Analytics

At the beginning of my career as a data analyst, I had to rely on other team members when something went wrong in our data pipeline, often only finding out about it after the event. That experience was one of the driving factors for me to join Kensu. When I spoke with the team for the first time, I had that “lightbulb moment”: data observability is a way of providing help to various data team members, including data analysts, in making their lives more productive and less painful.

The data landscape is constantly evolving, and with it come new challenges and opportunities for data teams. While generative AI and large language models (LLMs) seem to be all everyone is talking about, they are just the latest manifestation of a trend that has been evolving over the past several years: organizations tapping into petabyte-scale data volumes and running increasingly massive data pipelines to deliver ever more data analytics projects and AI/ML models.

Read about how BigQuery now allows you to use manifest files for querying open table formats.

‍ The mobile analytics stack isn't just about collecting data; it's about making sense of user behavior on your app. It's an amalgamation of various tools and techniques that allow businesses to gauge the effectiveness of their mobile applications, measure user engagement, and optimize for better performance.

Google recently introduced significant changes to its existing BigQuery pricing models, affecting both compute and storage. They announced the end of sale for flat-rate and flex slots for all BigQuery customers not currently in a contract. Google announced an increase to the price of on-demand analysis by 25% across all regions, starting on July 5, 2023.

Data is a powerful force that can generate business value with immense potential for businesses and organizations across industries. Leveraging data and analytics has become a critical factor for successful digital transformation that can accelerate revenue growth and AI innovation.