Systems | Development | Analytics | API | Testing

Programmers spend more time coding APIs than debugging, testing, or documenting APIs. Building APIs manually is an arduous task that prevents professionals from focusing on other tasks. Luckily, you don’t have to waste time creating APIs by hand. Instead, you can rely on instant API generation that does most of the work for you. Of course, you want to make sure you choose the right types of API generation for your projects.

Self-service analytics is fast becoming a necessity, not a luxury, in the modern enterprise. More businesses want to provide staff with self-service BI tools they can all use, without needing IT help or technical knowledge. This helps drive a data-driven culture across the organization, open up access to data to more people, and unlock actionable insights.

See how analyzing your business performance and relying on data can help you make better decisions and optimize your business operations.

Loadero supports three feature-rich test frameworks: Javascript + Nightwatch Java + TestUI, Python + Py-TestUI, but to expand the functionality further, we also develop custom commands for more insightful performance and load testing. One of the recent additions to the custom commands to use in your Loadero test scripts is the timeExecution() command.

These days, few people need to be convinced of the value of data. As businesses face pressure to innovate, IT is often overwhelmed with requests for custom data-driven applications. But the issue is that for many IT teams, data is still more of an obstacle than an enabler. As data sources proliferate, data access becomes a complex puzzle.

While APIs (application programming interfaces) have been around for a while, their importance has grown in recent years. Over this time, APIs have even become necessities in most business operations. Did you know that the global investment in APIs is estimated to be worth around $23.6 million annually? The numbers speak for themselves. APIs are quickly becoming a vital part of every business operation competing in the digital economy.

The data-driven culture cultivated in modern-day organizations is focused on deriving the best possible business insights from their data. With data scattered across the globe, these organizations' most significant challenge is to break the silos of their decentralized data and gather new data for analysis in real-time. To address the data silo problem, data engineering brought forward solutions like ETL, ELT, and data integration tools.

A data warehouse is a centralized storage system for structured data. The data stored here is used for reporting, analytical processing and business intelligence.

For the next interview in our series speaking to technical leaders from around the world, we’ve welcomed Zack Rosenberg, CEO and Co-Founder at CatapultX.

Data exploration is the first step of data analysis. It allows us to uncover how the dataset you are working with looks like: ‍ Data scientists, data engineers, data analysts and business users regularly use data exploration as part of their pipeline to understand data, uncover hidden insights and prepare data for further analysis.