Data challenges: From mainframes to the modern data stack
The sheer quantity and diversity of data sources make today’s landscape strikingly different — which requires a new set of tools.
The sheer quantity and diversity of data sources make today’s landscape strikingly different — which requires a new set of tools.
In part 1 of this blog we discussed how Cloudera DataFlow for the Public Cloud (CDF-PC), the universal data distribution service powered by Apache NiFi, can make it easy to acquire data from wherever it originates and move it efficiently to make it available to other applications in a streaming fashion.
In 2015 GraphQL was created by Facebook as an alternative to REST APIs to give more power to frontend developers by making API calls more flexible. GraphQL achieves this goal by providing its API consumers with a query language that allows them to query just the data they need. While GraphQL can improve frontend developer experience, its specification doesn’t have opinions on security.
It is necessary to test web apps and ensure that they perform according to user requirements in order to provide a high-end user experience. There are several tools and frameworks available on the market for testing online applications, including Playwright, Cypress, and Selenium to mention a few.
Have you ever paid a bill for electricity, internet, or water? Was that bill based upon the amount of resources you used?
Apache Kafka is an open-source distributed event streaming platform used by thousands of companies for high-performance data pipelines, streaming analytics, data integration, and mission-critical applications. Administrators, developers, and data engineers who use Kafka clusters struggle to understand what is happening in their Kafka implementations.
What are the best practices of the mobile team behind one of the world’s leading buy-and-sell platforms for improving productivity and the developer experience? Read on for the five main learnings from our recent customer story.