In this Looker Tutorial journey. Discover ins and outs of Looker with our comprehensive tutorial how to unlock the full potential of your data with step-by-step guidance, best practices, and expert tips. Start making data-driven decisions today!”
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What’s Looker?
- As a part of the Google Cloud Platform, Looker empowers everyone in your organization to easily search and uncover insights from your data. With its intuitive and meaningful data visualization, Looker makes it simple to create a centralized platform for data exploration.
- Utilizing the power of Data Modelling Language (DML), Looker streamlines your data analysis process and allows you to connect with multiple data sources. Build custom dashboards, KPI trackers, and more with Looker’s flexible framework.
- From modern BI and embedded analytics to integrating workflows and custom apps, Looker has the tools to enhance your data experiences. No matter where your data resides, Looker provides you with real-time access to the most current version of your company’s data. Upgrade your data analysis game with Looker today.
Looker and Google Cloud Platform (GCP): A Powerhouse Partnership for Data Analytics
Features of Looker:
Break down barriers to insight
You can get valuable insights that you simply need by filtering to granular slices of knowledge from the dashboard with a few clicks. Furthermore, you’ll initiate data in every conversation possible with the power to seek out the solutions you would like on the fly, right from within Slack.
Increase performance and optimize costs
With Looker’s powerful features, you’ll improve performance, optimize costs, and manage enterprise-scale deployments better.
Build better data products faster
With prebuilt UI components, you’ll unlock new sorts of data experiences and speed up development workflows. At a coffee cost, Looker improves your product’s competitive advantage and boosts revenue growth.
Apart from this, we’ve many other new features.
Integrated end-to-end multi-cloud platform: you’ll perform data analysis and visualization across Google Cloud, AWS, Azure, and on-premise databases.
Common data: you’ll operate business intelligence for anyone with powerful data modeling that abstracts underlying data at any scale and creates a typical data model for the whole organization.
Embedded data experiences: Looker supports embedded analytics with rapid value to time and customization.
Augmented analytics: Augment enterprise intelligence from Looker with leading-edge device knowledge, AI, and developed analytic abilities constructed into the Google Cloud Platform.
Tailored data applications: Develop data-centric apps from supply chain logistics to sales assets across different initiatives with embedded machine learning and interactive data visualizations. We recommend you to observe this Looker Tutorial Video learn Looker easily.
List of the key areas that Looker covers, to give you a better understanding of the platform:
- Data Modeling: Looker provides a data modeling layer that enables organizations to create a semantic layer over their raw data, making it easier to understand and analyze.
- Data Exploration: Looker provides a powerful data exploration interface that enables users to quickly explore and understand their data, with the ability to create custom dashboards and reports.
- Data Visualization: Looker provides a wide range of data visualization options, including charts, graphs, tables, and maps, to help users understand and communicate data insights.
- Data Sharing: Looker provides tools for sharing insights and reports with others in an organization, with the ability to control access and set permissions.
- Data Governance: Looker includes tools for data governance, such as data definitions, data lineage, and data validation, to help ensure data quality and consistency.
- Data Transformation: Looker provides a data transformation engine that enables users to transform and clean their data without writing any code.
- Custom Development: Looker provides a platform for custom development, with the ability to create custom integrations, extensions, and plugins to meet specific business requirements.
- Collaboration: Looker provides tools for collaboration, such as discussion forums, comment threads, and project management, to help teams work together on data projects.
- Integrations: Looker provides integrations with a variety of third-party tools and platforms, including data warehousing solutions, analytics tools, and cloud-based services.
Looker Architecture:
he architecture is composed of several key components:
- The Looker application, which is a web-based interface that users interact with to explore and analyze data.
- The Looker database, which is a relational database that stores the metadata and data model for the Looker application.
- The Looker back-end, which is a collection of servers and processes that handle the processing and querying of data.
- The Looker data connectors, which allow Looker to connect to various data sources, such as databases, data warehouses, and cloud services, and retrieve data for analysis.
- The Looker caching layer, which is used to improve performance by caching frequently accessed data.
This architecture allows Looker to provide a seamless, intuitive, and highly performant data exploration experience to its users.
Looker Tutorial: What is a LookML?
LookML (Looker Modeling Language) generates abstracted SQL and provides a modeling layer between the database and the user. It is Looker’s proprietary language that provides an abstraction layer for SQL databases.
