Many in the community have been asking us to develop a new Kafka to S3 connector for some time. So we’re pleased to announce it's now available. It’s been designed to deliver a number of benefits over existing S3 connectors. Like our other Stream Reactors, the connector extends the standard connect config adding a parameter for a SQL command (Lenses Kafka Connect Query Language or “KCQL”). This defines how to map data from the source (in this case Kafka) to the target (S3).

This short guide for conducting successful big data analysis will take you through a couple of the most essential tips to help you learn how to analyse data & become a better data analyst by avoiding rookie mistakes and embracing the correct mindset to get the most from your data. We’ll also cover some of ways that conducting effective data analysis can help your organisation.

Over the years the DreamFactory team has collaborated on a lot of really interesting API-oriented projects. Many of these projects involve integrating the DreamFactory platform into a much larger technology ecosystem. To put this into perspective, although our client base ranges widely in terms of both industry and company size, we often work with organizations tending towards the larger end of the scale.

Fraud awareness in the Financial Services industry is more important than ever. According to the September 2020 benchmarking report conducted by the Association of Certified Fraud Examiners (ACFE) in response to the coronavirus, 77% of survey respondents, representing a range of industries, have observed an increase in the overall level of fraud as of August, compared with 68% in May. The report reveals 68% of respondents have observed an increase in payment fraud schemes (vs.

In June, Snowflake announced the public preview of the external functions feature with support for calling external APIs via AWS API Gateway. With external functions, you can easily extend your data pipelines by calling out to external services, third-party libraries, or even your own custom logic, enabling exciting new use cases. For example, you can use external functions for external tokenization, geocoding, scoring data using pre-trained machine learning models, and much more.

A product manager’s job first and foremost is to deliver value to their customers with the products they oversee. In order to ensure quality, those products need to be tested. Testing before release in a well thought-out manner gives product managers the confidence to create and execute high quality products and user experiences.

With COVID-19, it has become clear that ideas and assumptions that worked in the past will no longer apply now and in the future. The region is expected to face its deepest economic slowdown since the 1970s due to COVID-19. Businesses might be wondering what to do next.

When SmartBear added TM4J to our product portfolio earlier this spring, it brought us one more step towards our mission: help teams achieve quality throughout the entire software development lifecycle (SDLC), no matter where they’re starting from. The addition rounded out our test management portfolio, offering a solution to those customers who seek a SaaS test management solution within Jira. So why the name change?

Deep learning has evolved in the past five years from an academic research domain, to being adopted, integrated and leveraged for new dimensions of productivity across multiple industries and use cases, such as medical imaging, surveillance, IoT, chatbots, robotic,s and many more. From NLP to computer vision, deep learning has been breaking the barriers of SOTA algorithms and providing results that were, otherwise, impossible to achieve.