In the beginning, simple systems collected data, wrote data to files, and created reports. For the most part, these systems operated on transaction-based data—bank deposits, sales, telephone calls, and the like. An entire infrastructure supported these essential business systems, but there was little or no place for text. All data was highly and tightly structured, and text was ignored.

This is a guest post by computer scientist Bill Inmon, recognized as the "father of the data warehouse." Bill has written 65 books in nine languages and is currently building a technology called textual ETL. Many years ago, there were no data warehouses. Most Ecommerce retailers relied on legacy systems with unintegrated data that couldn’t communicate with each other, resulting in data silos. Comparing data sets from these systems was almost impossible.

This is a guest post by computer scientist Bill Inmon, recognized as the “father of the data warehouse.” Bill has written 60 books in nine languages and is currently building a technology called textual ETL. Here are five things to know about textual warehouses: Imagine this scene. One day, someone in your ecommerce organization wakes up and can’t locate the information they need to prospect leads or check inventory processes.

The key points you need to know about data warehousing for Ecommerce include: The fate of Ecommerce businesses can rise or fall based on their decision-making and forecasting skills. By tracking the right metrics and KPIs (key performance indicators), Ecommerce stores can determine if their most recent marketing campaign or product launch was a massive success or an underwhelming disappointment—and if so, how should the organization correct its course?

Our main points: For any business, there are multiple advantages of data warehouse integration. For instance, it’s the primary source of Business Intelligence (BI). Some of the most common reasons why an organization chooses to warehouse data include: Integrate.io offers the best ETL, ELT, and blazing fast change data capture (CDC) so your Ecommerce API integrations work for you. Email us at hello@integrate.io to learn more.

When it comes to data management, there are two main options: databases and data warehouses. Both have their unique benefits and drawbacks, so it can be challenging to decide which option is best for your business. This article will discuss the differences between databases and data warehouses and help you decide which option is right for you.

As companies go all in on the cloud to dominate the decade of data, agility, flexibility, and ease of use are critical to success. That’s why we’re so excited to announce ThoughtSpot’s support for Amazon Redshift Serverless which allows customers to leverage the Modern Analytics Cloud to run and scale analytics on Amazon Redshift without having to provision and manage any data warehouse infrastructure.

Nearly a decade after Amazon announced Redshift, its competitor to Apache’s Hadoop, the Hadoop vs. Redshift debate continues. For companies seeking a reliable data warehousing solution, both are worthy contenders, but examining the following aspects will help you decide the winner for yourself.

Since I joined Snowflake, I have been asked multiple times what data warehouse modeling approach Snowflake best supports. Well, the cool thing is that Snowflake supports multiple data modeling approaches equally. Turns out we have a few customers who have existing data warehouses built using a particular approach known as the Data Vault modeling approach, and they have decided to move into Snowflake. So the conversation often goes like this.

Here are seven key differences between data lakes vs data warehouses: A lot of terms get thrown around in the big data space that every business should understand. Many of these terms are easily confused with each other. This is the case with data lakes vs data warehouses. What are some of the most important differences between them, and how can your business use them most effectively for data analytics and data management? Read on to learn the differences between data lakes and data warehouses.