Systems | Development | Analytics | API | Testing

Five paths give Fivetran the flexibility to connect virtually any data source, from SaaS applications and flat files to application databases, proprietary APIs, and real-time event streams.

Node.js debugging allows us to identify and fix errors, unexpected behavior, and performance issues in server-side JavaScript applications. Instead of using the less precise console.log, we can connect a real debugger and control execution step by step. Most Node.js developers actually use one of two distinct setups: This guide will show you how to use both technologies, with a clear step-by-step workflow for each.

A lot of teams think their AI quality problem begins when the first test run starts but it usually begins much earlier.

A dropped connection used to cost a typing indicator. With AI in the product, it costs an entire response, mid-generation. That single shift is enough to reopen a decision most teams made years ago and stopped thinking about: whether to keep building realtime infrastructure themselves, or buy it. Fin, the AI agent platform formerly known as Intercom, made the call to buy. It had run its own realtime system, Nexus, for years, at the scale of one of the biggest support platforms on the internet.

Managing data pipelines across AWS, Azure, and GCP simultaneously is one of the most demanding infrastructure challenges data teams face today. Native cloud services like AWS Glue and Azure Data Factory solve problems within their own ecosystems, but they create friction the moment data needs to move across provider boundaries.

Every Monday morning, someone on your team downloads a report, opens it in Excel, cleans up the column headers, removes the blank rows, and uploads it to Salesforce or Snowflake. Then they do it again on Tuesday for a different source. By Friday, half their week is gone, and the dashboard is still showing last week's numbers.

You have tested hundreds of features. You know the drill. Open the test case, write the preconditions, list the steps, fill in the expected result, run it, compare. Pass or fail. Move on. Then someone hands you an AI feature. A chatbot. A "summarize this ticket" button. A search box that answers in full sentences instead of returning a list of links. You open your test case template, you get to the "expected result" field, and you stop.

Last month, a two-line bug fix took down three unrelated features in a colleague’s app. The fix itself was correct — it patched a null check on a checkout API. Nobody re-ran the tests for the inventory service that depended on it, and by Monday, support tickets were stacking up. That gap is exactly what maintenance testing exists to close. Maintenance testing is the QA work you do after software ships — testing every bug fix, upgrade, patch, or migration to confirm nothing else broke.

Spend enough time in the data management world, and you’ll quickly encounter a flood of terminology: semantic layers, knowledge graphs, unified metadata, governance fabrics, data meshes, and, of course, agentic AI. Most organizations know these aspects matter, yet many still struggle to understand how they fit together. The problem with traditional data architecture is that it is often treated as purely technical.