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What is Greenplum Database? Intro to the Big Data Database

Scalegrid

When handling large amounts of complex data, or big data, chances are that your main machine might start getting crushed by all of the data it has to process in order to produce your analytics results. Greenplum features a cost-based query optimizer for large-scale, big data workloads. Query Optimization.

Big Data 321
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What is IT operations analytics? Extract more data insights from more sources

Dynatrace

IT operations analytics (ITOA) with artificial intelligence (AI) capabilities supports faster cloud deployment of digital products and services and trusted business insights. Then, big data analytics technologies, such as Hadoop, NoSQL, Spark, or Grail, the Dynatrace data lakehouse technology, interpret this information.

Analytics 184
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What is a data lakehouse? Combining data lakes and warehouses for the best of both worlds

Dynatrace

While data lakes and data warehousing architectures are commonly used modes for storing and analyzing data, a data lakehouse is an efficient third way to store and analyze data that unifies the two architectures while preserving the benefits of both. What is a data lakehouse? Data warehouses.

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What is APM?

Dynatrace

With answers at your fingertips, data backed decisions, and real-time visibility into business KPIs, Dynatrace enables you to consistently deliver better digital business outcomes across all your channels more efficiently than ever before. Download Magic Quadrant. Dynatrace APM – Named a Leader in APM and yet, we’re much more.

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What is Application Performance Monitoring?

Dynatrace

With answers at your fingertips, data backed decisions, and real-time visibility into business KPIs, Dynatrace enables you to consistently deliver better digital business outcomes across all your channels more efficiently than ever before. Download the 2020 Gartner Magic Quadrant report for APM here.

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Structural Evolutions in Data

O'Reilly

Each time, the underlying implementation changed a bit while still staying true to the larger phenomenon of “Analyzing Data for Fun and Profit.” ” They weren’t quite sure what this “data” substance was, but they’d convinced themselves that they had tons of it that they could monetize.