Big Data is not just a technology, its a paradigm shift..

WHAT ARE IMPORTANT PROBLEMS/CHALLENGES IN THE FIELD OF BIG DATA?

The biggest problem with true big data (massive, less structured, heterogenous, unwieldy data up to, including and beyond the petabyte range) is that it's incomprehensible to humans at scale. We can't get machines to help us enough. And yet big data keeps getting bigger. So we're drowning in our own ocean of data...

These machines in the cloud without the cleverest human inputs are inarticulate, uncomprehending brutes themselves, even when they're in clusters of thousands and easy to reach out to. And they can amplify noise or errors in the data just as easily as amplify signal or provide insight, which isn't helpful. So what can they help us do?

Google over a decade ago developed a way that Yahoo cloned to spread data out across huge commodity clusters and process simple batch jobs to begin to mine big datasets on an ad-hoc batch basis cost effectively. That method has evolved as Hadoop.

Since then, simpler more and powerful means of distributed analytics have appeared such as Apache Spark (for batch data) and Flink(for streaming data).

Then on the more conventional database front, there are ways to scale analytics using non-relational and modified relational database technologies.

So here as a stack of challenges. Among these challenges are the following:

1. Recognition: identifying what's what in the data.

2. Discovery: efficient ways to find the specific data that can help you.

3. Modeling and simulation: intelligent ways to model the problems big data can solve so human inputs can result in useful outputs.

4. Semantics: effective and efficient ways to contextualize the data so that it's relevant to specific individuals and groups. See Ontology-based Applications

5. Analytics: effective ways to analyze and visualize the results of the data.

6. Storage, streaming and processing: efficient ways to take human inputs and act on batches or streams of big data to be able to extract insights from it.

There are sub-challenges beneath challenges. And each challenge requires its own special level of understanding.

a) Volume: Big data typically involves massive amounts of data that exceed the storage and processing capabilities of traditional systems. Handling and storing such large volumes of data requires scalable infrastructure and distributed computing techniques.

b) Velocity: Big data is often generated and updated at high speeds in real-time or near-real-time. Processing and analyzing data in a timely manner to extract meaningful insights can be challenging, as traditional methods may not be able to keep up with the data influx.

c) Variety: Big data comes in various formats, including structured, semi-structured, and unstructured data. It can include text, images, videos, social media posts, sensor data, and more. Integrating and analyzing diverse data types from multiple sources pose challenges in terms of data integration, data quality, and interoperability.

d) Veracity: Big data can be noisy, incomplete, or contain errors. Ensuring data quality and veracity is crucial to obtain reliable insights. Cleansing and preprocessing the data can be time-consuming and resource-intensive.

e) Complexity: Analyzing big data often requires complex data processing techniques, including machine learning, data mining, and statistical modeling. Implementing and managing these advanced analytical approaches can be challenging, requiring a skilled data science team and specialized tools.

f) Privacy and Security: Big data often includes sensitive and personal information. Protecting data privacy and ensuring data security are critical concerns. Safeguarding data against unauthorized access, breaches, and misuse requires robust security measures and compliance with data protection regulations.

g) Cost: Processing and storing massive volumes of data can be expensive. Big data infrastructure, such as servers, storage systems, and analytical tools, can involve significant upfront and ongoing costs. Managing and optimizing the cost of big data operations is an important consideration.

Overcoming these challenges requires a combination of technical expertise, scalable infrastructure, efficient algorithms, and effective data management strategies. What are your views, ideas, expertise?