As a Customer Success Architect with Talend, I spend a significant amount of my time helping customers with optimizing their data integration tasks – both on the Talend Data Integration Platform and the Big Data Platform. While most of the time the developers have a robust toolkit of solutions to address different performance tuning scenarios, a common pattern I notice is that there is no well-defined strategy for addressing root causes for performance issues.
The best article I’ve read recently, Is the business intelligence dashboard dead? by Marius Moscovici. This article challenges the status quo around what a dashboard is, why it exists, and what business users want from their analytics.
We’re heading into the holiday season, which is a great opportunity to reflect on the year that's just gone. For me, this year has been dominated by the release of Yellowfin 8 and two new products in particular - Stories and Signals.
Deploying a successful technology solution, especially in data management, takes more than just installing software and writing a job (or multiple jobs… thousands in some cases), and running those jobs. If you’re taking on a new data management initiative, deploying using containers and serverless technology, migrating from traditional data sources to Hadoop, or from on-premises to the cloud, you may be sailing in unfamiliar waters.
Ever since Business Intelligence emerged as a technical and commercial practice, the promise has been that the right information, delivered at the right time, in the right format would help us, as users, to make better decisions.
In the last few years, microservices or microservice architecture has become a popular reference in IT due to its benefits and the flexibility this architectural style brings. Before we get into working with microservices and Talend, we should review the basics of microservices or a microservice architecture.
This is the first of a series of blogs on how to architect, engineer and manage performance. In it, I’d like to attempt to demystify performance by defining it clearly as well as describing methods and techniques to achieve performance requirements. I'll also cover how to make sure the requirements are potentially achievable.
New year, and time for the annual trends webinar. It’s the third time I have the honor to present it. As always, it takes a village to create this, and I’m thankful for all the people who chipped in. I’d like to highlight in particular Murray Grigo McMahon. His blog on data as an ecosystem, as well as great discussions, was a big source of inspiration, and will feature in the trends. This year, the title of the webinar is “The Dawn of Postmodern Analytics”.
Google BigQuery is a serverless enterprise data warehouse tool that’s designed for scalability. We built BigQuery to be highly scalable and let you focus on data analysis without having to take care of the underlying infrastructure. We know BigQuery users like its capability to query petabyte-scale datasets without the need to provision anything. You just upload the data and start playing with it.
Earlier this year, I attended the Pacific Northwest BI & Analytics Summit. It’s a relatively small industry event that brings together some thought leaders, industry analysts and representatives from major vendors who are all intensely passionate about the BI space. Some of the people who were there include Donald Farmer, Doc Searls, Jill Dyche, Claudia Imhoff, Mike Ferguson and Shaun Rodgers.
