Systems | Development | Analytics | API | Testing

We're excited to share our latest feature enhancement that improves reliability and control for outbound data delivery across the platform. This release introduces configurable request throttling on the REST API destination, giving data teams a native way to respect target API rate limits directly within their pipeline configuration.

AI has evolved fast. We've gone from static, predictive models to dynamic, interactive agents. But most organizations still run data pipelines that haven't kept up. Consider what’s happening in modern AI architecture. Teams deploy high-performance engines like large language models (LLMs) and real-time fraud detectors, then feed them data that's hours or days old.

Zero-ETL Database APIs let you access live data instantly without needing traditional ETL processes. Instead of extracting, transforming, and loading data, these APIs query databases directly in real-time, significantly reducing delays that can span hours. Key features include federated querying (accessing multiple data sources simultaneously) and schema-on-read (applying schemas dynamically during queries).

While winning in artificial intelligence (AI) is critical to the future of business, old-school analytics—visualizations, dashboards, and infrequent reports—are still core to an organization's data needs. Behind the scenes, this analytics ecosystem remains heavily hydrated by batch-based ELT data integration. For a long time, this made perfect sense, as data sources were fewer, data volumes were manageable, and analytics consumers were limited.

Learn what reverse ETL is and how it enables operational analytics. Explore its process and applications within sales, marketing, and support.

Every business decision relies on data—and bad data leads to bad decisions. ETL testing validates that your data extraction, transformation, and loading processes deliver accurate, complete, and consistent information to your analytics platforms. In 2026, the stakes have never been higher for organizations struggling with manual data validation that automated testing could eliminate.

Your BI team didn't sign up to spend 69% of their time on repetitive data preparation tasks. Yet this is the reality for most data teams drowning in support ticket backlogs while strategic initiatives languish. Every hour spent manually updating schemas, troubleshooting failed data loads, or running ad-hoc queries is an hour not spent on the analytics that actually drive business decisions.

Learn what ETL is, how the ETL process works, why it’s important, and how ETL software supports modern data integration, transformation, and analytics.

Your executive dashboard shows yesterday's data while your competitors make decisions with information that's minutes old. This gap isn't just an inconvenience—it's a competitive disadvantage costing businesses millions in missed opportunities, delayed responses, and stale insights. Service Level Agreements (SLAs) for real-time dashboards solve this problem by establishing measurable commitments for data freshness, accuracy, and availability.

Apache HBase provides a distributed, column-oriented model with tables → rows → column families/qualifiers and versioned cells. The design is ideal for sparse, wide datasets. ETL is central because performance hinges on how data moves through the default write path—WAL → MemStore → HFiles—versus bulk-load paths that write HFiles directly.