An archive of Looker blog posts
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This content, written by Margaret Rosas, was initially posted in Looker Blog on Jun 20, 2014. The content is subject to limited support.Looker's product roadmap is developed from our own vision and experience as data analysts and in response to customer feedback. We strive to build an analytics platform that turns any organization into a data-driven organization. Looker v2.2 is our latest release and adds important new capabilities for: The new Looker integrated developer environment (IDE) with improved git integration, auto-generated code, and global search and replace More flexible join syntax, which further simplifies data modeling Custom map visualizations Looker’s new IDE A Looker implementation consists of a 3-step process. The first step is to create a connection to a SQL dialect database. The second step is to run the model generator to create the model files. The final step is to further develop the generated model to reflect the analytical needs of your business. All of these
This content, written by Erin Franz, was initially posted in Looker Blog on Feb 10, 2016. The content is subject to limited support.Looker is super excited to announce our partnership with Heap and the creation of a new Looker for Heap SQL Block. Heap makes collecting data on web or mobile activity painless - and Looker for Heap SQL makes deploying a BI solution on top of the resulting data just as easy to deploy across an entire organization - so that everyone can answer their own questions on things like user behavior and application performance. What is Heap SQL? Web and mobile analytics can provide meaningful insights to any modern organization. Heap automatically captures user actions - clicks, taps, gestures, form submissions, page views - and allows you to add any additional custom properties you’d like without even having to touch code. What makes Heap unique is that as your analytics' requirements continue to grow and change with your business and you add new events to track
This content, written by Bruce Sandell, was initially posted in Looker Blog on Apr 2, 2018. The content is subject to limited support.Last week, Oracle announced the General Availability of Autonomous Data Warehouse Cloud (ADWC), which was introduced during the . Looker has spent the past year working closely with Oracle to ensure that we’re able support ADWC at launch, and we are happy to report that Looker provides full support for ADWC as of our 5.10 release. About autonomous data warehouse cloud ADWC has all of the key features that you’d expect in a modern data warehouse: it’s simple to provision, it’s fast, and it’s fully elastic for both compute and storage: you can scale your storage and compute up or down instantly, with no down time. ADWC also provides some advanced features that set it apart. The database is fully autonomous, meaning that is self-managing. DBA’s don’t need to worry about performance optimization techniques like indexes, partitions and materialized views. AD
This content, written by Pedro Arellano, was initially posted in Looker Blog on Aug 11, 2020. The content is subject to limited support.Last November, we announced our latest software version, , and our vision of empowering companies to build any data experiences they can imagine. With features like updated , new , , a managed data integration and database offering, and multicloud hosting options powered by kubernetes, Looker 7 delivers capabilities to support the demands of the data-driven workforce. These demands increase every day, thanks to a mainstream appreciation of the value that data can bring to a business. Twenty years ago, working with data was still limited to roles that required specialized expertise. Today, everyone needs data, and it’s hard to think of a job that can’t be enhanced with it. Digital marketers, for instance, can use data to more effectively that increase or decrease bids on online ads based on performance. Data-driven product managers can leverage data to
This content, written by Nouras Haddad, was initially posted in Looker Blog on Feb 8, 2017. The content is subject to limited support.Looker is excited to be the exclusive launch partner for DataVirtuality’s new data integration solution - . Pipes allows companies to access and integrate data from multiple databases and APIs (more than 100 and counting) into a central data warehouse that is directly leveraged by Looker. Build an agile data stack with Looker and Pipes Looker provides a next generation data analytics platform that bridges the gap between self-service, agility, governance and consistency. Most of our customers are companies operating in rapidly changing markets and undergoing hyper growth. In this sort of environment, it is crucial to build an agile data infrastructure that scales with their analytics needs; taking a few months to build a new data mart is unacceptable - by then the business needs will have evolved enough to render that mart obsolete. Traditional ETL desi
This content, written by Brian Dirking, was initially posted in Looker Blog on Sep 14, 2017. The content is subject to limited support.We are very excited today as we announce a partnership between Databricks and Looker. We have seen customers using these products together to provide an easy and intuitive way for business users to visualize and discover the powerful analytics results of Apache Spark. Using Looker and Databricks, you can experience the following benefits: Easy to Use – Make analysts productive instantly through easy to use visualizations Fastest User Adoption – Enable widespread use of analytics throughout your organization through fast user adoption Process More Data Faster - Provides the fastest implementation of Spark by using Databricks Answer Your Toughest Questions - Run the most complex analytics problems, providing answers to your toughest questions. (See this ) Bigger Insights, More Intuitively - Easy to use on your toughest problems makes bigger insights more
This content, written by Daniel Mintz, was initially posted in Looker Blog on Aug 14, 2017. The content is subject to limited support. (AWS) is immensely popular among modern companies because Amazon has found a way to strike a great balance between flexibility and usability. AWS gives you every tool you could imagine in the cloud, with none of the overhead of managing hardware yourself. With compute, storage, networking, database, and dozens of other services, AWS gives companies the flexibility and customizability to address virtually any use case. AWS’ services can be combined in almost any way you can imagine. And Amazon Redshift, AWS’ data warehouse solution, gives you tons of customization options—from specifying how your tables are distributed, to type of hardware you want, to how much computing power you allocate to different queries. Do even more with AWS + Looker So how can companies easily get the most out of all this power and customizability? That’s where Looker comes in.
