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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 246
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What is Greenplum Database? Intro to the Big Data Database

Scalegrid

It can scale towards a multi-petabyte level data workload without a single issue, and it allows access to a cluster of powerful servers that will work together within a single SQL interface where you can view all of the data. At a glance – TLDR. The Greenplum Architecture. Greenplum Advantages. Major Use Cases.

Big Data 321
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Netflix’s Distributed Counter Abstraction

The Netflix TechBlog

By: Rajiv Shringi , Oleksii Tkachuk , Kartik Sathyanarayanan Introduction In our previous blog post, we introduced Netflix’s TimeSeries Abstraction , a distributed service designed to store and query large volumes of temporal event data with low millisecond latencies. Today, we’re excited to present the Distributed Counter Abstraction.

Latency 251
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Introducing Netflix’s Key-Value Data Abstraction Layer

The Netflix TechBlog

Second, developers had to constantly re-learn new data modeling practices and common yet critical data access patterns. To overcome these challenges, we developed a holistic approach that builds upon our Data Gateway Platform. Data Model At its core, the KV abstraction is built around a two-level map architecture.

Latency 260
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Spring Boot and Time Series Data in ScyllaDB

DZone

The following tutorial walks you through how to use Spring Boot apps with ScyllaDB for time series data, taking advantage of shard-aware drivers and prepared statements. ScyllaDB is used to store the stock price (time series data). And you’ll learn how ScyllaDB can be used to store time series data.

Storage 285
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Introducing Netflix TimeSeries Data Abstraction Layer

The Netflix TechBlog

Rajiv Shringi Vinay Chella Kaidan Fullerton Oleksii Tkachuk Joey Lynch Introduction As Netflix continues to expand and diversify into various sectors like Video on Demand and Gaming , the ability to ingest and store vast amounts of temporal data — often reaching petabytes — with millisecond access latency has become increasingly vital.

Latency 239
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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.