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Data quality for vector databases

Data quality for vector databases

Vector databases drive AI applications by storing high-dimensional data, but issues like incorrect metadata, inconsistent timestamps, and…
Go to the profile of Max Lukichev
Max Lukichev
A Rundown of Telmai’s Recent Key Product Feature Updates

A Rundown of Telmai’s Recent Key Product Feature Updates

Ensuring the quality and integrity of your data isn’t just a technical requirement — it’s a business imperative. As modern data…
Go to the profile of Anoop Gopalam
Anoop Gopalam
Unpacking Data Quality In The Age Of AI

Unpacking Data Quality In The Age Of AI

Learn why data quality in AI is key to business success and operational reliability.
Go to the profile of Anoop Gopalam
Anoop Gopalam
Telmai continues to lead the market in the 2024 GigaOm Radar for Data Observability

Telmai continues to lead the market in the 2024 GigaOm Radar for Data Observability

Telmai has once again been recognized as a leader in the 2024 GigaOm Radar Report for Data Observability, marking its second consecutive…
Go to the profile of Anoop Gopalam
Anoop Gopalam
Ensuring Data Completeness: Building Trust in Your Data

Ensuring Data Completeness: Building Trust in Your Data

Similar to a guest who forgets to bring their share to a potluck, incomplete data can leave everyone dissatisfied and scrambling to fill…
Go to the profile of Anoop Gopalam
Anoop Gopalam
Data Quality
Is Your Data AI Ready?

Is Your Data AI Ready?

In recent years, the emergence of new data-driven technologies has often been met with great enthusiasm. These technologies initially…
Go to the profile of Mona Rakibe
Mona Rakibe
Why Sampling Falls Short in Ensuring Reliable Data Systems

Why Sampling Falls Short in Ensuring Reliable Data Systems

Performing data quality checks on a sample of your dataset can miss critical anomalies and cause costly delays. Discover how full-fidelity…
Go to the profile of Max Lukichev
Max Lukichev
Understanding data quality’s impact on Large Language Models

Understanding data quality’s impact on Large Language Models

In this experiment, we introduce errors into data for fine-tuning an LLM and discuss strategies for ensuring high-quality data.
Go to the profile of Max Lukichev
Max Lukichev
Data Architecture
Data Pipeline Testing: Essential Approaches, Tools, and Tips

Data Pipeline Testing: Essential Approaches, Tools, and Tips

Learn effective Data Pipeline testing methods with our guide on approaches, tools, & tips.
Go to the profile of Max Lukichev
Max Lukichev
How to Avoid The Hidden Costs of Bad Data in Your Data Lakehouse

How to Avoid The Hidden Costs of Bad Data in Your Data Lakehouse

One key area for organizations is controlling exploding data infrastructure costs. With the power and scale of a modern lakehouse, this is…
Go to the profile of Max Lukichev
Max Lukichev
The 12 Data Pipeline Metrics That Matter Most

The 12 Data Pipeline Metrics That Matter Most

Your data pipeline’s strength is in its metrics: performance, quality, and efficiency. Track key factors like throughput…
Go to the profile of Anoop Gopalam
Anoop Gopalam
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