This content, written by Kyle Coleman, was initially posted in Looker Blog on Sep 3, 2015. The content is subject to limited support.The top of the funnel is a numbers game. A game that becomes considerably easier when you have real-time access to all the numbers. And easier still when these numbers are consistent across every department. This consistency and reliability allows confident, data-driven decisions at the top of the funnel that can impact the entire organization. At Looker, we pipe our Salesforce.com data to our Amazon Redshift database (as well as many other data sources like Zendesk, etc), and run Looker on top for analysis. As such, we’ve almost entirely eliminated native reporting in Salesforce and instead rely on our own product. Looker queries are simple to build and , making this data readily available to anyone who needs it. One key metric we track at the top of the funnel is overall lead flow: current, retroactive, and forecasted. This allows us to properly allocat
This content, written by Pedro Arellano, was initially posted in Looker Blog on Feb 13, 2020. The content is subject to limited support.The 2020 Gartner Magic Quadrant for Analytics and Business Intelligence (BI) Platforms latest edition published this week. We believe this report is a veritable who’s who of our industry. Vendors anxiously await its release each year, while companies rely on Gartner’s research to make informed decisions when evaluating analytics and BI solutions All of us at Looker are thrilled to be 1. This is a crowded market and simply appearing on the quadrant is an important accomplishment. We thank our customers for their wonderful support and we congratulate all our peers recognized this year. We believe, the Magic Quadrant can also reveal important shifts in the industry. We believe that the vendor movement observed in this year’s edition mirrors a profound change happening in the market. According to 2, “incorporating data into your already existing cultural p
This content, written by Mike Xu, was initially posted in Looker Blog on May 11, 2015. The content is subject to limited support.Today Amazon released a brand new feature for Amazon Redshift called . The feature was designed to improve filter query performance without the need for indices or projections used by traditional databases. The result is dramatically improved average query times across diverse use cases for large multi-faceted datasets. Interleaved Sorting has significant ramifications for customers interactively querying big datasets and executing complex queries. Traditionally, analysts are restricted to a single facet that they can use for optimal query restriction and aggregation. A classic example is timeframes. Since most aggregate queries are timeframe restricted, large datasets typically use a timestamp as their primary sortkey. Interleaved Sorts removes this constraint and allows users to service a wider ranges of use cases on the same dataset. For example, in additi
This content, written by Ravi Shankar, was initially posted in Looker Blog on Aug 25, 2017. The content is subject to limited support. is a data integration and data management company that’s partnered closely with Looker. Our platform integrates data from disparate sources and uses data virtualization to create secure views of the data, as needed, in real time, and deliver it to business users. As opposed to the traditional style of data integration - Extract, Transform, Load (ETL) - Denodo is real time data integration or data virtualization, which allows you to interact with your data sources without physically centralizing it. Like Looker, we do not require users to physically move their data and this is very useful for quickly accessing new data sources for ad hoc exploration. Denodo is a data abstraction layer that goes across all of the enterprise sources, and provides a central point - as a virtual database - that can access the underlying data and provide that to a consumer. F
This content, written by Jill Hardy, was initially posted in Looker Blog on Oct 3, 2019. The content is subject to limited support.Modern food delivery amazes me. As soon as I feel hungry I can open my phone, click a few times, and food magically appears on my doorstep. It’s my lazy Sunday afternoon paradise. The Looker Action Hub is like that, but for data: it enables you to take action on information from right where you are in your workflow. As soon as you see interesting data, you can use an action to automatically create a JIRA ticket, send it to a colleague via Slack with a few clicks, or send it to an S3 bucket. See? Exactly like instant food delivery from the comfort of your couch... Okay, okay, maybe not exactly like that, but it’s still pretty great. And it’s better for your business because you can send your data pretty much anywhere your heart desires. You can even revolutionize your business with automated alerts like Alto did. Automated alerts with Slack and Twilio Ride-h
This content, written by Ryan Gurney, was initially posted in Looker Blog on Feb 23, 2018. The content is subject to limited support.Looker is continuously advancing and making improvements to its security programs, policies, and procedures. Today, we are pleased to announce that our SOC 2 Type 1 Report for the Looker Cloud Hosted Data Platform is complete and available for customers and prospects. The assessment was conducted by independent auditors, The Cadence Group, who specialize in compliance across multiple industries. The SOC 2 report includes management’s description of Looker’s trust services and controls as well as Cadence’s opinion of Looker’s system design. Looker is committed to implementing all necessary security controls, and to ensuring that our customers and prospects trust the Looker Data Platform. To that end, we are already working on our SOC 2 Type 2 report to confirm the operational effectiveness of our controls.
