Why the Need for the Post-Modern Data Stack?
A discussion about the issues developers have when building data pipelines using modern data stack solutions, and what’s needed to overcome those issues.
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Dialing Down the Dollars: Quantify and Control Your Data Costs
Data costs must be tied to data products and the business value they create to make informed decisions based on return on investment.
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Data Pipeline Basics: From Raw Data to Actionable Insights
The traditional method of building and managing pipelines brings many challenges. That’s why forward-thinking teams are pursuing a better way: data pipeline automation.
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Data Pipeline vs ETL: Which Delivers More Value?
Data pipelines are not just an upgrade to ETL processes but a transformative approach that equips businesses to be future-ready.
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The Hidden Challenges of the Modern Data Stack
The modern data stack landscape comes with an integration tax, perpetuates the need for expensive, highly specialized resources, and complicates change management.
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Right-Sizing Security for Small and Emerging Teams
For emerging security teams that don’t need overly complex tools, Sumo Logic Cloud Security Analytics addresses key security challenges without the higher cost or added complexity of enterprise-grade tooling.
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Five Data Pipeline Best Practices to Follow in 2023
Following these data pipeline best practices will help any team deliver data pipelines with the integrity needed to build trust in the data, and do so at scale.
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What Are Intelligent Data Pipelines?
To knock down the barriers to delivering business value from data, organizations need to envision a new type of intelligence in their data pipelines.
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Addressing FinOps and Security Issues with Automated Cloud Operations
Hitachi Ventara’s Taqi Hasan discusses cost management and security challenges enterprises face today and how automated Hitachi Ventara services can help.
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Why Businesses Need Data Reliability Engineering
Hitachi Ventara’s Pratik Dakwala talks data reliability engineering and how it helps businesses be proactive so they can detect data issues before they become critical.
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