After migrating a Data Warehouse to Google Cloud BigQuery, ETL and Business Intelligence developers are often tasked with upgrading and enhancing data pipelines, reports and dashboards. Data teams who are familiar with SQL Server Integration Services (SSIS) and SQL Server Reporting Services (SSRS) are able to continue to use these tools with BigQuery, allowing them to modernize ETL pipelines and BI platforms after an initial data migration is complete.

For our latest specialist interview in our series speaking to technology leaders from around the world, we’ve welcomed Charles Denyer. Charles is an Austin-based cybersecurity and national security expert who has worked with hundreds of US and international organizations. He is a founding member and senior partner in two consulting and compliance firms.

After the closure of the influential Epic v. Apple trial, a California judge has ordered Apple to immediately remove its anti-steering rules, which barred app developers from offering payment methods outside of Apple’s in-app purchasing system.

In today’s 24/7 digital world, real-time data is a necessity to stay relevant for today’s businesses. Companies who wish to remain competitive must be able to quickly respond to customer demand and adjust to market changes. Supplying business leaders with real-time information for informed decision-making can be a challenge with information spread among disparate systems.

The amount of big data that enterprises churn out is simply staggering. All this information is worthless unless organizations unlock its true value for analytics. This is where ETL proves useful. Traditional ETL (extract, transform, and load) remains the most popular method for moving data from point A to point Z. It takes disparate data sets from multiple sources, transforming that data to the correct format and loading it into a final destination like a data warehouse.

Leveraging the Internet of Things (IoT) allows you to improve processes and take your business in new directions. But it requires you to live on the edge. That’s where you find the ability to empower IoT devices to respond to events in real time by capturing and analyzing the relevant data.

Analysts have found that roughly 50% or more enterprise resource planning (ERP) projects fail, and approximately half of all projects are considered challenging.

ETL and ELT refer to two patterns of data storage architecture within your data pipelines. The letters in both acronyms stand for: So both ETL (extract, transform, load) and ELT (extract, load, transform) processes help you collect data, transform it into a usable form and save it to permanent storage, where it can be accessed by data scientists and analysts to extract insights from the data. What is the difference?

Data is the new oil. It’s a phrase we’ve heard a lot in recent years, and it’s not hard to understand why. We’re generating more data every day than ever before, and companies are scrambling to find ways to store that information without running out of space.