In today’s data-driven world, enterprises rely on data more than ever for everything from operations, customer experience, Artificial Intelligence (AI)/Machine Learning (ML) applications, and Business Intelligence (BI) for informed decision-making. However, the quality of this data is critical, especially when it comes to fueling BI and AI/ML systems.
Applying the “shift left” DevOps approach to Data Quality, forward-thinking enterprise organizations want to deploy Data Quality Checks as early as possible, so they can understand the quality of their raw data at the beginning of the data life cycle in their Object Storage.
You asked, we delivered.
We’re excited to introduce Lightup’s new support for Deep Data Quality Checks on Amazon S3 and Azure Blob Object Storage. This breakthrough capability addresses a crucial need in the market that no other platform has tackled before.
But First, Why Does Data Quality Matter?
Data Quality is the foundation for effective and trustworthy data analytics, business intelligence (BI), and AI/ML products and services. Ensuring that data is accurate and reliable is essential to avoid costly downstream issues.
Accurate Insights
Imagine making critical business decisions based on flawed or incomplete data. Data Quality Checks ensure that the data you analyze is accurate, allowing you to make well-informed decisions.
Mitigating Risks of Costly Mistakes
Faulty data, such as missing key columns or “Nulls” for important attributes, can lead to failed AI/ML training or incorrect analysis. These mistakes can be costly in terms of both time and money.
Since use cases related to ML generate models and subsequent inferences by looking back at historical data, any time-sensitive Data Quality issues that aren’t surfaced early enough can lead to costly delays in deriving critical business insights.
Why Is It So Crucial to Run Data Quality Checks on Object Store Data?
To step in front of the proverbial Data Quality problem, enterprise organizations are “shifting left” to focus on Data Quality testing as early as possible in the data life cycle. And that means, in many cases, deploying Data Quality Checks on raw, unstructured data in Object Storage.
This is especially important for use cases related to Machine Learning, since many enterprises have the right infrastructure and tooling in place to support model building and inferences directly from their Object Store data.
Here’s why.
Early Data Stage
In the modern Cloud ecosystem, Object Stores — such as Amazon S3 or Azure Blob — are typically the first stop for data. Raw data lands here, awaiting further transformations and movement into downstream pipelines and systems. Whether you use a data warehouse, data lake/data lakehouse, or federated query engines, your data life cycle starts in Object Stores.
Cost Efficiency
Data Quality Checks at the Object Store level are very cost-effective, especially for BI and AI/ML use cases. Compared to Data Warehouses that typically have higher compute costs, making changes to data after it’s been loaded in a Data Warehouse or Data Lake/Data Lakehouse is more expensive.
By running Data Quality Checks early in the data life cycle at the Object Store level — where compute costs are much less expensive — enterprises can ultimately:
- Save time, avoiding hours of downstream transformations
- Mitigate the risks of training ML models on faulty data, requiring resource-intensive remediation and retraining
- Reduce data warehouse/data lake/data lakehouse costs in the long run
But, deploying Data Quality Checks directly on Object Stores isn’t an easy task. It requires building a federated query engine and dealing with raw, unstructured data.
That’s where Lightup comes in, addressing this challenge head-on to benefit our customers.
“Shifting Left” with Lightup Data Quality Checks on Amazon S3 and Azure Blob Object Storage
Lightup continuously innovates its Data Quality Monitoring capabilities to meet emerging and complex demands of modern data stacks.
Unlike other platforms, Lightup doesn’t limit data sources to traditional data warehouses, databases, or data lakes/data lakehouses. Leading the charge for continuous innovation to meet evolving customer requirements, Lightup is the first Data Quality Monitoring platform on the market to support time-bound, in-memory Data Quality Checks on Object Store data in Amazon S3 and Azure Blob Storage.
How It Works
Lightup now supports Data Quality Checks on Amazon S3 and Azure Blob Object Storage, enabling you to establish direct connections via access keys or IAM/managed identity. Then, simply define and deploy Deep Data Quality metrics and activate monitors to start tracking the health of your Object Store data.
For more information on how Lightup supports Data Quality Checks on parquet formatted data in Object Stores, please see Lightup Docs: datasources.
Key Benefits
Time Savings
By catching Data Quality issues early, enterprise organizations save time that would otherwise be spent troubleshooting incidents downstream.
Cost Efficiency
Avoid unnecessary compute and storage costs associated with data changes in warehouses.
Streamline Systems
Lightup’s approach enables customers to run Data Quality Checks on raw, unstructured data — otherwise impossible without a homegrown or third-party federated query engine on top of Amazon S3 or Azure Blob.
Data Quality from the Get-Go
Data Quality is non-negotiable in today’s data-driven business landscape. Lightup’s innovative approach to running Data Quality Checks directly on Amazon S3 and Azure Blob is an industry first in the Data Management world. It addresses the urgent need for early Data Quality Checks, saving organizations time, money, and effort.
It’s time to “shift left” and make Data Quality a priority from the very beginning. And Lightup is blazing the way to make Data Quality Monitoring flexible, accessible, and deployable at any and every stage of the modern data life cycle for trusted data and insights.
See the Lightup difference firsthand, start a free trial of Lightup today or book a demo now.
