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Privacy spotlight: Control compliance in Dynatrace with multiple layers of sensitive data masking

Dynatrace

Observing complex environments involves handling regulatory, compliance, and data governance requirements. This continuously evolving landscape requires careful management and clarity regarding how sensitive data is used. This is particularly important when dealing with large volumes of data.

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Storage handling improvements increase retention of transaction data for Dynatrace Managed

Dynatrace

Using existing storage resources optimally is key to being able to capture the right data over time. In this blog post, we announce: Compression of transaction data that’s older than three days. Improvements to Adaptive Data Retention. Transaction-data compression for Dynatrace Managed environments.

Storage 196
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MySQL General Tablespaces: A Powerful Storage Option for Your Data

Percona

Managing storage and performance efficiently in your MySQL database is crucial, and general tablespaces offer flexibility in achieving this. This blog discusses general tablespaces and explores their functionalities, benefits, and practical usage, along with illustrative examples. What are MySQL general tablespaces?

Storage 89
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Storage Strategies for PostgreSQL on Kubernetes

Percona

There are a wealth of options on how you can approach storage configuration in Percona Operator for PostgreSQL , and in this blog post, we review various storage strategies — from basics to more sophisticated use cases. For example, you can choose the public cloud storage type – gp3, io2, etc, or set file system.

Storage 105
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Enhance data management with Grail: Ultimate guide to custom buckets and security policies

Dynatrace

Grail: Enterprise-ready data lakehouse Grail, the Dynatrace causational data lakehouse, was explicitly designed for observability and security data, with artificial intelligence integrated into its foundation. Tables are a physical data model, essentially the type of observability data that you can store.

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Privacy spotlight: Retain data in Grail with 1-day precision, for up to 10 years

Dynatrace

Streamline privacy requirements with flexible retention periods Data retention is a critical aspect of data handling, and it’s not just about privacy compliance—it’s about having the flexibility to optimize data storage times in Grail for your Dynatrace use cases.

Storage 165
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The history of Grail: Why you need a data lakehouse

Dynatrace

Some time ago, at a restaurant near Boston, three Dynatrace colleagues dined and discussed the growing data challenge for enterprises. At its core, this challenge involves a rapid increase in the amount—and complexity—of data collected within a company. Work with different and independent data types. Thus, Grail was born.