It is indispensable for every data engineer to keep up with the trend to thrive in the data engineering world. As the need to extract every bit of information from the data has increased, data engineering is ever booming. Every organization is looking for a data engineer who can reduce the complexity of the operational processes and help them achieve business growth. Here are the booming aspects a modern data engineer should concentrate on to stay ahead of the queue.
Data as Data Products
It is high time we start treating data as products. Organizations that concentrate on improving the quality of their data should consider treating data as products. A product in the development phase goes through a series of processes to ensure the end quality meets the customer’s requirement. Similarly, when you treat data as data products, the quality of the data can be continuously improved and maintained at every step, ensuring better data-driven decisions, improved reliability, and transparency.
Enhanced Real-time ML
Machine Learning has unbelievable advantages in real-time and is becoming one of the core technologies businesses use today. It can process the data in just milliseconds. The more data it consumes, the better its accuracy and predictions. Organizations can take advantage of this powerful tool to gain a hold in the fast-changing market conditions with improved business operations and better understand the customer requirements. Key areas where real-time ML can be a hit are User behavior analysis, manufacturing automation, security on web services, economic management, and Cognitive services like image recognition, processing, and authentication. Overall, it helps to increase the efficiency and scalability of business processes.
Reverse ETL over ETL
Organizations try to get the most out of their data in the data warehouse as the potential information lies there. With reverse ETL, data becomes accessible and visible to all the teams as data engineers work on the data in the warehouse. The processed data is streamed to multiple platforms across the organization, making it easier for the teams to access the data and take a call quickly. As a data engineer, get your hands on reverse ETL as it improves overall efficiency and simplifies your work.
Few benefits you can reap using reverse ETL
- You can easily automate and distribute the data workflow across multiple channels.
- It removes the engineering burden for data engineers.
- Data becomes actionable.
- It helps you improve customer personalization.
SQL as the principal data language
SQL is the most-in-demand skill that every data engineer should pursue to go up the ladder in their career. We know that the widely used data warehouses use SQL as their query language. With its simplicity and organized structure, SQL makes it easier to use and speeds up the data management process. As a modern data engineer, the in and out
knowledge of SQL will help you with the following
- Take the right decision and save a reasonable sum of money for your organization.
- SQL comes in handy in tasks related to data analytics
- Helps to manage a massive volume of structured data
- Data mining is easy with SQL
Data-driven applications with improved speed and agility
Organizations are moving towards automation in all possible ways. We use dashboards to provide information to the user expecting they will take time to analyze and get helpful information. But stakeholders and business leaders look for immediate insights that can help them make critical decisions at the earliest.
In 2022, data-driven apps will take the front seat as they provide a personalized experience and can automate many operational processes when coupled with real-time data. It reduces any form of latency and provides insights as they happen.
Real-time data over slow data
Even valuable data can become junk if you do not use it properly. Organizations must take crucial steps when the data is still hot. We are in a fast-growing technological world where time is one of the key influential factors for an organization’s success. Combining SQL with real-time data engineers can help organizations progress towards automating operational processes and getting insights from real-time data.
Real-time analytics in the cloud
Andreas Kretz says, “From a data engineering standpoint, I currently see a big shift towards real-time analytics in the cloud.”
Businesses concentrate more on real-time data and experiment with tools and software that can provide accurate time analytics to feed their business growth. Data engineers should learn new techniques and tools to integrate, analyze and combine data from various real-time data sources.
ML for data
Using ML functionality on data platforms is trending. It has extensive advantages that you can leverage, like avoiding the need to export huge data to another space after prepping and cleaning, thereby cutting down the need for extra space and saving cost. The data warehouses or databases where you store your data are powerful enough to perform ML tasks and are scalable to meet all your needs. The data can then be streamed to multiple teams or businesspeople via SQL.
Data infrastructure and data integration as a service
As cloud computing is at its pinnacle, Organizations are constantly looking for ways to utilize their cloud space effectively and keep cloud costs under their budget. Public cloud platforms like AWS, Google Cloud, Microsoft Azure, and other third-party vendors provide various tools to manage data on the cloud. Still, organizations need resolute data infrastructure engineers who can track the procurement, integration, and cost-control measures to attain cloud computing benefits.
Data warehouses as the customer data platform
The data warehouse manages all the customer data and collects information from different sources. With the increase of operational analytics, reliable channels bring customer data into marketing systems that can be used in email workflows, targeting campaigns, etc. It avoids the traditional method of separate CDPs. ML and SQL can be combined to get analytics and insights directly from the data warehouses.
