Data Learning Center

This article will guide you through an in-depth understanding of deep learning – its primary attributes, uses, and operational mechanisms. We will then delve into the key differences between machine learning and deep learning.

This article discusses generalization in Machine Learning which plays a pivotal role in facilitating accurate predictions and valuable insights beyond the scope of the training dataset.

This article aims to illuminate the differences between machine learning and statistics, their respective roles in handling datasets, and how these two powerful disciplines can mutually enhance one another.

This article covers training models in machine learning and why it is an essential process that equips machines with the ability to spot patterns, predict outcomes, identify anomalies, and test correlations.

This article defines behavioral analytics, explains how to do it, and why it is important. It also explains what behavioral data consists of and introduces a variety of behavioral analytics tools.

This article defines diagnostic analytics, explains how to use diagnostic analytics in your business, and gives examples of the kinds of questions diagnostic analytics aims to answer.

Data analytics sets up the context and process for analyzing data. Statistics verifies it with rigorous proof. These processes must be well understood in order to efficiently sort, communicate, and validate data-based business decisions.

This article compares data analytics and business analytics, examining the data sources, analytical approaches, and deliverables for each. It also covers how business analytics fits into the data analytics lifecycle.

This article gives an overview of data analytics and explains the different types of data analytics, why data analytics is important, and how it’s different from business intelligence.

This article explores the differences between data analytics and data visualization, and explains the primary data visualization strategies you should use and how they benefit your business.

In this article, we explain the differences between data analytics and data science, and discuss the job roles, skill sets, and responsibilities of both a data analyst and a data scientist.

The data your business collects for analysis and research will fall into one of two categories — quantitative or qualitative. This article explains what quantitative and qualitative data are and how each can be used in your business.

For a successful data analytics project, it’s important to have streamlined processes in place. In this article, we detail the different steps of the data analytics process and give valuable tips to guarantee the success of your data-driven project.

Understanding the role that data analysis has to play in the data analytics lifecycle is vital to developing a healthy data system that produces value from your incoming data.

This article describes the data analytics lifecycle — question discovery, data preparation, model planning, building and executing the model, communicating the results, and operationalizing the process — and why each phase is important.

This article explains the differences between data analytics and business intelligence in terms of scope, temporal focus, and frequency. It discusses the benefits of data analytics and business intelligence and covers some useful BI techniques.

This article defines descriptive analytics, explains how it works and its benefits for your business, and it gives real-world examples of descriptive analytics, such as financial metrics, social media engagement, and web traffic reports.

Google Analytics is a powerful data analytics platform. In this article, we discuss Google Analytics 4, the newest version of Google Analytics, how it can be beneficial to your business, and why you should prioritize migrating to it.

All interactions on your site or app are now recorded as events, as part of the new data model that Google Analytics 4 relies on. This article explains how eCommerce tracking has changed in GA4, compared with UA.

In July 2023, Universal Analytics will stop collecting data, so you need to migrate to Google Analytics 4, the next generation of Google Analytics. In this article, we break GA4 migration down into phases to guide you through your implementation.

Data streams in Google Analytics 4 replace and improve on several features from previous versions of Google Analytics. This article explains what data streams are, how to set them up, and some things you should keep in mind when working with them.

Google Analytics 4 (GA4) has several differences from Universal Analytics. It’s more compliant with privacy regulations, it’s made events more customizable, and it’s spun out some functionality into other products.

The Google Analytics hierarchy is different in Google Analytics 4 (GA4) compared to Universal Analytics. This article explains the changes to the hierarchy in terms of accounts, properties, and data streams, and how user permissions work in GA4.

Google Analytics 4 (GA4) is the next generation of Google's web and app analytics platform, replacing the previous version, Universal Analytics (UA). This article outlines GA4’s benefits and limitations in comparison to its predecessor.

The Google Analytics platform is undergoing some major changes with the introduction of Google Analytics 4 (GA4). This article explains the biggest new features of GA4 and how they will benefit your online business.

Data collection is an essential step in conducting any research or analytics project. This article covers the basics of data collection, types of data and methods used to collect it, and highlights some challenges that may arise during the process.

In this article, we will discuss the importance of data validation, its different types, and how to perform it. By the end, you will have a clear understanding of data validation and be able to perform validation checks on your own data.

