Systems | Development | Analytics | API | Testing

Historically, finance teams largely operated in the background, focusing on record-keeping, compliance, and ensuring that financial operations ran smoothly. Their primary responsibilities revolved around managing costs, balancing the books, and maintaining operational efficiency. In recent years, that has changed.

According to recent survey data from Cloudera, 88% of companies are already utilizing AI for the tasks of enhancing efficiency in IT processes, improving customer support with chatbots, and leveraging analytics for better decision-making. More and more enterprises are leveraging pre-trained models for various applications, from natural language processing to computer vision. For that reason, Cloudera is evaluating a new line of business: Cloudera Integrated Data and AI Exchange (InDaiX).

In the business world, gaining the “unfair advantage” often seems like a strike of great luck and timing (think Zoom and the pandemic). But in reality, it’s often the result of deliberate strategies and actions (in Zoom’s case, delivering a superior user experience). One great strategy: getting the unfair advantage through a best-in-class technology ecosystem.

Why now is the time for data leaders in financial services to address the challenges of tick data analysis — and how Snowflake can help.

For historical reasons, we have been using dynamic collection creation. This means that each new event creates a new collection in the database. While this provides some benefits, such as managing permissions on the collection level, it also has many downsides. And because we wanted to future-proof Countly, we decided to make this step and change the model, which will potentially allow us to do more in the future.

Ingestion and transformation are the primary workloads of real data warehouses.