts of data from various sources and share it with analysts.
In particular, the data warehouse operation is based on the ETL (Extract, Transform, Load) process. That is how most data flows are built and structured. Data is first extracted from various applications. Then the raw data is transformed into data useful for analysis. In summary, the job of a data engineer is to keep data sets up-to-date, documented, and of the highest possible quality.
Why do companies need data engineering services?
While the role of the data engineer is becoming crucial for many companies, some companies may not necessarily need it. So what are the situations where a data engineer would be helpful? We can identify three prominent cases leading to the demand for a data engineer.
That means that each business unit collects and manages its data independently of other departments. As a result, the data is spread across the company’s various business units: these, called “data silos.” With “frozen” data, it is impossible to have a global vision of its operations. Furthermore, data silos create duplicate and incomplete versions of data that are detrimental to machine learning work. As a result, companies suffer greatly from problems caused by data silos.
The data explosion in the significant data age has given these problems unprecedented importance. Therefore, to make informed and effective decisions, it is necessary to standardize and consolidate the data in advance, either in a specific location (Data Lab) or with a single repository (data warehouse/data lake). The data engineer intervenes to guarantee the standardization (or revocation of control) of this data and to develop applications that will use it.
Data standardization has only one goal: to support the decision-making process. Therefore, using the cleansed data must be adequately organized to obtain the indicators necessary for decision-making. Depending on the volume, data characteristics, and IT tools used by the company, this can be very complex, to the point where all discipline is required to deal with the topic. Business intelligence refers to methods, techniques, and IT tools used for effective data retrieval and decision support in data polling. Due to their proficiency in business intelligence, the data engineer is the most appropriate profile to create reports and indicators necessary to monitor the company’s activity.
Complex Use Cases Requiring New Paradigms
The scale of data growth today exceeds the reasonable possibilities of traditional technologies or even a typical hardware configuration supporting access to this data. The correct reasoning now is to distribute data storage and to parallelize its processing across cluster nodes. Likewise, new paradigms for the use case are necessary for other specific applications, such as on-the-fly or streaming data valuation. Companies can create these solutions thanks to data engineers’ diverse skills and in-depth knowledge of conceptual approaches to data processing.
Grow your business with data engineering
Data is the lifeblood of any organization. However, there is a crucial caveat: if the data is not accurate, up-to-date, consistent, and complete, it can not only lead to bad decisions but can even impact profitability. The growing popularity of data engineering is driving change by bringing together technology, process, the right people, and organizational culture to deliver accurate and valuable data.
There are two ways to implement data engineering: the first is to manage the data internally, and the second is to entrust this management to the service provider. Currently, the popularity of outsourcing data engineering services is growing. This process is beneficial to the company and can even contribute to the success of a given brand, as it marks the transition to a business model based not on fixed purchase and maintenance costs but variable service costs linked to the company’s consumption. However, regardless of whether you decide to create internal structures or use the services of an external company, there is no doubt that data engineering services should be used by any company that wants to implement data quality optimization processes to apply business analysis better.
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