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 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.

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.

It’s no secret that advancements like AI and machine learning (ML) can have a major impact on business operations. In Cloudera’s recent report Limitless: The Positive Power of AI, we found that 87% of business decision makers are achieving success through existing ML programs. Among the top benefits of ML, 59% of decision makers cite time savings, 54% cite cost savings, and 42% believe ML enables employees to focus on innovation as opposed to manual tasks.

Enabling data and analytics in the cloud allows you to have infinite scale and unlimited possibilities to gain faster insights and make better decisions with data. The data lakehouse is gaining in popularity because it enables a single platform for all your enterprise data with the flexibility to run any analytic and machine learning (ML) use case. Cloud data lakehouses provide significant scaling, agility, and cost advantages compared to cloud data lakes and cloud data warehouses.

Demand for both entry-level and highly skilled tech talent is at an all-time high, and companies across industries and geographies are struggling to find qualified employees. And, with 1.1 billion jobs liable to be radically transformed by technology in the next decade, a “reskilling revolution” is reaching a critical mass.

Information technology has been at the heart of governments around the world, enabling them to deliver vital citizen services, such as healthcare, transportation, employment, and national security. All of these functions rest on technology and share a valuable commodity: data. Data is produced and consumed in ever-increasing amounts and therefore must be protected. After all, we believe everything that we see on our computer screens to be true, don’t we?

The telecommunications industry continues to develop hybrid data architectures to support data workload virtualization and cloud migration. However, while the promise of the cloud remains essential—not just for data workloads but also for network virtualisation and B2B offerings—the sheer volume and scale of data in the industry require careful management of the “journey to the cloud.”

In this post I will demonstrate how Kafka Connect is integrated in the Cloudera Data Platform (CDP), allowing users to manage and monitor their connectors in Streams Messaging Manager while also touching on security features such as role-based access control and sensitive information handling. If you are a developer moving data in or out of Kafka, an administrator, or a security expert this post is for you. But before I introduce the nitty-gritty first let’s start with the basics.

Like all of our customers, Cloudera depends on the Cloudera Data Platform (CDP) to manage our day-to-day analytics and operational insights. Many aspects of our business live within this modern data architecture, providing all Clouderans the ability to ask, and answer, important questions for the business. Clouderans continuously push for improvements in the system, with the goal of driving up confidence in the data.