This content, written by Ryan Gurney, was initially posted in Looker Blog on Oct 8, 2018. The content is subject to limited support.Looker remains committed to continually improving its security and compliance practice. In September of 2018, our Service Organization Control 2 Type 2 Report for the Looker Cloud Hosted Data Platform became available for customers and prospects. The SOC 2 Type 2 assessment was conducted by independent auditors, The Cadence Group, who specialize in compliance across multiple industries. The Type 2 report addresses service organization security controls that relate to operations and compliance, as outlined by the . The report includes management’s description of Looker’s trust services and controls, as well as Cadence’s opinion of the suitability of Looker’s system design and the operating effectiveness of the controls, in relation to availability, security, and confidentiality. While our SOC 2 Type 1 , released in February of 2018, was a "test of design,"
This content, written by Pedro Arellano, was initially posted in Looker Blog on Nov 6, 2019. The content is subject to limited support.The explosion of data generated by SaaS applications is fundamentally changing the data and analytics industry. IDC1 predicts that the collective sum of the world’s data will grow to 175 ZB by 2025. To put this in perspective, if each gigabyte in a zettabyte were a brick, we could build the Great Wall of China (made of 3,873,000,000 bricks)... 258 times! This data volume and complexity is something we would not have envisioned just a few years ago, and it has been a driving force behind advancements in data and analytics technologies in recent years. Data is no longer isolated in a single monolithic software suite. It is spread out across multiple applications in the cloud. Modern databases are more powerful, faster and cheaper. The traditional ETL paradigm is giving way to data transformation on demand. But it’s not just tools and technologies that hav
This content, written by Daniel Mintz, was initially posted in Looker Blog on Oct 24, 2018. The content is subject to limited support.Software engineers know what good code looks like. It’s readable, organized, modular, version-controlled, well-tested, and it doesn’t repeat itself. It leverages work others have already done and it’s been reviewed before it ships, ensuring that it makes sense to more than just its author. Developers realized that if they were going to be building immensely complex systems in collaboration with tens or hundreds (or thousands) of colleagues, all of these principles were essential to moving their craft forward. Unfortunately, analytics has been slow to adopt similar principles. Data scientists have certainly moved things in the right direction, using R and Python to write real code that conforms to many of these principles. But most data work is still done in SQL and/or Excel, using manual, unaudited processes that waste time, impede collaboration, and lea
This content, written by Joel McKelvey, was initially posted in Looker Blog on Oct 10, 2018. The content is subject to limited support.This week, we are proud to announce the launch of Looker 6. Looker 6 takes the analytics platform to a new level with a robust set of new features, greater extensibility than ever before, and an application-focused approach designed to provide users with more value, faster. Whether you’re a Looker customer or considering the benefits of a self-service analytics platform for your organization, here’s a brief look at some of the reasons to be excited about Looker 6. 1. Advanced analyst and model developer tools. Analysts and developers are going to love how Looker 6 platform now builds upon the core services with a suite of open, web-native features designed to make building impactful data applications easier. And why should software developers have all the fun? Looker 6 provides a model development environment that includes branching, folders, version c
This content, written by Lloyd Tabb & Daniel Mintz, was initially posted in Looker Blog on Sep 14, 2017. The content is subject to limited support.When we last year, we were making a big bet on the data platform. That meant dramatically improving our developer experience, unveiling a new way to find content, and introducing the ability to send Looker data just about anywhere. Back then, Looker 4 was all about possibility. Looker 5 is about accessibility. Looker 5 takes the power of our data platform–custom visuals, custom applications, custom workflows–and makes them so easy to build and deploy that just about anyone can do it. That means it’s simple for everyone to integrate Looker into their workflow, whether it’s asking Looker questions through Slack or changing the priority of a ticket in Github through Looker. There’s something for everyone to get excited about in this release. Looker 5, the Data Platform Looker 5 makes building on the Looker data platform easier than ever b
This content, written by Lloyd Tabb & Daniel Mintz, was initially posted in Looker Blog on Oct 18, 2016. The content is subject to limited support.We are so excited to introduce Looker 4, fulfilling our vision of Looker being a complete data platform. It’s a major step forward. With Looker 4, you not only have an amazing data exploration tool, but also powerful mechanisms to deliver data anywhere it needs to go. What’s more, Looker 4 introduces the most advanced suite of tools to help get you started yet. Thanks to LookML, Looker’s centralized modeling layer, building data applications and services has never been easier. In Looker 4, LookML is coming into its own as a true language—one that allows you to completely describe your data in business terms so humans and machines can ask and answer complex questions simply. We think Looker 4 is a quantum leap forward for data analysts, explorers, and consumers. And we think that by radically simplifying the data platform, Looker gives ap
