Looker Interview Questions from MindMajix is your quick guide to recap and revise all the core concepts of Looker before you attend an interview at global MNCs. Looker is gradually improving its presence in business intelligence with its powerful insights and analytics in the browser alone, without installing software. Explore the latest set of frequently asked interview questions of Looker to beat the race.
If you're looking for Looker Interview Questions for Experienced or Freshers, you are in the right place. There are a lot of opportunities from many reputed companies in the world. According to research, Looker has a market share of about 0.1%. So, You still have the opportunity to move ahead in your career in Looker Development. Mindmajix offers Advanced Looker Interview Questions 2022 that helps you in cracking your interview & acquiring a dream career as Looker Developer.
We have categorized Looker Interview Questions - 2022 (Updated) into 2 levels they are:
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Looker is a robust Business Intelligence (BI) tool that helps companies develop insightful visualizations. It has a user-friendly, browser-based workflow (so there's no need for desktop software) and allows dashboard collaboration. Users can design interactive and dynamic dashboards, schedule and automate report distribution, set custom data parameters, and employ integrated analytics, among other features.
A unique feature of Looker is its modeling language known as LookML. This lightweight, flexible markup language empowers teams to describe their data's sources, how it's shared, and how it's merged with other data. As a result, everyone in the organization can produce reports and dashboards and access a centralized data source.
Tableau creates visuals from both structured and unstructured data, and it also includes storyboarding and a spatial file connector. Looker allows you to create custom visuals from a library full of blocks with pre-made dashboard and visualization templates.
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Looker Program is a cloud-based BI application used for exploring and analyzing data. The tool aids businesses in capturing and analyzing data from a variety of sources and making data-driven decisions.
Looker allows businesses to examine supply chains, quantify customer value, market digitally, interpret customer behavior, and assess distribution operations.
Listed below are the benefits of using Looker:
The following parameters help you to know the differences between Looker and Data Studio:
Looker is a tool for creating SQL queries and submitting them to a database. Looker makes SQL queries using the LookML project, which describes the database's table and column relationships.
Although Looker does not connect directly to an Excel spreadsheet, a derived table can be used to transfer data.
Looker uses a model written in LookML for constructing SQL queries against a database. LookML is a SQL database language used to describe calculations, dimensions, aggregates, and data connections.
A derived table in Looker is a query whose results are used as simple database tables.
Let's imagine we have a database table called orders, which includes a lot of columns. We can create a derived table called customer order summary that contains a subset of the columns from the orders table.
Looker integrates with Redshift, Snowflake, BigQuery, and 50+ SQL dialects, allowing you to connect to various databases, prevent database lock-in, and manage multi-cloud data environments.
LookML, Looker's powerful semantic modeling layer, enables teams to quickly create a uniform data governance framework and empowers users to perform their analysis while staying sure that they are all based on the same single source of truth.
A model in Looker is made up of several Explores and dashboards that are coupled to each other. A model does not have a distinct "model" parameter, unlike other LookML elements. Any file defines a model in the Looker IDE's Models section (the Develop page). The model name is derived from the unique filename and must be across your instance.
Any explore declarations, and several model-level options are normally contained in a model file.
Looks are saved visualizations that a business user can build. These single visualizations are built in the Looker's explore section and are used to comprehend and evaluate data. The looks can be shared and reused in a variety of dashboards.
Looker has two ways to connect to MongoDB using the MongoDB Connector for BI:
The Looker API is a secure "RESTful" application programming interface for managing and retrieving the data from the Looker platform. You may use the Looker API to create new Looker user accounts, execute queries, schedule reports, and more.
Looker Blocks are pre-built data models for typical analytical patterns and data sources. Looker blocks can be used as a starting point for quick and flexible data modeling in Looker, from efficient SQL patterns to fully built-out data models.
Many types of Looker content, such as Looker Blocks, applications, visualizations, and plug-ins, can be found, deployed, and managed through the Looker Marketplace. By default, the Looker Marketplace feature is turned on.
Looker's Boards help teams discover curated dashboards and Looks. Dashboards and Looks can be pinned to several boards because they are kept in folders. Users can execute the following things with the help of boards:
Users will only be able to see boards to which they have been granted access. To see a board, a user must have View access. Users with Manage Access and Edit access can pin dashboards and Looks to the board, and offer context to benefit other users.
Looker makes it simple to build visuals and charts from query results. The following steps show how to create visualizations that best show off your data.
You can further modify your visualization by choosing which dimensions and measures to include.
Users can use cross-filtering to select a data point in one dashboard tile and have all dashboard tiles filter on that value. Cross-filters can be used in conjunction with conventional dashboard filters, and several cross-filters can be built at once.
