A Look at Fast-Approaching Trends of Big Data Engineering In 2023
Data engineering is evolving. The days of storing and statically processing data will soon be gone. It’s time to step into the future of data engineering, so we can change the future with better data-driven decisions.
Prediction articles are clichéd at the beginning of the year, but they have a function. They assist us in stepping outside of the daily grind and thinking about where to lay our longer-term bets.
As we attempt to create a clear “large picture” of a sector that is fast evolving in numerous directions, they also frequently serve as an exercise in perspective. Finding a field where more is expected of its professionals or big data engineers in this case is significant to keep current is essential for everyone.
These potential possibilities become even more significant as data companies evaluate and re-evaluate their objectives in light of an impending crisis, and your data engineering investment can make or break your business’s capacity to remain agile, innovative, and competitive.
Before looping into the predictions for big data engineers in 2023, let’s understand the contemporary data stack, which is:
- Metadata-driven
- Cloud-based
- Customizable and modular
- Runs on SQL (at present)
Keeping these concepts in mind, let’s understand some predictions for this year’s vital big-data engineering trends.
1. There will be more specialization in data team roles
As it stands, big data engineers pipe in the data, analytical engineers clean it up, and data analysts or scientists visualize and analyze it. These roles may come across further segmentation based on the company’s objectives. They may include:
- Data reliability engineers who will make sure the quality of data serves the purpose
- Long-term expenditures that will be prioritized by data architects where Silos will be eliminated.
- DataOps developers will emphasize efficiency and accountability.
This is similar to a situation where software engineers were further divided into related specialties like DevOps engineering or site reliability engineering. As occupations start to develop and grow more complex, it is a natural evolution.
2. Use cases for data lakes and warehouses are beginning to overlap
Data warehouses concentrated on streaming capabilities over the previous year. Pub/Sub connections to data warehouses are now easier than ever thanks to Google’s announcement that streams can now be broadcast directly into BigQuery. Likewise, data lakes like databricks have given stored data more structure and description.
Databricks announced Unity Catalog, a feature that makes it easier for teams to give their data assets structure like metadata. In order to achieve a 10x reduced latency, Snowflake launched Snowpipe streaming and refactored its Kafka connector. As a result, when data enters Snowflake it is immediately queryable.
3. Data anomalies are resolved faster by teams
Over 300 data professionals were polled in 2022, and Wakefield Research found that respondents dedicated 40 percent of their workdays on average to improving the quality of their data. The Wakefield poll also found that organizations suffer 61 events on average each month, with an average detection time of 4 hours and a resolution time of 9 hours. In 2023, we can expect data leaders and big data engineers to reduce the time to detection by switching from static, hard-coded data testing to machine learning-based data monitoring.
This is intriguing, as is the prospect of further developments in automatic root cause analysis. Whether the problem is with code, systems, or the data itself, features like query change detection, segmentation analysis, data lineage, and more can help reduce the number of potential causes of “why is the data inaccurate” from an infinite number to a manageable number.
4. Data engineers will invest more time in Data Cloud Cost Optimization
As more data workloads migrate to the cloud, data will account for a higher amount of a company’s spending and attract increased financial scrutiny. It goes without saying that the macroeconomic environment is beginning to shift from a phase of quick development and revenue acquisition to a more limited focus on maximizing operations and profitability. Financial officials are more involved in negotiations with data teams; therefore it makes sense that this cooperation will also have ongoing expenses.
The primary ways that data teams will continue to bring value to the business are through boosting the productivity of other teams and boosting revenue through data monetization, but a third, more crucial area of focus will be cost management.
Because data engineering teams have concentrated on agility and speed to meet the extraordinarily high demands placed on them, best practices in this field are still quite new. Instead of optimizing complex or degrading queries, they spend the majority of their time developing new queries or feeding in more data.
5. Platforms remain central, even as data becomes meshier
Data mesh has been one of the most popular ideas among data teams for a while now. Creating a data economy that is used case-driven, is a notion that aids businesses in directing the use of decentralized information. Organizations that utilize data mesh treat data as a component of different teams’ “domains,” which are to be maintained individually. This is in contrast to organizations that consolidate their data architecture into a single warehouse or data lake.
Data teams are increasingly combining domain-embedded teams with a center of excellence or platform team. This organizing approach allows many teams to combine the agility and alignment of decentralized teams with the uniform standards of centralized teams, giving them the best of both worlds.
While some teams will make this halt a temporary stop on their data mesh journey, others will carry on. Although they will keep a potent central platform and data engineering SWAT team, they will implement data mesh principles including self-service, domain-first architectures, and treating data like a product.
Lastly…
As 2023 has approached, big data may be as huge as it wants to be because storage and computing constraints have mostly been eliminated. This presents a great opportunity for data engineering.
As a result, the most popular trends for this year will focus less on scaling or optimizing infrastructure and more on methods for improving the organization, dependability, and accessibility of this expanded universe. This inevitably increases the industry’s growth potential and the demand for data engineers in the labor market.
