Systems | Development | Analytics | API | Testing

Learn what data replication is. Explore the best data replication tools, compare pros and cons, and discover how to choose the right solution.

Discover new BigQuery Data Transfer Service capabilities. Automate low-cost data ingestion from diverse sources directly into your data warehouse.

Learn about the latest innovations to help improve BigQuery price-performance with autonomous query processing.

Two engineers used AI coding agents to design, build, and ship a production-ready status page — here's what worked, what didn't, and what we'd do differently.

A single source of truth doesn’t just come from centralizing data. You have to engineer context from it, too.

Your data pipeline breaks at 2 AM. Again. By morning, corrupted data has cascaded through dashboards, reports sit empty, and your team spends half the day tracking down root causes instead of building features. This scenario plays out across organizations daily. Data engineers spend 44% of their time firefighting pipeline failures rather than delivering value.

Traditional integration platforms were built for a world of predictable, human-configured workflows. But with enterprise software rapidly incorporating agentic AI capabilities, that world is changing fast. Agentic iPaaS represents a fundamental architectural shift where intelligent agents reason, adapt, and execute integrations autonomously, moving beyond simple "if-then" automation to goal-oriented systems that make real-time decisions.

Establishing a data warehousing system that meets all your business intelligence targets is by no means an easy task. It traditionally involves profiling source systems, designing a dimensional model by hand, writing the DDL to deploy it, building the load pipelines, and scheduling them to run, work that can take weeks. Astera's manual, step-by-step approach to this is covered in Building a Data Warehouse – A Step by Step Approach.