Starting from version 6.0, Denodo includes a connector for Amazon Redshift.
#REDSHIFT CREATE VIEW HOW TO#
Read more about dynamic masking in our complete guide to dynamic data masking.This document describes how to access Amazon Redshift from the Denodo Platform.Īmazon Redshift is a fast, fully managed, petabyte-scale Data Warehouse solution that makes it simple and cost-effective to efficiently analyze data using business intelligence tools. These reasons make static masking less attractive to data teams nowadays and push for use of dynamic data masking. Even though storage is not very expensive, it still accumulates cost, and sometimes the compute and maintenance cost on each such ETL can grow over time.Ĭhanges to In many cases, introducing new use cases or needs for masking of additional data, or differently creates a project that delays our time-to-value from the data. This sometimes has a high operational cost, especially at scale. In many cases, especially where there are several use cases of the redacted data or a lot of sensitive data to redact, this has more cons than pros:ĭepending on the specific way you create this, it may create delays in the data and a chance of inconsistencies between data sets handled by different teams. INSERT INTO redacted_customers (first_name, last_name, country_code, email ) SELECT sha2 (first_name, 256 ) AS first_name , We will discuss several ways of doing masking of AWS Redshift data, with or without Satori: Another team, such as analysts or data scientists, may get hashed data so they can run statistical predictions and analytics without being exposed to sensitive data, and so on. On the other hand, the accounting teams may get a mirror of that they will get the personal information without the health information. Masking would mean that different teams in the organization may get different redaction levels for the same source data sets.įor example, in a healthcare company, specific medical professionals will receive the medical information about the patients but will not get certain data like the patients’ email addresses, payment cards, or home addresses. When either using Amazon Web Services (AWS) Redshift as a standalone data warehouse solution or as a data processing engine for a data lake, it is commonly used in environments where sensitive data of different types are to be found. This is usually led by the privacy office or legal team. This is to make sure that the organization meets privacy regulations when handling personally identifiable information (PII). The projects are usually initiated by data governance, GRC, or compliance teams. This kind deals with masking projects driven by requirements or recommendations based on specific standards, regulations, and frameworks (such as the NIST Cybersecurity Framework).
This is guided by either the data owners of specific data sets or by other teams, such as those in charge of data governance.Ĭompliance. This kind of data masking is done for business reasons, such as masking of financial data that should not be common knowledge, even within the organization. The main reason here is risk reduction, and according to guidelines set by security teams, to limit the possibility of a sensitive data leak.Ĭommercial.
Data is masked for different reasons, which usually fall into one of the four categories:
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