We're living in a data-driven age. In every sector, we've seen new companies emerge, executing lightning-fast strategies based on sophisticated analytics. These data mavericks have disrupted and sometimes even devoured their more traditional rivals. To stay afloat, you need a state-of-the-art data infrastructure. That means having the right platforms, the right data pipelines, and the right analytics engines. But when you have all that data, what do you actually do with it?
COVID-19 has forced virtually every industry to embrace an acceleration in digital capabilities. While it can be argued that digital transformation was already underway; it’s hard to dispute that it has accelerated in recent months. A recent McKinsey survey, cited in CRN, shows that worldwide, 58 percent of customer interactions were digital as of July 2020.
Managing online teams has become the new normal! In an online world, how do you give effective feedback, have a difficult conversation, increase team accountability, communicate to stakeholders effectively, and so on? At Unravel, we are a fast-growing AI startup with a globally distributed engineering team across the US, EMEA, and India. Even before the pandemic this year, the global nature of our team has prepared us for effectively leading outcomes across online engineering teams.
Have you been burned by the unexpected costs of a cloud data warehouse? If so, you know about the failed economics of some cloud-native solutions on the market today. If not, before adopting a cloud data warehouse, consider the true costs of a cloud-native data warehouse. Data warehouses have been broadly adopted to provide timely reports and valuable insights. However, traditional deployments are notoriously cumbersome and cost-prohibitive at large scales.
Data engineers love to use SQL to solve all kinds of data problems. For this and more, Snowflake is a perfect partner. Snowflake’s support for standard SQL and several SQL variations, combined with JavaScript stored procedures, has helped me solve complex data challenges. But sometimes you might have the need for custom code.
In the previous post, we talked about Kerberos authentication and explained how to configure a Kafka client to authenticate using Kerberos credentials. In this post we will look into how to configure a Kafka client to authenticate using LDAP, instead of Kerberos. We will not cover the server-side configuration in this article but will add some references to it when required to make the examples clearer.
Two years ago we wrote a research report about Federated Learning. We’re pleased to make the report available to everyone, for free. You can read it online here: Federated Learning. Federated Learning is a paradigm in which machine learning models are trained on decentralized data. Instead of collecting data on a single server or data lake, it remains in place—on smartphones, industrial sensing equipment, and other edge devices—and models are trained on-device.
As part of our ongoing commitment to supporting Government regulations and standards in our enterprise solutions, including data protection, Cloudera recently introduced a version of our Cloudera Data Platform, Private Cloud Base product (7.1.5 release) that can be configured to use FIPS compliant cryptography.