Data teams are constantly exploring ways to collect data that can provide insights and enable strategies. In this article, we explore the variety of data collection methods, ranging from traditional approaches to emerging technologies.

This article outlines significant areas to consider and address before, during, and after data collection. It defines the best practice, then explains how to implement them and why this action will help a business's data collection process.

In this article, we will take you on a captivating journey through the history of data, starting from ancient times to the emergence of modern data science and machine learning.

This article will give you an in-depth understanding of Customer 360 including its benefits and applications. You will learn how creating a Customer 360 can help businesses increase profitability and build a positive reputation.

In this article, we will delve into different aspects of obtaining a single customer view and the challenges ahead for data teams in their pursuit of establishing and sustaining this as an invaluable asset for their business.

Identity resolution is the process of combining personal information gathered by a website to generate a complete view of customers for enhanced data analysis.

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.

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.

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.

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.

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).

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 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 (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.

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.

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.

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.

Criminal activity is a constant threat in today's digital world. Cybercriminals only need a few minor details to hack email or company accounts. If successful, they can use this information to do all kinds of damage.

In this article you will learn about the history of data analytics and our future predictions, as we explore major milestones and popular tools used for data-driven decision-making.

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.

Data warehouses and data lakes occupy different positions in the tradeoff between responsiveness, resource costs, and flexibility. In this article, we explore their differences.

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.

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.

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.

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.

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 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.

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.

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.

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.

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.

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.

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.

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.

This article describes how the role of the data engineer has evolved massively with data transformations becoming an essential part of the data engineering process.

This article discusses the different aspects of cloud transformation, details how businesses can build an effective cloud transformation strategy, and explores how the cloud unlocks cloud data transformation capabilities.

This article outlines the most common data transformation techniques, highlights their benefits, and helps you choose the best techniques for you. By mastering these methods, you'll be well-equipped to prepare your data for insightful analysis.

This article describes how data can be transformed into a useful format and integrated into a single repository to enable data-driven decision-making. This is where data transformation with ETL (Extract, Transform, Load) comes in.

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.

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.

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.

This article explains data access controls and why your organization must implement them to safeguard your valuable data and protect you from liability.

Data security is vital to the continuity of your business. This article explains the data security technologies you should implement to protect it.

”Persistent” data is stored on media from which it is accessible for the long term. This article explains persistent data terminology and best practices.

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.

This article explains how consent management enables you to establish trust with your users and ensure your data operations are within regulatory compliance.

PII masking protects sensitive user information by changing data across a database. This article discusses techniques and concepts in PII masking.

Data protection security controls include physical, technical, and administrative controls. This article outlines best practices for security controls.

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.

In this article, we will explore the key aspects of the ETL process as well as the benefits of using ETL, its relation to data warehousing, and how it supports data analytics and machine learning applications.

This article explains the three stages of the ETL process: extraction, transformation, and loading. By following this process, businesses can effectively migrate data to their data repository, facilitating informed decision-making.

The ETL process is integral to a data warehouse, as it is the stage during which data spends the most time being prepared for storage and analysis. This article will outline the ETL process, its importance, and its benefits in a data warehouse.

This article will delve into the significance of utilizing pipelines in business operations. It will differentiate between ETL and data pipelines, and shed light on the advantages that businesses can reap by adopting a pipeline-based approach.

ETL pipeline and ELT pipeline have different benefits and purposes. In this article, we will cover ETL pipelines, how they work, examples, benefits, and how it plays a significant role in the preparation and analysis of data.

This article will explain how to utilize the strengths of both ETL and SQL and how organizations can optimize their data operations by leveraging best practices.

This article will dive deeper into implementing reverse ETL, and by embracing reverse ETL, organizations can ensure that their teams have real-time access to relevant data, fostering agility and driving data-driven success.

In this article, we will delve into the world of Cloud ETL, exploring its benefits, features, and use cases. We will discuss how Cloud ETL enables organizations to handle large volumes of data, integrate data sources, and accelerate data preparation.

In this article, we will delve deeper into the differences between ETL and ELT. Each process has its own unique characteristics and benefits, depending on the business goals and the nature of the data sources.

To establish an efficient ETL process, an organization must undertake a comprehensive evaluation of its available tools and resources, and develop a comprehensive strategy. This article will outline the main steps involved in this process.