Migrating analytics workloads to the public cloud has been one of the most significant big data trends in recent years—and it shows no sign of slowing down any time soon. According to a study by IT research company Forrester: Within three years, however, Forrester predicts that the fates will have reversed: Of course, before data can be processed in the public cloud, it has to get there in the first place via data migration.

APIs are powerful for ETL (extract, transform, load) and data integration workflows. API integrations make it possible for the seamless exchange of information between websites, databases, and applications. The Xplenty API allows you and your enterprise to monitor Xplenty clusters and jobs. Through the Xplenty data processing package and Xplenty web application, you can call the Xplenty API to.

Data warehouses fixed one aspect of the data silo problem but introduced another. They function as a large, single source of truth for your organization, but getting insights from this data in a typical Extract, Transform, Load (ETL) data pipeline requires the use of Business Intelligence (BI) and analytics platforms. By the time your data team creates these reports and sends them to other business units, it’s too late for daily decision-making.

While traditional ETL (Extract, Transform, Load) collects data within a centralized data warehouse, reverse ETL flips the target and destination of the standard ETL process. This allows information to be pushed out of data warehouses and into powerful third-party operational systems that can provide better analytics and reporting services. With the Xplenty platform and its ETL tools, all information is sent between warehouses and third-party operational systems in an efficient and secure manner.

If you're looking for a better way to organize your data and ensure it stays up-to-date, you need to start utilizing CDC processes today. Change data capture uses various techniques to detect changes made in source tables and databases in real-time. Read on to learn more about change data capture and how it can be implemented to better serve your business.

ETL Pipeline and Data Pipeline are two concepts growing increasingly important as businesses keep adding applications to their tech stacks. More and more data is moving between systems, and this is where Data and ETL Pipelines play a crucial role. Take a comment on social media, for example. It might be picked up by your tool for social listening and registered in a sentiment analysis app.

Stitch is a popular cloud-based Extract, Load, Transform (ELT) tool. Stitch seamlessly moves data between databases, warehouses, data lakes, SaaS services, and other applications with no code required whatsoever, making it a valuable weapon for data integration. However, the platform has limited data transformation capabilities and, away from its free tier, charges users for the amount of data they use per month, which often works out to be more expensive than other pricing models in the ETL/ELT space.

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.

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?

According to Gartner, leading organizations in every industry are wielding data and analytics as competitive weapons. Companies that leverage data as a competitive differentiator will stand the best chance of acting faster on opportunities and responding to threats in a competitive marketplace. The problem is that most companies aren’t aware of the value of their data. As a result, they aren’t leveraging the full potential of their data to make informed decisions.