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looker.com

Founded Year

2012

Stage

Acquired | Acquired

Total Raised

$280.5M

Valuation

$0000 

Revenue

$0000 

About Looker

Looker provides a SaaS unified platform for data that delivers business insights to every employee at the point of decision. It delivers applications through its platform, including Business Intelligence, business-specific solutions such as Digital Marketing Analytics and Web Event Analytics, and a flexible, embedded analytics framework. The solution also integrates data into the daily workflows of users to allow organizations to extract value from data at web scale.

Looker Headquarters Location

101 Church Street 4th Floor

Santa Cruz, California, 95060,

United States

831-244-0340

ESPs containing Looker

The ESP matrix leverages data and analyst insight to identify and rank leading companies in a given technology landscape.

EXECUTION STRENGTHMARKET STRENGTHLEADERHIGHFLIEROUTPERFORMERCHALLENGER
Insurance / P&C Insurance Tech

Business intelligence & analytics systems provide dashboards and other visualization tools that can let underwriters better analyze the data involved in their underwriting submissions. This market consists of companies with customizable visualization tools that can be utilized by any industry, as well as those with underwriting-specific capabilities.

Looker named as Leader among 12 other companies, including Qlik, SAS, and ThoughtSpot.

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Research containing Looker

Get data-driven expert analysis from the CB Insights Intelligence Unit.

CB Insights Intelligence Analysts have mentioned Looker in 4 CB Insights research briefs, most recently on Feb 24, 2021.

Expert Collections containing Looker

Expert Collections are analyst-curated lists that highlight the companies you need to know in the most important technology spaces.

Looker is included in 2 Expert Collections, including Tech IPO Pipeline.

T

Tech IPO Pipeline

286 items

C

Conference Exhibitors

5,302 items

Looker Patents

Looker has filed 10 patents.

The 3 most popular patent topics include:

  • Data management
  • Database management systems
  • SQL
patents chart

Application Date

Grant Date

Title

Related Topics

Status

12/18/2018

4/19/2022

Cryptography, Key management, Disk encryption, Data security, Computer security

Grant

Application Date

12/18/2018

Grant Date

4/19/2022

Title

Related Topics

Cryptography, Key management, Disk encryption, Data security, Computer security

Status

Grant

Latest Looker News

How LeaseLock Builds Successful Data Workflows

Sep 26, 2022

There’s no one-size-fits-all approach to building a robust workflow. But some tools are better than others. Written by Brendan Meyer If you have a hammer, everything can look like a nail. People tend to over-rely on the tools they’re most familiar with, even if superior tools are available. When it comes to building workflows in the data science industry, this sentiment is especially true. Why? There is no one-size-fits-all approach to solving any given data science problem, which means data scientists who rely on tried-and-true tools to build workflows might be missing out on simpler and more effective methods. “In the data realm, software engineers usually neglect some steps in their workflow,” Amir Katoozian, data engineering manager at LeaseLock , said. “Adding those steps to a data science workflow requires time and a certain level of software engineering expertise. It will also contribute to a more robust workflow.” Learning which tools and processes that companies use to solve different problems can be enormously helpful to data scientists. That’s why Built In LA sat down with Katoozian to get the inside scoop on his processes. What are your favorite tools that you’ve used to build your data science workflow? Airflow enables the orchestration of the data engineering pipeline using directed acyclic graphs (DAGs). LeaseLock uses dimensional modeling data structure to create ready-to-use data assets. This model consists of roughly 20 dimension table tasks and about six fact table tasks. Airflow helps manage how these tasks run in relation to each other. At LeaseLock, Airflow’s two main use-cases include parallelizing independent tasks for faster execution and managing dependencies. That way, a task only runs after all of the upstream tasks are successfully finished. Looker is a business intelligence (BI) and visualization tool that enables non-technical users to easily create their own customized reports. The main advantage of Looker is that you can define the relationship between a group of tables in your database in what is called an “explore.” You can also define metrics, which are called measures, using one or more tables. The easy-to-digest Looker user interface (UI) allows anyone to pull the columns and metrics and create a custom report. LeaseLock uses dimensional modeling data structure to create ready-to-use data assets.’’   What are some of your best practices for creating reproducible and stable workflows? Using a continuous integration and continuous delivery (CI/CD) platform is important to make sure your production environment is running smoothly. At LeaseLock, we use CircleCI as our CI/CD tool to deploy any code changes. You can use a CI/CD platform to test installation of dependencies and set rules around linting and formatting. You can also run tests such as unit testing and generate coverage reports. You can also package up your application (eg. a Docker image) and deliver to the production platform. Using a version control platform such as GitHub allows us to track code changes and identify the cause of failures in a workflow. It also enables us to set merge rules around pull request titles, description of changes, mandatory reviews, etc. All CI/CD platforms can and should be integrated with GitHub. The combination of the two is the key to a reproducible and stable workflow. What advice do you have for other data scientists looking to improve their workflows? In the data realm, software engineering teams usually neglect some steps in their workflow. Adding those steps to a data science workflow requires time and a certain level of software engineering expertise, but will contribute to a more robust workflow. QUICK TIPS Unit testing: Test all functions and logics by creating hypothetical test cases to make sure your function/logic works as expected. Python has a library for this called pytest. Integration testing: Compare the output of a modified workflow with the output of the original workflow to ensure the changes are aligned with your expectations. Version control: Many data science teams ignore version control and instead focus more on the science and math behind the solutions they provide. Adding Git to your workflow might seem like a headache that adds extra steps, but its benefits are absolutely worth the hassle. Slowing down and taking some time to execute the process correctly yields reproducible and more accurate results! LeaseLock’s real estate insurtech platform creates more valuable and efficient rental properties for owners and operators

  • When was Looker founded?

    Looker was founded in 2012.

  • Where is Looker's headquarters?

    Looker's headquarters is located at 101 Church Street, Santa Cruz.

  • What is Looker's latest funding round?

    Looker's latest funding round is Acquired.

  • How much did Looker raise?

    Looker raised a total of $280.5M.

  • Who are the investors of Looker?

    Investors of Looker include Google, Redpoint Ventures, Meritech Capital Partners, Kleiner Perkins Caufield & Byers, Goldman Sachs and 10 more.

  • Who are Looker's competitors?

    Competitors of Looker include Yellowfin, Observable, InsightSquared, ThoughtSpot, Hyper Anna, Sisu, Locale, GoodData, MicroStrategy, Kyligence and 23 more.

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