Systems | Development | Analytics | API | Testing

Here at Lenses.io, we’re focused on making data technologies such as Apache Kafka and Kubernetes as accessible to every organization as possible. It’s part of our DataOps vision and company DNA. Lenses is built by developers, for developers. We understand the headaches they live with and the challenges they face seemingly have to learn a new data technology every few months. We believe that’s just not the right model.

Editor’s note: Today we’re hearing from some of the team members involved in building BigQuery over the past decade, and even before. Our thanks go to Jeremy Condit, Dan Delorey, Sudhir Hasbe, Felipe Hoffa, Chad Jennings, Jing Jing Long, Mosha Pasumansky, Tino Tereshko, and William Vambenepe, and Alicia Williams. This month, Google’s cloud data warehouse BigQuery turns 10.

While cloud providers and data analytics firms are proliferating across markets and landscapes, what distinguishes one from another? How can you know which one holds the keys to your agency’s digital transformation? The reality is that no matter how slick the advertising, how pervasive the presence across conferences and webcasts, or how high the C-suite’s former government offices … it’s the offerings that matter most.

Many enterprise data science teams are using Cloudera’s machine learning platform for model exploration and training, including the creation of deep learning models using Tensorflow, PyTorch, and more. However, training a deep learning model is often a time-consuming process, thus GPU and distributed model training approaches are employed to accelerate the training speed.

We all know visualization alone is not enough in the world of modern BI. And, when Qlik Sense was introduced, we focused on building a world-class platform, driven by our associative engine, open APIs and modern architecture. Our vision was to drive all the major analytics use cases, and support a lightning fast pace of innovation for the next decade and beyond.

Time series data and real-time data acquisition is growing at a 50% faster rate than static, latent, or historical data. In some ways, it has become more important than any other type of data, as it provides real-time decision making, enables autonomous decisions at the edge, and allows for more complex Machine Learning (ML) applications. Time series data and real-time data acquisition dominate industrial use cases, as it is ubiquitous with the manufacturing process.