Challenges faced in data engineering
Every Organisations and Data engineers should know these.
DATA ENGINEERING
7/20/20254 min read
We had a look at what is Big Data engineering, Big data platforms, Data stores and the best practices to build those. In this article we will look at the general challenges which engineers and organisations face in Big-data world. As the volume, velocity and variety of data along with the technologies and technologies getting evolved at the unexpected rate which is resulting in the one or the other challenges they often face.
Data integration from number of sources:
As the sources of data gets increased and particularly if there are some commonalities between them, then it is big challenge to integrate them to be granular and consistent.
The data may be too much to handle even for the big data platform:
The organisations and the data engineers recently must work with more data than ever before, and there’s no sign of any saturation. More the data, it is better for the organisations for sure, but if it is something which goes beyond the expectations then can create a big problems.
Constant learning for the Data Engineers:
In recent years, i think has become on of the biggest challenges we as data engineers face. As the data is growing so is its storage and processing needs, there are n numbers platforms, processing engines, frameworks, tools etc getting innovated which are forcing data engineers to be on the feet all the time.
Support and Maintenance of the data pipelines:
As the data sources and the type of data increases, the need for the number of data pipelines increases as well. So is the support and maintenance of these pipelines. there, it is utmost important to have a consistent design patters and the automation in place which can ease debugging and maintaining if there is something wrong happens.
Performance and Scalability issues:
As the data increases, the need for analytics, modelling, dashboards, reports etc will increase significantly. This will cause the performance and scalability issues if the right platform and tools are not used. It is highly challenging and time consuming for the infrastructure teams to scale the storage and processing needs. So the data engineering team should be responsible to make the right decisions upfront to avoid any such scenarios.
Data Quality:
The accuracy of the reports, dashboards and models built on the data completely depends on its quality. There are different facets on how the data quality is defined and measured. They are, Completeness, Consistency, Conformity, Accuracy, Integrity and Timeliness. These can be addressed either while ingesting/ETL job or by scheduling the jobs which can check these aspects regular on the data loaded over the time.
Data Governance:
This is one of the most important process to be in place in any data engineering work. The responsible team will make sure there are policies, strategies and compliance regulators are followed appropriately by data engineers. But when the data grows at a rate faster than expected, then these data governance needs may act like an obstacle for the engineers. So to maintain this fine balance is one of the big challenges for sure.
Data security:
As with the Governance, the Security aspect such as data encryption at rest or in transit, access mechanism, data integrity etc are the most important factors to be considered in any data engineering work. But if not handled properly then may create lot of compliance and regulatory issues impacting the reputation of the data stores.
Data accessibility issues:
This is an unusual situation where even though we have data loaded from so many sources, there may be a problems of not getting it when needed. This may be because of an issue with ETL/ELT process or with the wrong access controls put in place.
Unclear Strategy:
The business will thrive on the data when there is a clear strategy between both the data engineering teams and the business. But often it happens that either data engineers are not clear why they are brining some data or business is not clear on what to do with the data which wastes the resources and efforts for no use.
Human element/mistakes:
We may have sophisticated/advanced tools and technologies, may have all the automation possible, but the human element in whole process cannot be avoided at all. And as human is prone to make mistakes, which in data world may cause some irreversible and costly damages.
Resistance To Change:
Some legacy programs and systems persist almost out of comfort. They take the role of a rock in the middle of a rushing river. But in the face of an ever-changing industry, sometimes these systems can pose problems that would be solved with a little software upgrade.
Lack of proper understanding of Massive Data:
Companies fail in their Big Data initiatives, when there is an insufficient understanding. Employees might not know what data is, its storage, processing, importance, and sources. Data professionals may know what’s happening, but others might not have a transparent picture. This can cause lot of data lying in the data stores either totally unused or overused.
Analysis Paralysis problem:
This is a common problem in software engineering world in general, due to the rate at which innovations are happening in the tools and technologies it has become a complicated task to select the right tools for the right job. It is getting even complex in the data world with so many options at hand for the similar tasks. This creates an analysis paralysis problems in the data engineering teams which may delay the development work or make them select a totally wrong tools in the end.
I hope to have covered most of the challenges faced by the data teams in general, please comment if there are any common challenges I have missed. Now that we have looked at more general concepts on the data engineering, next we will start looking at specific topics in the next subsequents articles.
References:
https://www.xenonstack.com/insights/big-data-challenges
https://www.velvetech.com/blog/data-engineering-challenges/.
