Systems | Development | Analytics | API | Testing

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Python Optimization: 3 Easy Steps

Python is one of the best programming resources available for designing machine learning systems. With a variety of technical abilities and potentially time-saving loops and processes, it can be an invaluable tool. However, it’s these capabilities that also make Python difficult to use. In many cases, Python may seem sluggish as it tries to navigate intricate, complicated strings of code.

Analytics best practice: 5 key dashboard design principles

Simply put, a lot of effort is going into creating dashboards that the intended audience don’t even look at. The main purpose of a dashboard is to communicate business data in a visual form that highlights to the reader what is important, arranges it for clarity and leads them through a sequence that tells the story best so they can make better data-led decisions. Design and an understanding of how humans make decisions exist to assist this purpose.

Migrating to Atlassian Cloud with Zephyr Test Management

As you may or may not know, Atlassian is accelerating their journey to Cloud. This has some important implications to anyone who uses Atlassian Server based products, including apps. Read on to learn how we support customers of Zephyr for Jira and Zephyr Scale as they transition to Cloud. As of February 2, 2021, Atlassian customers can no longer purchase or request a quote for new Server licenses. Existing customers can continue purchase Server apps on Marketplace until February 2, 2023.

Multi-layer API security with Apigee and Google Cloud Armor

Information security has become headline news on a daily basis. You have probably heard of security risks ranging from malicious bots used in schemes both big and small, to all-out "software supply chain attacks" that involve large-name enterprises and their customers, and that ultimately affect numerous governments, organizations, and people.

ClouderaNow 21 - Automate Data Enrichment Pipelines

See this demo of Cloudera Data Engineering which builds upon Apache Spark and allows us to load, transform, and enrich our datasets and has built-in workload orchestration to automate these pipelines at scale. The demo will also illustrate how easy it is to go from streaming to enrichment and data pipeline automation all in an end-to-end data platform.