The Kitchen Table
Explore the product knowledge we've built up together
This content, written by Corey Carruthers, was initially posted in Looker Blog on Nov 6, 2014. The content is subject to limited support. Knewton is an adaptive learning infrastructure that powers educational products from many providers. Using predictive analytics, Knewton figures out the best way for individual students to learn and makes recommendations for what to learn next. Students are able to work at their own pace and on the materials that help them learn the subject matter best. With millions of students using Knewton, there is a lot of data to track. Using Looker, they have a set of tools that allows everyone in the company to see how they’re doing on a per-partner integration. Learn how Knewton is using Looker to create dashboards and visualizations, share data, and increase the number of projects their data team is able to manage.
This content, written by Lloyd Tabb, was initially posted in Looker Blog on Aug 21, 2013. The content is subject to limited support.“Miss Goldstein, please take a memo for me…. Colleagues, it has come to my attention that we are spending too much money on our secretarial staff, particularly as in the creation of memos. We’re going to need to limit the number of memos we can each send…” Can you imagine living back then? Having someone literally type up your (non-e)mail for you? My mother was a executive secretary. She was an amazing writer. But when she wrote, she spoke for someone else. Too many companies do the same today with engineers. A product manager dictates what should be built and engineers go code it with little input on what should be built or why it is being built. The product manager changes his mind frequently and whipsaws the engineers from one feature to another. At Looker we’re following a different path. Engineers and analysts interact directly with our customers and
This content, written by Kyle Coleman, was initially posted in Looker Blog on Jan 19, 2016. The content is subject to limited support.Analyzing Salesforce data to drive activity that leads to results. I still remember the exact moment I was able to explore all of our Salesforce data in our internal Looker instance. I sat next to one of our data analysts and, in a quick tutorial, he built the same reports in Looker that usually took me hours in Salesforce. As he finished the walkthrough, I said in a state of near disbelief, “It’s this easy?” He smiled as he walked away and said, “Let me know if I can help with anything.” Over two years later, my Sales Development Reps and I now rely on our Looker dashboards and reports for pretty much every element of our day-to-day: lead routing, activity monitoring, efficiency stats, success metrics, funnel analysis, email subject line analysis, the list goes on and on. And the best part about these reports is that they’re all contained on dashboards
This content, written by Drew Kells, was initially posted in Looker Blog on Jan 31, 2018. The content is subject to limited support.As Super Bowl Sunday 2018 approaches, the matchup between the New England Patriots and Philadelphia Eagles is generating lots of different storylines. Tom Brady and Bill Belichick are going for their astonishing sixth championship together, which would tie the all-time team record held by the Pittsburgh Steelers. The Eagles are trying to overcome a devastating injury to MVP-hopeful Carson Wentz with the incredible redemption story of Nick Foles and three straight wins as an underdog. And of course, Sunday will mark a rematch of the great Super Bowl XXXIX matchup, 13 years later. While there are many different angles to try and figure out who has an edge in the matchup, we wanted to take a closer look at what the numbers tells us by exploring NFL game and player data from . Games against common opponents Zooming in a bit from the statistics above, it’s alw
This content, written by Barbara Lawler, was initially posted in Looker Blog on Jan 28, 2020. The content is subject to limited support.As data speeds us forward into the future, is the smarter use of data our best hope for a more prosperous society and for healthier humans and the planet? What kinds of roles will humans have in that future? Are we our own best hope or the weakest link as more and more organizations “think” with data at machine speed? The entry into a new decade prompts us to ask the critical question about the role of expanded data collection and data creation, especially from observed data — in improved products, services, health treatments, urban solutions and in solving the world's economic and well-being problems. It seems that increasingly, businesses and governments are equally at odds and in partnership with each other. Driven by the growth of machine learning algorithms and the expansion of artificial intelligence programs in companies and in governments, some
