Algorithmic Advancements and New Conceptual Frameworks In Data Engineering
What is Data Engineering? And the Changing Role of a Data Engineer.
Data engineering is a field in which data preparation meant for analysis in the enterprise takes place. In an analytics project, only 20% (if not lesser) of the work is deriving insights from data through data science-based tools and techniques, while the rest 80% is data engineering.
Data engineers are experienced in developing and managing large volumes of data. One of the primary responsibilities of data engineers is to aid data scientists to convert raw data into clean and usable data.
Companies are increasingly adopting the data-driven culture by leveraging the power of data to make successful business decisions and drive transformative technologies. The data science culture has driven a three-time increase in economic growth for leaders partaking in the external sharing of data. Data teams are moving beyond this to find concrete solutions that can transform, manage and track the organisation’s data.
The data analytics industry is dynamic and rapidly evolving. These are some of the changes that data engineers might expect in the next five years. This involves abandoning traditional asynchronous data processing methods in favour of synchronous operations such as automating data pipelines and data warehousing. Essentially, data engineers will build tools and infrastructures that allow for the efficient moving and processing of data using a well-defined framework.
Responsibilities in the Data Engineering Field
Data engineering is often done to provide correct data to the organisation and involves competence in programming languages such as Python, Java, and others.
Simultaneously, data engineers have the following characteristics:
- Support data scientist/analyst
- Manage data
- As generalist, pipeline-centric and database-centric
- They keep evolving
The data engineering sector is rapidly evolving, driven by disruption in the Internet of Things (IoT), artificial intelligence, and machine learning models. To keep up with technological changes, data engineers must continue to evolve and learn new methods in the sector.
- Data Engineering in the Financial Markets
A data engineer in the financial markets is responsible for obtaining data, purifying the data, and addressing errors such as duplication.
The transaction is then automated using the cleared data. There are certainly other ways in which data engineering helps the financial markets. These are Risk management, Predictive analytics, Fraud detection, and Algorithmic trading.
Will Data Engineering Efforts Reduce In The Future?
Data management complexity will continue to increase, which means continued and dedicated attention to data engineering is needed. Data engineering is not purely a technical function; an effective data engineering solution encompasses the integration of people, processes, technology, data and culture.
Business leaders are hoping that in the coming years, artificial intelligence (AI) and machine learning techniques can help reduce the efforts and costs involved in data engineering by accelerating and automating certain data engineering tasks.
A good data engineering solution relies on three main pillars:
- Quality Data
- Mature Processes
- Stable IT Systems
Advancing AI With Data And Machine Learning: What Else Is Needed?
So Get Your Data Strategy Right With Data Engineering
AI Can Help Fill In Gaps In Data Engineering
Machine learning is the one AI discipline that has survived two AI winters and will likely survive the next — not because we have built fantastic algorithms for learning from data, but because we have a lot more data. The few poor algorithms we have can compensate for their shortcomings by using the digital world’s oversupply of datasets, which is constantly rising.
When an algorithm turns “rogue” due to changes in the data or the environment, human intervention (efficient and appropriate) can save us from negative effects. Human intellect is exceptional in its ability to think, comprehend, and adapt to ambiguity and change. However, this is insufficient to build generic or autonomous AI. The best way to make AI robust and resilient is to accept that human involvement is required to compensate for algorithm and data constraints.
That’s Why are data engineering and artificial intelligence mutually beneficial.
Making sense of unstructured data is the process known as data science or data engineering. AI-programmed computers can learn as they go, getting better at solving particular sorts of problems as they accumulate more data. So one cannot exist without the other.
Routine tasks like removing redundant data, completing dataset gaps, and alerting human engineers to anomalies are all areas where AI analytics systems can add value. By handling the labour-intensive tasks that humans don’t want to do anyway, these systems can support dedicated data engineers as they take on difficult problems that will eventually yield greater rewards for the company.
Therefore pairing data engineering efforts with artificial intelligence tools is the ultimate combination required to generate the best insights from the available data.
Data may well be the Achilles Heel of AI, industry observers agree.
Challenges Of Data Engineering
That said, data engineering is far from easy.
One significant barrier is that the infrastructure required to manage the data is expensive and not accessible in most firms.
This is especially true when building models for complex tasks like fraud detection or machine learning. When you factor in the cost of renting hardware from cloud providers, model training and retraining and deployment, it can get very expensive.
Data mining is expensive and time-consuming — with 40% of companies taking longer than a month to deploy a single model into production. Engineers need to spend much time just sifting through the data, building pipelines and doing other tedious tasks.
To make matters worse, data is often unorganised and siloed, which means that it’s hard for teams to collaborate on analysing the data.
Further, this level of complexity means that many less-technical people are at a loss when analysing the data, and they can’t help but be overwhelmed by the sheer volume of information.
The Solution: AI is a tool data engineers can use to make their work easier and enable businesses to achieve a competitive advantage. Automating the tedious aspects of data engineering lets teams quickly and easily build and deploy AI models.
Real-World Examples
No-code AI is being utilised to gain a competitive advantage in a variety of fields, ranging from sales and marketing to finance and cybersecurity.
AI’s advantages include higher productivity, less human error, and cheaper expenses. Data engineers may assist firms in focusing on activities that genuinely move the needle by automating monotonous operations.
Data engineers may help firms gain a competitive advantage by organising and sourcing the data that powers these models.
Future of Data Engineering
As technology evolves and improves, so does data engineering.
According to the study, the data engineering services market is anticipated to grow to USD 77.37 billion by 2023, up from USD 29.50 billion in 2017.
It is predicted as a result of the widespread deployment of big data in recent years. Big data requirements are projected to rise and dominate the industry in the future as technology advances.
Here, I discussed innovations that are far too vital to overlook. Remember that newer technologies will continue to emerge while existing ones, at least some of them, will go away.
The strategy for listing these technologies is based on a basic concept derived from the financial industry — where the world is heading.
Conclusion
Data engineering enables businesses to acquire, store, transform, and categorise data to maximise the value of their AI-ML initiatives by addressing their downstream application sets. Data engineering, in addition to updating the organisation’s data and analytics environment, delivers scalability, robustness, dependability, and best-in-class data management standards. Organisations will undoubtedly need to connect their data management strategy with a specialised engineering team in the future.
But also, the systemic influence of algorithmic biases is frequently highlighted in critiques of AI decision-making systems. In a future where algorithms decide who has access to opportunities and information, discrimination in healthcare, legal protection, and government employment may persist.
Algorithmic innovation is portrayed as a gift to businesses by computer scientists, but it, like all good things, has limitations and hidden costs.
Furthermore, with increasing technological breakthroughs and the necessity for big data processing, the future of data engineering seems promising.
Thanks for reading my article!
Subscribe for free to receive new posts and support my work.
