Challenges of Big Data Architecture
3 min readJan 20, 2020
Big data architecture is the overarching system used to ingest and process enormous amounts of data (often referred to as “big data”) so that it can be analyzed for business purposes. The architecture can be considered the blueprint for a big data solution based on the business needs of an organization. Big data architecture is designed to handle the following types of work: get more on big data through big data online training
- Batch processing of big data sources.
- Real-time processing of big data.
- Predictive analytics and machine learning.
- A well-designed big data architecture can save your company money and help you predict future trends so you can make good business decisions.
Challenges of Big Data Architecture
When done right, a big data architecture can save your company money and help predict important trends, but it is not without its challenges. Be aware of the following issues when working with big data.
Data Quality
- Anytime you are working with diverse data sources, data quality is a challenge.
- This means that you’ll need to do work to ensure that the data formats match and that you don’t have duplicate data or are missing data that would make your analysis unreliable.
- You’ll need to analyze and prepare your data before you can bring it together with other data for analysis.
Scaling
- The value of big data is in its volume. However, this can also become a significant issue. If you have not designed your architecture to scale up, you can quickly run into problems.
- First, the costs of supporting the infrastructure can mount if you don’t plan for them. This can be a burden on your budget.
- And second, if you don’t plan for scaling, your performance can degrade significantly. Both issues should be addressed in the planning phases of building your big data architecture.
Security
- While big data can give you great insights into your data, it’s challenging to protect that data.
- Fraudsters and hackers can be very interested in your data, and they may try to either add their own fake data or skim your data for sensitive information.
- A cybercriminal can fabricate data and introduce it to your data lake. For example, suppose you track website clicks to discover anomalous patterns in traffic and find criminal activity on your site.
- A cybercriminal can penetrate your system, adding noise to the data so that it is impossible to find the criminal activity.
- Conversely, there is a huge volume of sensitive information to be found in your big data, and a cybercriminal could mine your data for that information if you don’t secure the perimeters, encrypt your data, and work to anonymize the data to remove sensitive information.
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