This content, written by Pedro Arellano, was initially posted in Looker Blog on Jun 4, 2019. The content is subject to limited support.The business intelligence (BI) landscape is quite different than when I began my career in the late ’90s. That was the age of centralized, IT-led projects, enterprise data warehouses, and on-premises deployments. Today we see decentralized, business-oriented environments, vast data lakes (and a number of “data swamps”), and a multitude of cloud-based solutions. This continuous evolution keeps my line of work fun and interesting. But it’s easy to lose perspective of just how much the BI and analytics industry has changed. , published last week by Dresner Advisory Services, is a helpful guide to track the evolution of the BI space. In this year’s edition of the study, Dresner identifies a number of unmistakable trends that reaffirm a shift in the way we’re used to thinking about BI. For example, the report shows that Executive Management and Operations ar
This content, written by Erin Franz, was initially posted in Looker Blog on Feb 25, 2016. The content is subject to limited support.Looker is excited to support HPE Vertica OnDemand, HPE’s cloud deployment option for their high performing data warehouse. The value of Looker’s data platform has already been proven for many HPE Vertica customers, and now HPE has made it even easier to deploy Vertica in the way that your business requires: either in the cloud and on-prem. Looker for Vertica OnDemand: solution overview Looker is the only analytics and BI tool that can fully utilize all the capabilities of HPE Vertica: Looker works in-database, directly leveraging the power of the underlying Vertica engine and enabling full access to Vertica’s proprietary advanced analytics capabilities. Because Looker directly connects to Vertica, data availability is real-time: there is no latency between when data is available in Looker and when it’s available in the data warehouse. Your entire organizat
This content, written by Jen Grant, was initially posted in Looker Blog on Jun 12, 2017. The content is subject to limited support. When we were kids, we were full of questions. And we weren’t afraid to ask “why?” to just about everything. As a mom of four, this “why” stage can be irritating at times, but I know my kids are asking because they want to learn. And as they learn more, they will make better decisions and have more interesting insights about the world around them. Believe it or not, within a business, the process is no different. Last year in New York we hosted our first user conference , bringing some of the most innovative minds in data together to collaborate, share ideas and connect over what data can make possible for business and even for the world. We were amazed with the variety of ways customers were using data to better understand their businesses. Buzzfeed’s data science team spoke on while Bonobos, Jet.com, and Casper discussed . All of these talks had one th
This content, written by Nouras Haddad, was initially posted in Looker Blog on Oct 13, 2017. The content is subject to limited support.It was so great to see so many Looker customers and partners under one roof at in September. It granted us real opportunity to learn about the new tools and technologies our customers are using. It also revealed the broad range of innovation advancing across our partner network. From data collection, to analytics, to business decision making, companies are striving trying to bring agility to each step of the process. Next week, Looker is pleased to be sponsoring , the 2nd annual user conference hosted by our data-friends at Denodo. Things get underway in New York City on October 19th and 20th before rolling into London on 26, October. Both events will be for those who can’t attend in person. Sessions at DataFest will explore the ways some of today’s most innovative companies are using Data Virtualization as part of their data stack to become more agi
Will migrating from legacy --> K8s rebuild PDTs?Knowledge Drop
Last tested: Mar 2, 2021 No, the PDTs shouldn't regenerate. The instance slug that determines the reg key is now persisted in the internal DB for hosted customers for this reason, so when the DB is copied over the slug will be copied over too and the instance will keep using the same reg key. This content is subject to limited support.
This content, written by Frank Bien, was initially posted in Looker Blog on Sep 27, 2017. The content is subject to limited support.Last year at JOIN, Looker’s annual data conference, I talked about the . The first wave started 30 years ago with monolithic stacks that were reliable but inflexible. You could only get answers to specific questions, but you could trust those answers. It was also the era of the data rich and the data poor, where those without access to data starved as they waited for someone to finally have time to help them. That frustration led to the second wave: a revolution toward self-service tools where users grabbed whatever data they could lay their hands on and threw it into data cleansing, blending, and visualization tools for analysis. But that revolution came at a cost. Scattered tools created a mess of silos that didn’t talk to each other, and you lost the ability to speak in a common language with anyone else at your company. Enter data chaos. Today, we’re
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