- A top-level guide to data lakes
- Redshift vs Snowflake vs BigQuery: Choosing a Warehouse
- What Is a Data Warehouse?
- How to Create and Use Business Intelligence with a Data Warehouse
- Best Practices for Accessing Your Data Warehouse
- Data Warehouse Architecture
- Data Warehouse Best Practices — preparing your data for peak performance
- How do Data Warehouses Enhance Data Mining?
- Data Warehouses versus Databases: What’s the Difference?
- What are the Benefits of a Data Warehouse?
- Key Concepts of a Data Warehouse
- Data Warehouses versus Data Lakes
- Data Warehouses versus Data Marts
- How to Move Data in Data Warehouses
- Difference Between Big Data and Data Warehouses
- What is Identity Resolution?
- What Is Customer Data?
- Customer Data Analytics
- Customer Data Management
- Collecting Customer Data
- The Importance of First-Party Customer Data After iOS Updates
- CDPs vs. DMPs
- Types of Customer Data
- What Is a Customer Data Platform?
- What is an Identity Graph?
- Customer Data Protection
- A complete guide to first-party customer data
- What is Consent Management?
- What is Persistent Data?
- Data Access Control
- Data Sharing and Third Parties
- Cybersecurity Frameworks
- What is PII Masking and How Can You Use It?
- Data Security Strategies
- Data Security Technologies
- Data Protection Security Controls
- How to Manage Data Retention
- How To Handle Your Company’s Sensitive Data
- Data Security Best Practices For Companies
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A top-level guide to data lakes
In this article, we’ll cover everything you need to know about data lakes. You’ll learn, what is a data lake? How is a data lake different from a data warehouse? Benefits of data lake.
Redshift vs Snowflake vs BigQuery: Choosing a Warehouse
When it comes to choosing a data warehouse for the open-source analytics stack, it is very common to pit data warehouses against each other. Redshift vs BigQuery, Redshift vs Snowflake, etc. are some common examples.
What Is a Data Warehouse?
Data warehouses offer a wide range of benefits. In this article, you’ll learn about the benefits and features of a data warehouse and how to effectively implement, use, and maintain a data warehouse.
How to Create and Use Business Intelligence with a Data Warehouse
In this article, we cover what business intelligence (BI) is, which roles within the company work with it, and explain BI's relationship to the data warehouse.
Best Practices for Accessing Your Data Warehouse
In this article, you will learn how data is stored in a data warehouse, how it’s accessed, best practices for writing SQL queries, who the major cloud data warehouse vendors are, and the various types of data storage models available.
Data Warehouse Architecture
Data warehouses need to be architected in a way that maximizes flexibility and maintains speed. In this article, you'll learn about data warehouse architectures, as well as what you should consider when setting up your own data warehouse.
Data Warehouse Best Practices — preparing your data for peak performance
One of the best modern tools for maximizing the observability and analytic power of your data is the data warehouse. This article will guide you through data warehouse best practices and illustrate how to get the most value from your data warehouse.
How do Data Warehouses Enhance Data Mining?
Everyone’s saying it more and more, and in increasingly tired metaphors: data is a new currency. This article covers key ways in which data warehouses enhance the data mining process.
Data Warehouses versus Databases: What’s the Difference?
Understanding the sometimes-subtle differences between types of data infrastructure can be challenging. This article will explain the differences and the best usages of a data warehouse as opposed to a database.
What are the Benefits of a Data Warehouse?
A data warehouse is a software construct that pulls data from different sources into a single target for business intelligence analysis and support for strategic decisions. In this article, we examine the benefits of implementing a data warehouse.
Key Concepts of a Data Warehouse
This article covers key concepts of the data warehouse, comparing its capabilities with that of relational databases, data marts, and data lakes — all common solutions to the challenges of consuming large, varied types of data.
Data Warehouses versus Data Lakes
Data warehouses and data lakes occupy different positions in the tradeoff between responsiveness, resource costs, and flexibility. In this article, we explore their differences.
Data Warehouses versus Data Marts
In the worlds of business intelligence and outcome modeling, the terms data warehouse and data mart are often used interchangeably. The differences are worth knowing, though, so in this article we’ll compare and contrast the two.
