Technical interviews about software topics.
Prophecy: Apple of Data Engineering with Raj Bains
Prophecy is a complete Low-Code Data Engineering Platform for the Enterprise. Prophecy enables all your teams on Apache Spark with a unique low-code designer. While you visually build your Dataflows – Prophecy generates high-quality Spark code on Git. Then, you can schedule Spark workflows with Prophecy’s low-code Airflow. Not only that, Prophecy provides end-to-end visibility
Pulsar Rerevisted with Enrico Olivelli
In the previous episode, Pulsar Revisited, we discussed how the company DataStax has added to their product stack Astra Streaming, their cloud-native messaging and event streaming service that’s built on top of Apache Pulsar. We discussed Apache Pulsar and the added features DataStax offers like injecting machine learning into your data streams and viewing real-time
CockroachDB: Distributed Databases and Containerization with Spencer Kimball
In 2003, Google developed a robust cluster management system called Borg. This enabled them to manage clusters with tens of thousands of machines, moving them away from virtual machines and firmly into container management. Then, in 2014, they open sourced a version of Borg called Kubernetes, or K8s. Now, in 2021, CockroachDB is a distributed
Imply Infra: Big Data Analysis and Real-World Examples with Jad Naous
Big data analytics is the process of collecting data, processing and cleaning it, then analyzing it with techniques like data mining, predictive analytics, and deep learning. This process requires a suite of tools to operate efficiently. Data analytics can save companies money, drive product development, and give insight into the market and customers. The company
Better Stack: A New DevOps Experience with Juraj Masar
DevOps has shortened the development life cycle for countless applications and is embraced by companies around the world. But managing and monitoring multiple environments is still a major pain point, particularly when companies need to mix cloud and legacy systems. Knowing when services go down and quickly pinpointing the cause is essential for continuous development.
Data Science on AWS: Implementing AI and ML Pipelines on AWS with Chris Fregly
Data science is an interdisciplinary field that combines strong technical skills with industry knowledge to perform a large range of jobs. Data scientists solve business questions with hands-on work cleaning and analyzing data, building machine learning models and applying algorithms, and generating dynamic visuals and tools to understand the world from the data it generates.