As analytics software vendors, we need to do something profoundly different to improve how business users access, consume, and interact with data.

When doing data matching with large sets of data, consideration should be given to the combinations that can be generated, and it’s associated effects on performance. This has an effect when using Talend’s Data Integration Matching and Data Quality components. Matching routines do not scale in a linear fashion.

Ever find yourself in your closet searching for an article of clothing or trying to squeeze one more hangers on the rack? I took the better part of the afternoon on Saturday to clean out my closet. It made me realize a few key things. I am a pack rat and this clean up exercise took way longer than I thought. I anticipated the exercise taking 30-45 minutes tops. How hard could it be? What I discovered 3 hours later is I keep too many unnecessary items.

We know that data is a key driver of success in today data-driven world. Often, companies struggle to efficiently integrate and process enterprise data for fast and reliable analytics, due to reliance on legacy ETL solutions and data silos. To solve this problem, companies are adopting cloud platforms like Microsoft Azure to modernize their IT infrastructure.

Last year, Yellowfin changed the world of business intelligence and analytics with the introduction of Yellowfin Signals. We automated data analysis, removed dashboard dependency, and sped up the process where you would get alerted to the most important changes in your business data, just ask AeroEdge.

A common perspective that I see amongst software designers and developers is that Machine Learning and Artificial Intelligence (AI) are technologies which are only meant for an elite group. However, if a particular technology is to truly succeed and scale, it should be friendly with the common man (in this case a normal software developer).

Azure SQL Data Warehouse (DW) has quickly become one of the most important elements of the Azure Data Services landscape. Customers are flocking to Azure SQL DW to take advantage of its rich functionality, broad availability and ease-of-use. As a result, Talend’s world-class capabilities in data integration, data quality and preparation, and data governance are a natural fit with Azure SQL DW.

Since the release of Talend 7.1 users can build Talend jobs as Docker images and publish them to Docker registries. In this blog post, I am going to run through the steps to publish to the major cloud provider container registries (AWS, Azure and Google Cloud). Before I dig into publishing container images to registries, I am going to remind you the basics of building Talend Jobs in Docker images from Talend Studio as well as point out the difference between a local build and a remote build.

In Yellowfin 8 we introduced two new products, Yellowfin Signals, to automatically discover the most critical changes in your business as they happen, and Yellowfin Stories, to provide context to the numbers and tell compelling stories with your data. We’ve received great feedback since then and implemented some of the requested enhancements to improve the user experience, increase governance and performance.

In my last post, I gave you the first six Do’s and Don’ts of Data Governance and promised to bring together an additional six to consider when making a data governance plan for your organization. Here are six more dos and don'ts when building your data governance framework.