As the years go by, the importance of data engineering grows. It can be observed that organizations more and more often invest not only in talents but also in infrastructure. Companies focus on building data teams and exploiting data platforms, which is why data engineering is on the rise. In this article, we will take a brief look at five important data engineering trends. What do you need to know about them? And how will they help your company to grow?
It is widely known that data is crucial for any enterprise. Every business collects, analyzes, and examines data in order to make important decisions. Due to the pandemic, many companies have moved their IT infrastructures to diverse cloud-based platforms. As a consequence, the demand for data engineers has increased in the last few years. Thus, the development of data engineering cannot be neglected. Here are the 5 latest trends in data engineering you need to know.
Data integrated into the cloud
The cloud offers mass storage. When it comes to analytics and data management, the implementation of the cloud is the future. Hybrid cloud plays a significant role as it combines public cloud and private infrastructure, which enables data migration. This way of sharing data helps companies, above all, to cope with fluctuations in demand. What’s more, thanks to hybrid cloud computing, companies can scale their on-premises infrastructure to the public cloud.
Consider platforms such as Google Anthos, Microsoft Azure, or AWS that provide flexibility, with which the launch of certain services can take place anywhere. Those platforms provide mechanisms that allow you to process and store data.
Customer and user experience
There is no doubt that data engineering plays a key role in the customer and user experience. The main idea is to create a seamless environment where all elements (from the client, through technology, to the user and the employee) intersect. A key is a customer-oriented approach with rich company-customer interaction.
Real-time analytics
Real-time data analysis is based on a constant flow of information in your organization. In order to make faster decisions, agile companies break down data so that it can be analyzed in real-time. Moreover, AI also proves to be helpful in generating immediate information.
A good example is Tesla. Their designers use machine learning algorithms that analyze data coming from cameras and sensors. Here, real-time data analysis provides an accurate response to various traffic situations. Another interesting example would be apps that track blood pressure or sleep quality. When more data is needed, new data analysis platforms must be used. Some examples of real-time analytics platforms are Kafka or Spark Streaming, or various cloud solutions such as Amazon Kinesis.
Notebook interfaces
Notebook interfaces have been extremely popular for many years. Moreover, they continue to win popularity among data engineers. Generally, notebook interfaces are used for data science, statistics, or machine learning. With notebooks, data scientists can view and explore information. In addition, they allow you to match and mix languages according to the requirements of a specific task. At the same time, they document and visualize the intermediate stages.
There is no doubt that notebook interfaces are a useful tool that, because it follows the methodology, makes it easy to duplicate calculations and results with different datasets.
Growth of CDPs (customer data platforms)
Customer Data Platforms are a type of database software that creates persistent and unified records of all customers. Moreover, it creates a complete picture of the customer on an individual level. How do you recognize a good CDP? When it integrates easily with existing data and when it enables easy recovery of stored data.
The CDP market is expected to grow by $10.3 billion by 2025. CDP has not experienced any disruptions or declines. No wonder that companies like to use this software. It helps them to move fragmented data to a single location efficiently. Moreover, CDP provides great analytical capabilities and allows you to get to know the client very well.
Data engineering has grown tremendously in the last few years. It was not only the pandemic that contributed to this, but also technological development. To sum up, let’s quickly summarize the five latest trends in data engineering worth knowing: Shift to cloud computing, the importance of customer and user experience, real-time analytics, notebook interfaces, and Customer Data Platforms.
All five of these trends are critical for businesses that want to make the most of big data. All these trends and solutions are extremely useful for companies that have been built around data as well as for those struggling with the permanent integration of data into their workflows.
If you’re looking for a solution that will enable your company to grow, see data engineering services provided by the AI-consulting company Addepto. They will help your business flourish.