This content, written by Dan Young, was initially posted in Looker Blog on Mar 8, 2017. The content is subject to limited support.At Looker, we’ve come a long way from our humble single MySQL database and nightly extraction, transformation, and loading (ETL) jobs via cron. Today we have multiple data sources such as Salesforce, Zendesk, Marketo, etc, as well as multiple databases, including Redshift and BigQuery. Over the years we‘ve cobbled together a number of homegrown scripts running on a fleet of ambiguously named instances to assemble our analytical backend. This architecture resulted in a number of challenges that compounded over time, including job visibility, completion status, error detection and notification, logging, and one really important one; job dependencies on other jobs completion. In addition to the challenges, this workflow introduced an increased operational support load for various teams. Adding to the operational challenges, we are also consolidating more data i
This content, written by Joel McKelvey, was initially posted in Looker Blog on Dec 17, 2020. The content is subject to limited support.Retail businesses gain a competitive edge through the intelligent use of data — and ecommerce businesses are collecting more data than ever before. In many cases, this data is stored in AWS S3 buckets or in an AWS database such as Amazon Redshift. Looker is a data and analytics platform that helps retailers get the most value from their data. We’ve seen retailers leverage the combination of to discover profitable insights, closely understand customers, reduce risk and churn, and create opportunities for business growth. Vivino is one such retailer. Check out to learn how they optimized the ordering process and improved customer retention, used sentiment analysis to increase their Net Promoter Score by 30%, and ultimately stepped up to delivering the best possible customer experience — all using Looker and AWS services as retail solutions. Looker for r
This content, written by Frank Bien, was initially posted in Looker Blog on Dec 16, 2013. The content is subject to limited support.At Looker, we have the privilege of talking to lots of fast-moving companies that are trying to get value out of large data sets (yes, sometimes even “big data”). We also talk to more traditional businesses that have BI solutions in place, but are having difficulty applying these traditional approaches to their more complex (and large) data problems. Why? Traditional BI ways are broken. BI was architected in the days of big expensive databases that ran on very expensive machines. These big and expensive databases needed to be optimized at every level, because... well... they were expensive. If you didn’t optimize, things broke or you had to buy more. To run reports on data, or to do any kind of analysis, you generally pulled small pieces of aggregate (summary) data out of the big expensive database and moved it into some kind of cube or BI tool datastore.
This content, written by Rob Bailey, was initially posted in Looker Blog on Jun 22, 2017. The content is subject to limited support.The Customer Support function is playing an increasingly strategic and valuable role in companies. Today, we are excited to unveil a new powerful solution that uncovers valuable insights and trends about your customers. We have partnered with Looker to create a solution that enables companies to integrate their support team’s data into broader, company-wide insights and analysis. First, a little bit about us. Kustomer is the first customer support platform designed and built around the customer. Kustomer brings together your customer data and conversations in one place to give you a comprehensive view of your customer. Companies using Kustomer easily create a real-time data export stream of support activity into their data warehouse. The Kustomer & Looker Block Kustomer and Looker are a perfect match: Kustomer gives you one place to view all of your cu
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 Feb 10, 2014. The content is subject to limited support.Experimentation leads to discovery. Data “science” is about experimenting with data. In the data “lab,” the experiment is a transformation. And it is key to discovery Data captured in the wild, from a transactional system or event log, typically needs to be transformed before it’s useful for analytic purposes. With traditional ETL techniques, transforming and normalizing data as it moves into an analytic data store can be cumbersome and limits its future usability. A much more flexible approach is to bulk-load data into the analytic data store and then transform it at query (discovery) time, or very near query time. Looker is a highly adaptable environment for creating and deploying SQL-based data transformations—so that the results of your experiments are delivered within minutes, instead of days. What’s the best way to think about your data? Imagine you’
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