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Unlock The Power of Your Data With These 19 Big Data & Data Analytics Books

datapine

With that in mind, we have prepared a list of the top 19 definitive data analytics and big data books, along with magazines and authentic readers’ reviews upvoted by the Goodreads community. Essential Big Data And Data Analytics Insights. Discover The Best Data Analytics And Big Data Books Of All Time.

Big Data 263
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Data Modeling 201 for the cloud: designing databases for data warehouses

erwin

Designing databases for data warehouses or data marts is intrinsically much different than designing for traditional OLTP systems. Accordingly, data modelers must embrace some new tricks when designing data warehouses and data marts. Figure 1: Pricing for a 4 TB data warehouse in AWS.

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Reduce Risk, Increase Value: Why Equity Teams Need Automated Document Management

Jet Global

Without automated document management, you may find yourself falling victim to: Increased Risk of Errors : Manual handling of documents and data increases the risk of errors. Increased Security Risks : Document management features often include security measures to protect sensitive information.

Risk 52
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Combine transactional, streaming, and third-party data on Amazon Redshift for financial services

AWS Big Data

Amazon Redshift features like streaming ingestion, Amazon Aurora zero-ETL integration , and data sharing with AWS Data Exchange enable near-real-time processing for trade reporting, risk management, and trade optimization. This will be your OLTP data store for transactional data. version cluster. version cluster.

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Laminar Adds Support for SaaS and BigQuery Data Protection

Laminar Security

To effectively protect sensitive data in the cloud, cyber security personnel must ensure comprehensive coverage across all their environments; wherever data travels, including cloud service providers (CSPs), data warehouses, and software-as-a-service (SaaS) applications.

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How to Build a Performant Data Warehouse in Redshift

Sisense

This blog is intended to give an overview of the considerations you’ll want to make as you build your Redshift data warehouse to ensure you are getting the optimal performance. This results in less joins between the metric data in fact tables, and the dimensions. So let’s dive in! OLTP vs OLAP.

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The Benefits, Challenges and Risks of Predictive Analytics for Your Application

Jet Global

But we’re also seeing its use expand in other industries, like Financial Services applications for credit risk assessment or Human Resources applications to identify employee trends. Analysts can use predictive analytics to foresee if a change will help them reduce risks, improve operations, and/or increase revenue.