Big Data
Complex Volumes, Critical Challenges
See how QuerySurge can automate the
testing of Big Data systems
Big Data, Big Risk
Ensure Your Analytics Are Built on Trusted Data
Discover why validating your Big Data pipelines is essential—and how to eliminate costly data defects before they reach your BI reports.
What Is Big Data?
Big Data refers to vast volumes of information stored on platforms such as Hadoop data lakes and NoSQL data stores.
The 5 Vs define it:
- Volume – Massive quantities of data from diverse sources
- Velocity – Fast data inflows that require real-time or near-real-time processing
- Variety – A mix of structured, semi-structured, and unstructured formats
- Veracity – Trustworthiness of the data
- Value – The actionable insights derived from the data
Why Data Quality Matters
According to IBM, 90% of the world’s data was created in just the past 2 years.
But more data means more risk, primarily when executives rely on Business Intelligence dashboards that often sit on top of bad or misleading data.
Common Data Defects
- Missing or incomplete data
- Incorrect data types or nulls
- Truncation and translation errors
- Duplicate or orphaned records
- Formatting and input inconsistencies
- Logic or transformation gaps
- Numeric precision issues
Without validation, these defects flow unchecked into decision-making tools, putting business performance and regulatory compliance at risk.
Inside the Big Data Architecture
Your data moves through several stages, each introducing potential defects:
In this architecture, Big Data platforms collect data from various sources, including databases, flat files, APIs, and mainframes. Without robust validation along the way, bad data silently propagates downstream (highlighted in red).
Big Data Testing: What Makes It So Challenging?
Big Data testing isn’t just bigger, it’s harder. Traditional database QA methods often fall short.
Top Testing Challenges
- Overwhelming volume of data
- Complex testing across mixed data formats
- Limited effectiveness of traditional SQL-based testing (e.g., Minus Queries)
- Compatibility issues with Hadoop (HQL) and security tools like Kerberos
- Need for specialized test environments (like HDFS, distributed compute)
Big Data testing requires experienced engineers and purpose-built validation tools.
Take Control of Your Data Quality
Don’t let hidden data defects erode your analytics.
Let us show you how to detect issues before they hit your BI reports.