Socrates preached, “To know thyself is the beginning of wisdom.” This ancient Greek anecdote applies to your modern Apache Kafka project: developers, go forth and load test your real-time application to understand the capacity and limitations of your project before deployment. Failure to do so will cost you time and money (e.g. Robinhood’s outage on a historic trading day). Load testing your real-time applications has three main objectives.
With the pandemic forcing businesses worldwide to reboot, many have no choice but to exact drastic cost-cutting measures to keep the lights on. Cloud computing is an expense incurred by every digital business that, unlike many other operating costs, is largely variable.
Software development has greatly evolved over the years. Serverless is an emerging software architecture that could resolve issues when it comes to developing software solutions. As software developers, you’re tasked with server setup, installing the software, operating systems requirements, server management and maintenance, designing an application with high fault tolerance and availability, as well as managing load balance and more.
Cloudera Data Platform Powered by NVIDIA RAPIDS Software Aims to Dramatically Increase Performance of the Data Lifecycle Across Public and Private Clouds Cloudera announced today a new collaboration with NVIDIA that will help Cloudera customers accelerate data engineering, analytics, machine learning and deep learning performance with the power of NVIDIA GPU computing across public and private clouds.
With exponential growth in remote working, teams are struggling with collaboration and maintaining visibility. Sound familiar? In this blog we’ll explore how to keep track of your testing activity and team’s progress in Jira, and how TM4J – Test Management for Jira can help identify common problem areas so proper measures can be taken.
So, if you’re a nose-to-the-keyboard developer, there’s ample probability that this analogy is outside your comfort zone … bear with me. Imagine two Olympics-level figure skaters working together on the ice, day in and day out, to develop and perfect a medal-winning performance. Each has his or her role, and they work in sync to merge their actions and fine-tune the results.
This is the second post in a series about data modeling and data governance in the cloud from Snowflake’s partners at erwin. See the first post here. As you move data from legacy systems to a cloud data platform, you need to ensure the quality and overall governance of that data. Until recently, data governance was primarily an IT role that involved cataloging data elements to support search and discovery.
In recent years, organizations have been making massive investments in data analytics to transform their growing volume of data into actionable insights to inform decision-making. However, the pursuit of becoming data-driven has uncovered challenges earlier in the data pipeline that are preventing companies from reaping all the benefits from their data.
