What's Next Data Engineering Trends in 2023

Digital transformation has led to the continuous generation of massive data. This volume of data has created a need for data engineering services to organize and convert the data into usable formats.

Data engineering makes it possible to design and build data pipelines that can transport and transform raw data into usable formats. Data from multiple sources is collected in a data warehouse or a data lake and represented uniformly. Data engineering services assist in designing, managing, and optimizing this data flow.    

There has been a visible shift in how businesses have started viewing data as an asset, and using this data has been a game changer for business operations. Data is tracked, recorded, leveraged & analyzed to make informed decisions, unlock new business opportunities, and increase business growth. Digital data offers enormous possibilities for organizations to innovate and also serve customers. Organizations that adapted to this trend early on have benefited from it and are ahead of the competition. 

Importance of data engineering

A decade back, data engineering consisted of data warehousing, business intelligence and ETL (Extract, Transform, Load) (tooling to move the data). In the current scenario, data engineering focuses on -

Best practices: Data engineering focuses on best practices in the software development cycle, which includes appropriate use of version control, release management and automated DevOps pipelines.

Security:  Data hacking and data breaches seriously threaten businesses and customers. Data collected by companies must comply with System and Organizational Controls (SOC 2) or Personally Identifiable Information (PII) that the business legally must protect. Data engineers are expected to grasp the security issues related to this issue and take appropriate action in managing risks involved with data in transit and at rest.

Business domain knowledge: It is now essential for data engineers to have domain expertise to draw insights from the data. 

Data Engineering Trends in 2023

Data observability - The term refers to a process and a set of practices designed to help data teams monitor the overall health of data in the organization's IT system. Using these processes and procedures, data management and analytics professionals monitor data quality, reliability and delivery while identifying the issues that may need to be addressed.

Observability is the central tenet of DevOps processes and enables IT, teams to view the systems and identify the cause of performance problems. Data observability provides increased visibility into complex data environments and allows data management teams to create more robust and reliable data sets and pipelines.

Data observability is designed to help ensure healthy data pipelines, high data reliability, timely data delivery, and eliminating any data downtime. 

Data contracts - Data contacts are similar to data delivery contracts or service contracts. Data products have become popular and are widely used, leading to versioning and compatibility management. Applications that access or consume data from other applicants can suffer from the coupling or data interdependence. Changes in the data structure can have a direct impact on other applications. Sometimes small changes can simultaneously lead to adjustments in many applications, and many architects and engineers avoid building coupled architecture.

Data contracts have been designed as a solution to this technical problem. and guarantee interface compatibility that includes the terms of service and service level agreements (SLA). The SLA can detail how the data can be used, for example, only for development, testing, or production. It can also describe the quality of data delivery and interface, including uptime, error rates, availability, version numbers, and roadmaps.

Data cost optimization - As more data workloads move to the cloud, data will become a large portion of a company's cost. There is a transition from rapid growth and revenue acquisitions to a focus on optimizing operations and profitability. Data teams add value to the business by increasing revenue through monetization, but cost optimization will be increasingly important. 

With data engineering teams focused on speed and agility to meet the increasing demands of writing new queries or piping in more data, cost optimization is still the new kid on the block.

Data cloud cost optimization can help data warehouse and data lake vendors as it can stop unnecessary churn of data by increasing consumption from advanced use cases like data applications that can create user value and increase retention. 

Unistore databases - Traditionally, transactional and analytical data were stored and used separately. Databases were designed explicitly for either transactional or analytical data. Unistore unites these two data types in one place and allows the storage and use of these data types in a single platform.

Before Unistore, data to be analyzed had to be extracted from transactional databases and moved into the database designed for analytical purposes. Unistore can do this directly, which gives the benefit of simplicity and performance. It also means that the architecture can be simplified, fewer systems have to be managed, and there is no need to copy and move the data from one database to another.

In conclusion, data engineering is at a unique point where the lines of storage and compute limitations are blurred. It's a rapidly changing field and new developments are constantly emerging. With the increasing adoption of cloud computing, data engineering capabilities will increase, supported by AI and ML technologies. Data engineers can automate and optimize data processes beyond the current standards.