How to Move Data in Data Warehouses
In this article, we’ll explore the various ways that data enters, moves through, and exits warehouses. You’ll also learn how a data warehouse strategy helps businesses understand their current position and set benchmarks to drive long-term growth.
Difference Between Big Data and Data Warehouses
Although terms “big data” and “data warehouse” are often compared directly, there is a categorical difference between them. We will cover the difference in this article.
What is Identity Resolution?
Identity resolution is the process of combining personal information gathered by a website to generate a complete view of customers for enhanced data analysis.
What Is Customer Data?
This article gives a definition of customer data, as well as explaining the different types of customer data, how customer data is collected, why it is important, and whether collecting customer data is legal.
Customer Data Analytics
Customer data analytics is generating inferences from data that’s coming directly from your customers, or is inferred through their actions in your SaaS product, website, and campaigns. This article helps you choose the right processes for the job.
Customer Data Management
Customer data management (sometimes known as “customer database management”) is a common umbrella term for the strategies a company uses to collect, control and use its customer data.
Collecting Customer Data
It’s no secret that data has become the most precious commodity to today’s leading businesses. When properly mined, data can enable powerfully effective marketing and game-changing growth strategies.
The Importance of First-Party Customer Data After iOS Updates
This post highlights importance of first-party data after iOS updates and how this data is superior to third-party data while helping your achieve better results.
CDPs vs. DMPs
Customer data management has recently undergone changes with the tools used to handle customer data. This article will help illustrate the difference between two of these tools — customer data platforms (CDPs) and data management platforms (DMPs).
Types of Customer Data
Customer data is both a valuable business asset that can be used for marketing and business growth, as well as a sensitive source of information about individuals that must be protected.
What Is a Customer Data Platform?
This article defines a customer data platform (CDP), and explains the different types of CDPs. It also weighs the pros and cons of buying software vs building your own.
What is an Identity Graph?
Identity graphs are used to filter redundant customer data and connect potentially billions of points in a complex, scalable web of people and behavior.
Customer Data Protection
Customer data protection is vital for avoiding PII data breaches and their legal and reputational consequences. In this article, find out how to protect your customer data.
A complete guide to first-party customer data
With Google and Apple killing the ad cookie in an effort to improve data privacy, one thing is clear: third-party data access is on its way out. Learn more about how RudderStack can help your company get started with your first-party data journey.
What is Consent Management?
This article explains how consent management enables you to establish trust with your users and ensure your data operations are within regulatory compliance.
What is Persistent Data?
”Persistent” data is stored on media from which it is accessible for the long term. This article explains persistent data terminology and best practices.
Data Access Control
This article explains data access controls and why your organization must implement them to safeguard your valuable data and protect you from liability.
Data Sharing and Third Parties
Third-party data is important for building market insights and requires careful handling. This article explains third-party data terminology and best practices.
A cybersecurity framework will help you prevent cyberattacks and protect your data and reputation. Here’s a guide to the most common cybersecurity frameworks.
What is PII Masking and How Can You Use It?
PII masking protects sensitive user information by changing data across a database. This article discusses techniques and concepts in PII masking.
Data Security Strategies
A data protection strategy can avoid costly data breaches and protect your reputation and business assets. Learn the key components of a data security strategy.
Data Security Technologies
Data security is vital to the continuity of your business. This article explains the data security technologies you should implement to protect it.
Data Protection Security Controls
Data protection security controls include physical, technical, and administrative controls. This article outlines best practices for security controls.
How to Manage Data Retention
In this article, you’ll learn more about what data retention is and why a data retention policy is valuable to your organization. You’ll also learn some of the core ideas behind data retention policies and some best practices for creating your own.
How To Handle Your Company’s Sensitive Data
Damages from sensitive data loss can be devastating for companies. It can destroy customer trust and lead to financial and legal consequences. In this article, you will learn about sensitive data within a company and how best to protect it.
Data Security Best Practices For Companies
The viability of modern companies is rooted in data security. This article will explain the data security best practices you must implement in your business.
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