BigCommerce has teamed up with BigQuery to create a native integration to its ecommerce platform that makes data analysis easier than ever.

Hevo Data is a cloud-based ETL platform with a REST API that manages data pipelines. Chief Technology Officers (CTOs), software developers, and data analysts use Hevo to move data from one location to another for better data-driven decision-making.

Learn how Oviya Arasu and her team at HubSpot use Fivetran data models and Transformations for dbt Core™ to automate their pipelines and decrease engineering bottlenecks.

Businesses and organizations must leverage the power of data to stay ahead of competitors in today's fast-paced market. But ingesting data from various sources is only possible with a specialized solution like Matillion. Its ease of use and hundreds of pre-built connectors made Matillion popular among many companies. But its pricing and limited capabilities convinced some of them to seek an alternative.

As fraud continues to grow in prevalence, SecOps teams are increasingly investing in fraud prevention capabilities to protect themselves and their customers. One approach that’s proved reliable is the use of log analytics and telemetry data for fraud prevention. By collecting and analyzing data from various sources, including server logs, network traffic, and user behavior, enterprise SecOps teams can identify patterns and anomalies in real time that may indicate fraudulent activity.

Deploying trained models takes models from the lab to live environments and ensures they meet business requirements and drive value. Model deployment can bring great value to organizations, but it is not a simple process, as it involves many phases, stakeholders and different technologies. In this article, we provide recommendations for data professionals who want to improve and streamline their model deployment process.

As we hurtle into a more connected and data-centric future, monitoring the health of our data pipelines and systems is becoming increasingly harder. These days we are managing more data and systems than ever before, and we are monitoring them at a higher scale.

In today's data-driven world, product analytics is crucial in understanding user behavior, improving product features, and driving business growth. However, product analytics alone may not provide a complete picture of user interactions and business performance. Integrating product analytics with other data sources and systems is essential to gain deeper insights and make more informed decisions.