Through the Looker Action Hub, the Google Sheets action is connected to Looker. Users can choose Google Sheets as a potential destination when sending or scheduling Looks or Explores after the Looker admin has enabled the Google Sheets action in the Action Hub.
Looker ML is Looker's language to describe aggregates, dimensions, calculations, and data relationships in a SQL database. Looker ML constructs a model, which Looker then utilizes to create SQL queries to retrieve the precise data you need for your business research.
A Look ML project consists of a model, view, and dashboard files managed using a Git repository. The model includes files that detail which tables to use and how they should be connected. In each table, the view offers instructions on calculating specific parameters. Dashboard files provide data with a visual appeal that makes it easier to understand.
Explore is used as a beginning point for a query in the Looker application. Each Explore can contain joins to other Explores, and each Explore can reference views. In most cases, explore should be defined in a model file.
Looker uses AES 256 bit encryption to encrypt your database connection credentials and cached data stored at rest. TLS 1.2 is also used to encrypt network data between the Looker platform and users' browsers. IP whitelisting, SSL, SSH, PKI, and Kerberos authentication are just a few of the options for securing connections to your database.
Looker takes an advanced approach to analytics, making it simple to build dependable data applications that enable users to explore, evaluate, and comprehend the data they require. Data Actions, based on comprehensive APIs, allow users to do operations in practically any other application from a single Looker interface.
A Looker dashboard is a set of queries displayed as visualizations on a page. Dashboards allow you to integrate essential queries and visualizations into a single executive view on one page. You can alter the dashboard's tiles and add filters to make it more interactive. You can make as many dashboards as you need, tailoring each one to the needs of the people who use them. Looker dashboards are divided into two categories: user-defined and LookML.
Native derived tables are created using LookML terms and based on queries you define. The explore source parameter within the derived table parameter of a view parameter is used to generate a native derived table. The LookML dimensions or measures in your model are used to build your native derived table columns.
No, the templated filter would have to be created in your new derived table. The templated filter isn't "stored" by the DT; it's part of the SQL.
No, you do not need to create a scratch schema for most dialects.
Business Intelligence is nothing but the combination of approaches that an organization uses for data analysis. The useful data can easily be generated from the bulk information that seems totally useless. The biggest benefit of generating the data is that information and decisions can easily be built up. Many organizations have attained a ton of success because of no other strategy than this. Business intelligence makes sure that one can impose a limit on the competition up to a good extent. There are several other issues that can also be eliminated by gathering very useful information from sources that seem highly unreliable.
SSIP stands for SQL server integration services. When it comes to performing some important tasks related to both ETL and migration of data, the same is widely adopted. Basically, it is very useful to enable the automatic maintenance of the SQL server, and that is why it is considered to have a close relationship with the SQL server. Although maintenance is not required regularly, this approach is highly beneficial.
These arem0 Transformations, Data Sources, and Data Destinations. Users can also define other categories in case the need for the same is realized. However, it is not possible that all the features work in that particular category.
Well, it actually depends on the business. Most of the organizations have realized there is actually no need for this. The current workforce can easily be trained, and the most desired outcomes can easily be expected. The fact is it doesn’t take a lot of time to train the employees in this domain. Because BI is a simple strategy, organizations can easily keep up the pace in every aspect.
Generally, experts prefer SQL Server Deployment. The reason is it provides quick results without compromising safety. Yes, the same is possible.
There are three modes, basically, and all are equally powerful. These are Full cache mode, partially cache mode, and No-cache mode.
Basically, this is one of the very powerful modes in which SSIS analyzes the entire database. This is done prior to the prime activities. The process continues until the end of the task. Data loading is one of the prime things generally done in this approach.
Yes, they are very closely related to the package level. Even when there is a need for the configuration, the same is done only at the package level.
DTS stands for Data transformation services, while SSIS stands for SQL Server Integration Services.
SSIS can handle a lot of errors irrespective of their complexity, size, and source. On the other side, the error handling capacity of DTS is limited.
There is actually not Business Intelligence functionality in the DTS, while SSIS allows full Business Intelligence Integration.
SSIS comes with an excellent development wizard. The same is absent in the case of DTS.
When it comes to transformation, DTS cannot compete for SSIS
SSIS support .Net scripting while the DTS support X scripting
Well, it is basically an approach that is used for exploring the details of the data that seems useful. It can also be considered to eliminate all the issues such as authenticity and copyright.
There are multiple features for logging, and they always make sure of log entries. This is generally taken into consideration when the run-time error declares its presence. Although it is not possible to enable this by default, it can simply be used for writing messages that are totally customized. There is a very large set of log providers that are fully supported by the Integration services without bringing and problem-related to compatibility. It is also possible to create log providers manually. All log entries can be written into the text files very simply and without any third-party help.
