Systems | Development | Analytics | API | Testing

Reverse ETL (Extract, Transform, Load), a relatively newer data integration paradigm, operationalizes enterprise data to accelerate digital transformation. Lately, reverse ETL has become an essential part of data management practices, enabling enterprise data teams to reverse the traditional ETL and warehousing process.

Poorly written queries and rouge queries can create a nightmare for data teams when it comes to fixing and preventing performance issues, and as a result, costs can quickly spiral out of control. Whether you want to move your on-premises data to Google BigQuery or make the most of your Google BigQuery investments, Unravel can help businesses that struggle to find the optimal balance of performance and cost of Google BigQuery.

Web APIs are an integral piece of the development landscape today. According to a 2021 survey, top industries such as Digital Banking, Retail, and Financial services have experienced significant year-on-year API traffic growth with 70%, 51% and 50% increases since February 2021, respectively. The economic impact of APIs is significant and vast. Recent funding rounds of API-focused companies have seen millions of dollars go to companies precisely because APIs appeal to developers.

Today’s digital economy is shifting toward dependence on microservices — self-contained and reusable software components — working in coordination to compose the applications we use. Communication between microservices happens through the API (or application programming interface). Using APIs (which are like pieces of software that let applications communicate with each other) is how microservices expose their functionalities and allow access to their services and their data.

Business data is continuously growing in complexity and quantity. With so many variables, it’s impossible to scan every dashboard or report with a fine-eye, or track every trend. To get the most insights out of your dashboards and reporting today requires a two-prong approach of both manual analysis and automated analysis - the latter of which requires the integration and use of what is called augmented analytics.

This article is the third in a series of overviews of our new UI. You can find the others here: This time we will dive into the changes we’ve made in the runtime phase.

I think many of you would agree that the world is in a somewhat precarious situation at the moment. A long period of polarization preceded the current conflict in Ukraine that personally touches many of us. Maybe this is a good time to reflect on initiatives that bring people together and how data-driven insights and connection help us. Integrating (applications, platforms, data) is what we do at elastic.io, but maybe it’s better to think of it as connecting?

Following on from our post on Cucumber and Gherkin Language Best Practices we have compiled a list of 15 expert suggestions to bear in mind when working with Cucumber. Following on from our previous post on 'Cucumber Best Practices,' we have compiled a list of 15 export suggestions to bear in mind when working with Gerkin language and Cucumber.

One of the most common use cases our customers are using Kong for is rate limiting. There are a few common reasons for doing this: For all those and more, we’re able to easily add this functionality with Kong’s rate limit advanced plugin. The plugin can be applied at different levels such as service, route, specific consumer, or even in a global scope.

Bi-directional data movement and low latency are key to effectively integrating data between on-prem and cloud for real-time analysis.