Specifically, LookML is a language for describing dimensions, aggregates, calculations, and data relationships in a SQL database. Looker uses a model written in LookML to construct SQL queries against a particular database. It creates the layer between that SQL database and how the business user interacts with it.
As such, it defines many different things, like how to join tables, how to define custom tables, how to define fields from the database and the logic for new fields. In this lab, you will get hands-on experience with the fundamentals of LookML.
Overview of LookML structures
The hierarchy of LookML is structured using the following objects:
- Projects, which are libraries of LookML code. Because Looker uses Git for version control, a best practice is for each project to map 1:1 with a Git repository.
- A project is composed of one or more models.
- A model is a set of Explores by business area or need. An Explore is a set of pre-joined views for business-user analysis.
- Each model contains one or more Explores.
- A view in LookML is a database table or a logical representation of one.
- Each view includes dimensions (which are database columns or logical representations of them) and measures (which are aggregate functions on dimensions, such as a COUNT of customers or a SUM of cost).
- Each view includes dimensions (which are database columns or logical representations of them) and measures (which are aggregate functions on dimensions, such as a COUNT of customers or a SUM of cost).
Looker Tutorial: How Looker Writes SQL?
Looker writes SQL queries directly against your database based on the fields and logic that you define within the LookML. Any user in the organization can build their own reports and dashboards using SQL queries without actually knowing how to write SQL. Since you’re going to be writing the LookML that controls this SQL, it’s important to understand how Looker generates SQL so that you can write the most efficient queries.
Using Looker’s modeling language, known as LookML, you define your business logic based on columns in your table. In the example below, the names of users are stored in a column in the database and can be represented with the following LookML dimension. When you add a dimension field to Explore, Looker constructs a SQL query that includes that field and sends the query to your database.
When you add a dimension field to Explore, Looker constructs a SQL query that includes that field and sends the query to your database.
Here is an example to demonstrate how Looker writes SQL in practice.
Looker Tutorial: How does Looker work?
Looker is a tool that uses SQL to get the queries and submit them against the database connection. SQL queries are formulated to support the LookML project that has precise relationships between the columns and tables within the database. The subsequent steps describe the working of Looker.
Viewing the query: The SQL tab is employed within the data section to see and review what Looker sends to the info base to retrieve the data. The query is often viewed within the SQL Runner, which can be available within the sort of links.
The canonical sort of Looker query: The looker query is often represented during a canonical form by defining the measures, explores, views, and dimensions. Filter expressions also can be laid out in Looker.
Running Raw SQL: Looker uses SQL Runner, a singular feature that permits you to run any SQL against the database connections found out in Looker.SQL Runner. It helps in investigating the query as every query generated in Looker may be a complete functional SQL command.
Exploring the URL’s query features: Reading the URL will deliver the five fundamental factors after executing the SQL query. These features contain the model, explore, fields to question and display, sort field and order, filter fields, and values.
Installation of Looker: To perform any operation or functionality in Looker, Looker has got to be installed. The installation process varies supported by the hosting selected. The installation process of customer-hosted Looker is described below:
What are the benefits of looker?
Looker is built on modern web architecture, it provides the power of your own database (rather than using an internal analytics engine). The modern web architecture means that Looker hosted in the cloud and it runs entirely in-browser, so there’s no desktop install.
Looker offers amazing business representation and insightful instruments: diagrams, graphs, tables, and single numbers.
Looker operates entirely on the data in your database. It has no analytics engine of its own—it generates SQL based on the semantic layer you design—sends that off to the DB and then displays the results in tables or graphs.
Each Analysis can be saved in a ‘Look’ and added to a dashboard for reporting purposes. We utilize these dashboards for KPI reports which can be scheduled on a day by day, week by week or month to month.
Looker Installation
These are the steps you will need to follow to install Looker for a customer-hosted Looker Deployment:
1. Looker Installation
- Add Looker to your server
- Looker configuration startup options
- SSL certificate configuration for proper HTTPS
- Forward port for a cleaner URL
- Enable Looker support to access your instance
- Set up Looker monitoring and backups
- Ensure that Looker can access important services
- Install rendering software
- Check whether your Looker instance can hold the Looker Action Hub
- Allowing secure database access
2. Configuring database for Looker
3. Connecting Looker to your database
4. Testing database connections
5. Configuring Looker sign-in options
Looker Tutorial: What are Looker Blocks?
