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Data is at the center of many challenges in system design today. Difficult issues such as scalability, consistency, reliability, efficiency, and maintainability need to be resolved. In addition, there's an overwhelming variety of systems, including relational databases, NoSQL datastores, data warehouses, and data lakes. There are cloud services, on-premises services, and embedded databases. What
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Apache Kafka 1.0 Cookbook

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Academic book turns into a popular book read.Artificial, linkage and conversion analysis. One of the threeis demonstrated to be the most important.30 day tracking visitor period versus 24 hour period that Ad Firms stipulate. What can happen in the next 29 days is demonstrated.Find out real hidden characteristics only artificial visitors have and learn how to combat them.Inventory of clicks of how ... Read more

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As data science evolves to become a business necessity, the importance of assembling a strong and innovative data teams grows. In this in-depth report, data scientist DJ Patil explains the skills, perspectives, tools and processes that position data science teams for success.Topics include: What it means to be "data driven." The unique roles of data scientists. The four essential qualities of data
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Oracle RMAN Pocket Reference

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Causal Inference in Python

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by Matheus Facure ...

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Programming eBooks

Designing Data-Intensive Applications

Supercharge Excel

AI Systems Performance Engineering

Apache Kafka 1.0 Cookbook

Absolute Beginner (Part 1) Selenium WebDriver for Functional Automation Testing

Data Jujitsu: The Art of Turning Data into Product

What Is Data Science?

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The Age of AI

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Secret Techniques for Evaluating (Cpc) Cost Per Click Advertising: Google Adwords and Yahoo Overture Action Research Evaluation

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Python: Deeper Insights into Machine Learning

Causal Inference in Python