So, you’ve made a cool People Analytics App, filled it with data from your HCM, Finance, Talent Review System, and Recognition System (I could go on). You’ve also governed access to it to specific people and can use it to tell compelling stories.
So, you’ve made a cool People Analytics App, filled it with data from your HCM, Finance, Talent Review System, and Recognition System (I could go on). You’ve also governed access to it to specific people and can use it to tell compelling stories.
We’re excited to share that after adding ANSI SQL, secondary indices, star schema, and view capabilities to Cloudera’s Operational Database, we will be introducing distributed transaction support in the coming months. The ACID model of database design is one of the most important concepts in databases. ACID stands for atomicity, consistency, isolation, and durability. For a very long time, strict adherence to these four properties was required for a commercially successful database.
Across nearly every sector working with complex data, Spark has quickly become the de-facto distributed computing framework for teams across the data and analytics lifecycle. One of most awaited features of Spark 3.0 is the new Adaptive Query Execution framework (AQE), which fixes the issues that have plagued a lot of Spark SQL workloads. Those were documented in early 2018 in this blog from a mixed Intel and Baidu team.
The key differences between Hadoop vs. SQL: Organizations rely on big data to power their business, but many teams struggle with the complexities of data management. Thankfully, Hadoop and SQL handle large data sets more efficiently. These tools manage data in unique ways, which makes it difficult for us to compare them on a like-for-like basis. However, organizations looking to streamline their tech stacks might have reason to choose one over the other. In this article, we compared Hadoop vs.
If there’s one thing enterprises have learned in 2020, it’s how to navigate through uncertain times, and in 2021, organizations will likely have to continue navigating through a shifting landscape. One trend that we’ve seen this year, is that enterprises are leveraging streaming data as a way to traverse through unplanned disruptions, as a way to make the best business decisions for their stakeholders.
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.
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.