Looker Blocks are building blocks–pre-built pieces of code that you can leverage to accelerate your analytics. From optimized SQL patterns to fully built-out data models, custom visualizations to weather and demographic data, explore all the Looker Blocks® today as the starting point for quick and flexible analytics in Looker.
- Analytic Blocks
- Source Blocks
- Data Blocks
- Data Tool Block
- Viz Block
- Embedded Block
- Actions
| Name of the Looker Block | Usage |
| Analytics Blocks | Best practice design patterns for various types of analysis. |
| Source Blocks | The third-party data source to analytics |
| Data Blocks | Pre-modeled public data |
| Data Tools | Multiple techniques for data analysis types |
| Viz Blocks | Custom visualization types to represent query output. |
| Embedded Blocks | Techniques to embed data into custom apps |
Looker Tutorial: Looker Applications
Looker is a business intelligence and data visualization platform that helps organizations to make data-driven decisions. With Looker, organizations can explore, analyze and share data insights easily and securely.
Some of the popular applications of Looker include:
- Data Warehousing: Looker integrates with a wide range of data warehouses, such as Amazon Redshift, Google BigQuery, and Snowflake, to allow users to explore, analyze, and visualize large datasets.
- Dashboards and Reports: Looker provides a flexible and intuitive platform for building interactive dashboards and reports that can be easily shared with stakeholders.
- Data Exploration: Looker’s data exploration tools allow users to easily query and analyze large datasets, discovering insights and correlations that would be difficult to find through other means.
- Data Governance: Looker provides robust data governance and security features, allowing organizations to manage user access, enforce data privacy regulations, and ensure data accuracy.
- Data-Driven Decision Making: Looker empowers organizations to make informed, data-driven decisions by providing them with the insights they need to make informed business decisions.
Overall, Looker is a comprehensive and powerful platform that helps organizations to gain insights from their data, make better decisions, and drive business success.
1. Sales analytics
- You can organize account leads, contacts, and opportunities quickly, right out of the box.
- You can handle every step of your sales with governed and customized workflows.
- You can combine data from different systems to obtain meaningful insights that can benefit your stakeholders.
- It helps you maximize retention and upsell, and at the same time, it minimizes churning.
2. Digital marketing analytics
- You can customize powerful reports around components such as Google Ads, Facebook Ads, etc.
- It helps you grasp ad spend across different mediums with a unified dashboard that is a cross channel.
- You can make informed bid changes that are too on the spot related to updates of active ad performances.
3. Web analytics
- You can support your teams by providing access to trusted data from Google Analytics that is shareable.
- You can also dig deeper in your website with cross-property essential analysis, create schedules, dynamic cohorts, etc.
- Through a single point of truth, you can sharpen visibility into sales and marketing.
Looker Alternatives and Competitors
Top competitors to Looker for analytics and business intelligence platforms are Tableau, Qlik, Sisense, MicroStrategy, PowerBI, etc.
Looker Tutorial: Why is Looker better compared to business intelligence tools?
The below section compares top business intelligence tools and explains why Looker stands out.
1. Tableau vs. Looker
Differences Between Looker vs Tableau
What Looker is: Looker is a data-discovery platform that helps companies make better business decisions through real-time access to data. No matter the size, data can be analyzed within Looker’s 100% in-database and browser-based platform. Looker analytics integrate with any SQL database or data warehouses, such as Amazon Athena, Green plum and Microsoft Azure SQL Data Warehouse.
What Tableau is: Tableau offers robust BI tools that enhance data visualization and discovery for all types of organizations and business users. With simple drag-and-drop features, users easily analyze key data, share critical insights across the enterprise, and create innovative visualizations and reports. In addition, Tableau offers the option to embed dashboards into existing business applications such as Jive, SharePoint, and Salesforce.
Key Comparison by performance Looker vs Tableau
Below are the lists of points, describe the key Differences Between Looker vs Tableau
- Data collection, storage, and access
Both the Looker and Tableau tools do not offer data collection and storage but we can connect to the data sources like SQL from either of the tools and access data. In-ground tableau provides support for a large number of data sources compared to looker.
- Data modeling
Data modeling is the process of taking raw data as input cleaned, combined, converted and made ready for data analysis. data modeling can be carried out in ad hoc or publish all these data models in a platform.When it comes to data modeling Looker’s LookML data modeling layer is its core strength with a wider margin.
