Data engineering is the process of building, deploying, and integrating data pipelines to streamline data flow within an enterprise. It is the foundation for business intelligence processes to run and deliver actionable insights. Here, we’ll discuss the top data engineering trends and predictions for 2023.
Data engineering is a growing discipline in the global market. It involves the process of designing and building data pipelines to collect, transform, and transport data to end users (data analysts and data scientists) to derive actionable insights. The data pipelines have to connect all data sources to the central data warehouse or data lake. The success and accuracy of data analytics depend on how well data engineers set up the foundation. This requires high-level data literacy skills.
Unfortunately, there is a gap between the demand and supply of qualified and experienced data engineers in the market. It’s one of the primary reasons many SMBs and large enterprises partner with offshore data engineering companies to adopt advanced data-driven technologies and processes for effective decision-making.
Many experts feel that 2023 will be a vital year for data engineering. In this blog, we’ll take a detailed look at the various big data engineering trends and predictions that will transform the industry at different levels.
Cloud has become a favorite for many businesses around the world. Small, medium, and multinational companies are moving their data and IT infrastructure from on-premises to cloud servers. Data Engineering in AWS (Amazon Warehouse Services), Microsoft Azure, Red Hat, etc., are in high demand. While some companies are directly building data pipelines on the cloud, others are migrating their existing systems to cloud servers.
Another trend is the need for data cloud cost optimization. Top vendors like BigQuery and Snowflake are already talking about ways to optimize the data cloud cost and make cloud services more cost-effective to businesses from various industries and markets.
Financial managers are becoming a part of data teams to ensure that their data engineering strategies and processes will deliver the necessary returns. While there aren’t enough best practices in the industry (data engineering is still in its early stages), data teams are finding ways to overcome the challenges and make their cloud-based data architecture more agile, flexible, scalable, and future-proof. The cost of ownership is also a crucial topic of discussion.
In the current scenario, companies are focusing on using a unified cloud-based data warehouse. For example, AWS data engineering is popular for offering data warehousing services to several business enterprises. However, the same type of database cannot be suitable for all kinds of data workloads.
Experts predict that organizations will shift from data warehouses to data lakes where different databases and tools are individually organized and grouped into a unified setup. This can make the data architecture cost-effective and increase its performance.
Though data engineers are in short supply due to the complexity of the job, data teams will continue to expand and include professionals with more specializations. For example, the data teams will have data engineers, data analysts, data scientists, analytical engineers, etc., to handle different aspects of establishing and using the data architecture in an enterprise.
DevOps managers, finance managers, data reliability engineers, data architects, data product managers, etc., are other specializations we will see in future data teams.
In traditional data pipelines, the metrics layer (also called the semantics layer) is in the middle, between the ETL (extract, transform, load) layer and the cloud data warehouse. It defines the metrics for the values in the data tables and ensures consistency to eliminate errors during business analytics.
Experts predict that the metrics layer will have an addition of a machine learning stack that has its own infrastructure. The ETL layer will continue to do its job, but the data will flow through the machine learning stack, which will help data scientists choose the right metrics for the given data. One day, the metrics layer and the ML stack will be combined to work as a single automated unit.
The concept of data mesh is one of the emerging DE trends discussed by many top companies. This new architectural model is said to help organizations overcome the limitations of traditional data warehouses and centralized data lakes. Date mesh is the decentralization of data governance and ownership. As discussed in the previous trends, domain-specific data platforms, tools, and databases will be established for greater efficiency.
The idea is to build resilient, dynamic, and agile data pipelines that offer more autonomy, interoperability, and control to every member of the data team. However, establishing a data mesh requires more skills and tools. However, centralized data warehouses will continue to exist until enterprises can successfully build and deploy data mesh architecture.
In 2020, a report by Gartner shows that ML models had only a 53% success rate. That too when they were built by companies with strong AI foundations and prior experience. It means even three years ago, only half the machine learning models could be deployed accurately and effectively.
However, the success rate has been increasing over time. Soon, a greater percentage of the ML models can be successfully deployed by organizations. Of course, this will be possible when businesses overcome challenges such as misalignment of needs and objectives, overgeneralization, testing, validating issues, etc.
The architecture for data flow within an enterprise usually combines three different software applications. Databases from different departments (CRM, CDP, etc.) are connected to the data warehouse. The business intelligence and data visualization tools are connected to the other end of the data warehouse. Data flow occurs only in one direction.
However, in modern data engineering, the data flow will occur both ways. The next-gen cloud data architecture will be bi-directional and allow data sync across all applications and tools. Experts predict this trend will be popular for the next decade and more.
Data contracts are similar to SLAs (Service Level Agreements) and are a part of the centralized data architecture. It is an agreement between the service providers and end users (data consumers). It could be a contract within the same company or between two or more organizations. Data contracts make it easy for data teams to streamline the quality of datasets to derive accurate insights.
The discussions on LinkedIn show that it is among the hot topics of the year. While data contracts are still in their early stages in 2023, these will become more widespread by the next year or so.
Though real-time and near-real-time data analytics are already available, not many enterprises have invested in the necessary technology and tools to make it happen. This requires greater investment and professional experience.
However, this is an increase in the adoption rate of frameworks and tools like Apache (Kafka, Flink, etc.) to establish data pipelines that continuously stream data from one application to another. As more organizations adopt AI and ML models, IoT (Internet of Things), and edge computing technologies, real-time data streaming across the systems will become a reality.
Data engineering technologies will be successful when the business overcomes challenges, especially those related to data anomalies. If data engineers can reduce the time taken to identify and solve data anomalies using AI and ML models, they will speed up the resolution rate and increase the accuracy of the insights.
One way to make this happen is by partnering with expert data engineering service providers. They have the necessary experience to help organizations strategize, build, deploy, integrate, and upgrade data architecture on the cloud.
Despite the advancement in technology, data security concerns continue to be a part of Data Engineering trends for 2023. In fact, the increasing use of cloud servers, data centers, IoT devices, etc., indicates the need for stricter control of data access. Many privacy regulations, like GDPR, GDPR, HIPPA, etc., are already in place.
Businesses should have internal data governance policies and adhere to privacy regulations set by authorized bodies. Since data engineering will continue to be vital in the global market, it is important to ensure data security at all levels.
Based on a survey by GlobeNewsWire, 58% of non-tech professionals need to be data savvy and use technology for day-to-day work. Even if the software development team builds a highly complex application, it will be of little use to the enterprise without an easy and under-friendly interface.
Many global vendors are developing no-code tools to help non-tech professionals use advanced technology with minimum or no training. As low-code and no-code technologies grow in the market, data teams can start building their applications from scratch, and that too in less time.
The future of data engineering could see more diversification in data teams where every professional has a specific role. Instead of a single data engineer handling various tasks, each team member will take up a task based on their domain expertise.
New technologies will continue to enter the market and change data architecture. Data engineers have to be ready to handle the changes and adopt the latest technologies without affecting the quality of results. Artificial intelligence and machine learning data discovery will be prominent and provide an edge to businesses over their competitors.
These are the top trends and predictions in data engineering for 2023. Data engineering is a dynamic and ever-changing field. Businesses should continue to upgrade their systems and tools to make effective data-driven decisions and increase revenue.
Data engineers should be well-versed in the latest trends to deliver innovative data solutions to business organizations. SMBs, MSMEs, and large enterprises can contact reputed offshore data engineering and AI service providers to build and deploy agile data architecture in their business.