Image Metadata Manipulation Using CloverETL

Figure 1: Main Graph image for processing, reading, writing metadata to image files

Time and again, CloverETL has proven it can solve some pretty interesting problems. Have you ever though about image metadata manipulation? Turns out, there’s a ton of information contained within the metadata of images that people and systems find useful. For example, companies such as Facebook and Twitter strip the copyright information from image metadata that are uploaded to their servers. Imagine you're a photographer selling your pictures for a living. It probably would upset you if someone else took ownership of them, right?[Continue reading]

Moving Data to Amazon Redshift

Uploading data to Redshift is easy with CloverETL.

As you’ve probably experienced, cloud computing has become incredibly important. Today, we’ll show you how to easily feed data to the data warehouse service Amazon Redshift using CloverETL. We’ll create transformations that will read data from the source and upload them to Redshift. Luckily, this will work with any data source. With the help of CloverETL, you’ll able to deliver data to Redshift with very little effort and in matter of minutes. This is what we call rapid data integration.[Continue reading]

Getting Data From NetSuite

netsuite-fig-1

CloverETL is a powerful tool that can help you to work with data from different sources. Today we take a look on how CloverETL can help you getting data from NetSuite. Although NetSuite is a powerful and extensive platform, there are cases when you need to use your data as part of a broader mix somewhere outside of it. Let's say you're combining several data sources – NetSuite being one of them – into a single reporting database. However, this might get tricky, especially in the case of cloud-hosted services. To do the job, you’ll need some ETL tool, like CloverETL, and specific cloud connectors to all the data sources.[Continue reading]

Execution View – Helping you with Complex Graphs

pic 8 - recalled job

As data keep getting bigger and more complex, so do the graph and workflows used for processing these data––especially when they are nested within each other. While also granting users much more sophisticated options and functionalities with their graphs, subgraphs also added another layer of complexity for debugging graphs. It is easy to get lost in a complex graph; analyzing a long text log can be very difficult and time consuming to search for potential problems and correct them.[Continue reading]