Gaps in patient healthcare, ranging from access and affordability, to those specific to race, gender, age and beyond, are widening across the US and leading to a variety of detrimental results for people, the healthcare system, and the economy itself. Such ongoing disparities are slowing the country’s ability to achieve population health and accounting for billions of dollars in unnecessary health care spending annually.
It’s no secret that IT modernization is a top priority for the US federal government. A quick trip in the congressional time machine to revisit 2017’s Modernizing Government Technology Act surfaces some of the most salient points regarding agencies’ challenges: In the private sector, excluding highly regulated industries like financial services, the migration to the public cloud was the answer to most IT modernization woes, especially those around data, analytics, and storage.
A performance study on real-time data Integration from Oracle to Google BigQuery Using Striim.
A recent headline in Wired magazine read “Uber Hack’s Devastation Is Just Starting to Reveal Itself.” There is no corporation that wants that headline and the reputational damage and financial loss it may cause. In the case of Uber it was a relatively simple attack using an approach called Multi Factor Authentication (MFA) fatigue. This is when an attacker takes advantage of authentication systems that require account owners to approve a log in.
Sibros’ Connected Vehicle Platform on Google Cloud delivers OTA data updates, collection, and commands with the flexibility and scale of the cloud
The Apache Solr cluster is available in CDP Public Cloud, using the “Data exploration and analytics” data hub template. In this article we will investigate how to connect to the Solr REST API running in the Public Cloud, and highlight the performance impact of session cookie configurations when Apache Knox Gateway is used to proxy the traffic to Solr servers. Information in this blog post can be useful for engineers developing Apache Solr client applications.
BigQuery ML reduces data to AI barrier by making it easy to manage the end-to-end lifecycle from exploration to operationalizing ML models using SQL.