- Data visualisation
Looker provides data visualization features, channeling numbers into charts and graphs. These are displayed in various real-time dashboards that users can check at a glance. And Looker’s integration with products from companies like Google and Amazon Web Services makes it even more powerful. Numerous vendor tools and applications parse and store data in particular ways, but Looker is seen as a leader in the process of taking in raw data and refining it.
- Self Service BI
Looker offers all of your business users self-service capabilities. Whenever user need to see it and analyze it, he can see and analyze it. Additionally, you can provide external self-service access to your dashboards and other data, visualized or not.The self-service business intelligence component that Tableau brings to the table is also pretty powerful. All of your users, no matter what their background, can act as a data scientist. A drag-and-drop interface makes analyzing your data simple when using the Tableau system. And the self-service capabilities don’t provide surface analysis. The analytics allow your users to get down into the nitty-gritty details of their data if they so choose, so they can find the insights that they’re trying to find.
- Deployment
Deployment isn’t the biggest determinant of a BI vendor’s value, but it does matter for businesses thinking of adopting one. Tableau offers several different products, of which there are various deployments. Tableau Desktop and Tableau Server are both on-premise, using your business’ servers. Tableau Online, as the name suggests, is a web-based platform operating in the cloud. Looker, on the other hand, has an exclusively cloud-based deployment.
2. Power BI vs. Looker
Power BI is a cloud offering, which can be deployed only on Microsoft’s Azure public cloud environment, and that limits future flexibility. Looker’s multi-cloud flexibility overcomes this challenge by offering modern cloud and on-premise SQL database solutions by future-proofing your data strategy.
3. Sisense vs. Looker
Sisense’s ElastiCube architecture creates complex deployments as users attempt to scale. With Looker’s centralized modeling layer, you can define business rules that can be accessed by both users and downstream processes.
4. MicroStrategy vs. Looker
MicroStrategy’s centralized semantic layer needs skillful IT resources that drive bottlenecks and limit the activity. LookML centralized, accessible modeling layer of Looker allows more users to collaborate.
5. Qlik vs. Looker
Both QlikView and Qlik Sense need data extraction to a proprietary in-memory database. Looker’s centrally managed modeling layer promotes dimensions reusability and checks as business rules only need to be determined once without extracts.
Looker Pricing
Looker publicly doesn’t release pricing information. Instead, it offers a customized plan based on the number of users and scale of deployment. Pricing is designed to fit businesses of all sizes. Looker pricing specialists work directly with you to ensure an ideal business pricing structure for your business.
Looker pricing starts at $3,000.00 for the entire data platform, according to a third-party website.
Looker Certification
A Looker certification is a valuable industry credential that demonstrates your technical skills. The exams use industry best practices and standards to assess whether Looker’s proficiency standards are met.
The following exams are now available:
1. LookML Developer
This exam covers LookML code development, maintenance, troubleshooting, caching policies, and data modeling best practices. This exam expects that you are comfortable with SQL and have at least 3+ months of experience with these skills before you register.
2. Looker Business Analyst
With this exam, you can learn about report development in Looker, visualization of data, and best practices of the dashboard. It is recommended to have 5+ months of experience with these skills to clear this exam.
Why should I get Looker certified?
By becoming Looker certified, you will:
- Become an expert within the data community
- Show your potential with Looker knowledge
- Add the skills to your resume by clearing the certification exam
- Get something that you can show off, and other Looker perks
What are the prerequisites for the Looker Certification exams?
While we highly recommend Looker training and on-the-job experience, there are no prerequisites for taking Looker certification exams. Please review the exam details for the exam you want to take for more information about the exam covers and the suggested training.
Looker Interview Questions And Answers
Conclusion
Looker is definitely a tool to watch out for the features and the robustness that it provides. I hope that you now have a brief idea about Looker and its objective. With this, we have come to the end of this blog. I hope you have enjoyed reading it. If you have any queries related to this blog, you can write them in the comments box below. We will be more than happy to resolve them. Thank you and Happy Learning!
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About Me:-
I am Om Prakash Singh – Data Analytics Consultant , Looker Consultant , Solution Architect .
I am Highly analytical and process-oriented Data Analyst with in-depth knowledge of database types; research methodologies; and big data capture, manipulation and visualization. Furnish insights, analytics and business intelligence used to advance opportunity identification.
You’ve got data and lots of it. If you’re like most enterprises, you’re struggling to transform massive information into actionable insights for better decision-making and increased business results.
Reach out to us if you are interested to evaluate if Looker is right for you or any other BI solution.
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