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Data lakehouse innovations advance the three pillars of observability for more collaborative analytics

Dynatrace

The goal is to turn more data into insights so the whole organization can make data-driven decisions and automate processes. Grail data lakehouse delivers massively parallel processing for answers at scale Modern cloud-native computing is constantly upping the ante on data volume, variety, and velocity.

Analytics 195
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What is software automation? Optimize the software lifecycle with intelligent automation

Dynatrace

In what follows, we define software automation as well as software analytics and outline their importance. What is software analytics? This involves big data analytics and applying advanced AI and machine learning techniques, such as causal AI. We also discuss the role of AI for IT operations (AIOps) and more.

Software 196
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What is a data lakehouse? Combining data lakes and warehouses for the best of both worlds

Dynatrace

Therefore, it contains all of an organization’s data. Generally, the storage technology categorizes data into landing, raw, and curated zones depending on its consumption readiness. In a data lakehouse model, organizations first migrate data from sources into a data lake. Data management.

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In-Stream Big Data Processing

Highly Scalable

The shortcomings and drawbacks of batch-oriented data processing were widely recognized by the Big Data community quite a long time ago. In addition, we survey the current and emerging technologies and provide a few implementation tips. Towards Unified Big Data Processing.

Big Data 154
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How Our Paths Brought Us to Data and Netflix

The Netflix TechBlog

Part of our series on who works in Analytics at Netflix?—?and and what the role entails by Julie Beckley & Chris Pham This Q&A provides insights into the diverse set of skills, projects, and culture within Data Science and Engineering (DSE) at Netflix through the eyes of two team members: Chris Pham and Julie Beckley.

Analytics 223
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The Need for Real-Time Device Tracking

ScaleOut Software

Real-Time Device Tracking with In-Memory Computing Can Fill an Important Gap in Today’s Streaming Analytics Platforms. The Limitations of Today’s Streaming Analytics. How are we managing the torrent of telemetry that flows into analytics systems from these devices? The list goes on.

IoT 78
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Using Real-Time Digital Twins for Aggregate Analytics

ScaleOut Software

When analyzing telemetry from a large population of data sources, such as a fleet of rental cars or IoT devices in “smart cities” deployments, it’s difficult if not impossible for conventional streaming analytics platforms to track the behavior of each individual data source and derive actionable information in real time.