Big Data Architecture, Challenges and Benefits!!

Big data architecture is the predominant system used to ingest and process large amounts of data (often referred to as "big data") so it can be analysed for business purposes. Big data architecture is designed to handle the following types of work:

  • Batch processing of big data sources.
  • Real-time big data ingestion.
  • Predictive analytics and machine learning.

How Big Data Architecture Benefits Companies?

The volume of data growing daily in exponential form and is available for analysis. And, there are number streaming sources, including the data from traffic sensors, health sector’s sensors, transaction logs, and activity logs. Using a big data architecture can help your business save money and make critical decisions, including:

  • Reducing costs. Big data technologies such as Hadoop and cloud-based analytics can significantly reduce costs when it comes to storing large amounts of data.
  • Making faster, better decisions. Using the streaming component of big data architecture, you can make decisions in real-time.
  • Predicting future needs and creating new products. Big data can help you to gauge customer needs and predict future trends using analytics.

What are the main 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 major 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 analyse 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 cyber-criminal 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 cyber-criminal 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 cyber-criminal could mine your data for that information if you don't secure the perimeters, encrypt your data, and work to anonymity the data to remove sensitive information.

 


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