Data can easily be switched from row to column and vice versa. The switching categories related to this are considered pivoting. Pivoting makes sure that no information is left on either row or on the column when the same is exchanged by the user.
Upon adding the new rows, the SSIS starts analyzing the database. The rows are only considered or allowed to enter only if they match with the currently existing data, and sometimes it creates issues when the rows come instantly one after one. On the other side, the No Cache Mode is a situation when the rows are not generally cached. Users can customize this mode and can allow the rows to be cached. However, this is one after one and thus consumes a lot of time.
All the containers, as well as the tasks that are executed when the package runs, are considered as control flow. Basically, their prime purpose is to define the flow and control everything to provide the best outcomes. There are also certain conditions for running a task. The same is handled by the control flow activities. It is also possible to run several tasks again and again. This always makes sure of time-saving and things can easily be managed in the right manner.
It is basically a strategy that is used for arranging multidimensional data. Although the prime goal is analyzing data, the applications can also be manipulated in case the need for the same is realized. It stands for On-Line Analytical Processing.
For this, there is a file tagged as a Manifest file. Actually, it needs to be run with the operation. The same always make sure of authenticated or reliable information for the containers and the without the violation of any policy. Users are free to deploy the same into the SQL server or in the File System depending on the needs and allocation.
For hoc queries, the best available component is the OLAP engine.
These are Functionality-related tasks that are responsible for providing proper functionality to the process Containers that are responsible for offering structures in the different packages. Constraints that are considered for connecting the containers, executables in a defined sequence. All these elements are not always necessary to be deployed in the same tasks. Also, they can be customized up to a good extent.
The most commonly used tools are RapidMiner, Node XL, Wolfran Alpha, KNIME, SOLVER, Tableau, as well as Fusion Tables by Google.
Identification of records that are similar ad second is the restructuring of schemas.
This is generally called the process of slicing. Slicing always makes sure that the data is at its defined position or location, and no errors could be there due to this.
The very first thing is the right skills with the right ability to collect, organize, and disseminate big data without comprising accuracy. The second big thing should be robust knowledge, of course. Technical knowledge in the database domain is also required at several stages. In addition to this, a good data analyst must have leadership quality and patience too. Patience is required because gathering useful information from useless or unstructured data is not an easy job. Analyzing the datasets which are very large in size needs time to provide the best outcomes in a few cases.
Every container or task is allowed to do this. However, they need to be assigned during the initial stage of the operation for this.
Any general method can be applied to this. However, the first thing to consider is the size of the data. If it is too large, it should be divided into small components. Analyzing the summary statistics is another approach that can be deployed. Creating utility functions is also very useful and reliable.
It is basically an approach that is considered for proper verification of a dataset that contains independent variables. The verification level is based on how well the final outcome depends on these variables. It is not always easy to change them once defined.
It is basically a task that is executed with the help of an SSIS package and is responsible for data- transformation. The source and the destination are always well defined, and the users can always keep pace with the extensions and modifications. This is because the same is slowed up to a very good extent and users are always free to get the desired information regarding this from the support sections.
One of the biggest trouble creators is duplicate entries. Although this can be eliminated, there is no full accuracy possible. This is because the same information is generally available in a different format or in other sentences. The common misspelling is another major trouble creator. Also, the varying value can create a ton of issues. Moreover, values that are illegal, missing, and cannot be identified can enhance the chances of various errors, and the same effect the quality up to a great extent.
These are Data verification and Data screening. Both of these methods are identical but have different applications.
It is nothing but the other name of the data cleaning process. Basically, there are many approaches that are considered for eliminating the inconsistencies and errors from the datasets. A combination of all these approaches is considered data cleansing. Basically, all the approaches or methods have a similar target and, i.e., to boost the quality of data.
Yes. You can quickly build a great career with Looker, even if you're a novice or an experienced pro. All you need for a stable Looker career is to acquire the proper training and go over the top Looker interview questions, and you'll be all ready to land a job in Looker.
Looker is a promising career since it pays well and has many job opportunities, and Looker pros often have a good work-life balance. Another advantage of working with the Looker is the unlimited possibilities.
Looker is a powerful business intelligence tool that helps companies create compelling visualizations. Looker's growing popularity is due to its ability to deliver real-time data analysis and visualization. In 2019, Google purchased Looker for $2.6 billion, and it is now part of the Google Cloud Platform.
The demand for Looker professionals is at an all-time high, and it's only expected to rise further. In recent years, the number of job postings on Looker has increased. Soon, they are expected to grow even more.
According to PayScale, the average base compensation for a Looker Developer in the United States is $89k, with the average Senior Looker Analytics professional earning around $127k. Of course, the pay for a Looker pro varies based on where you work and whether you're an entry-level candidate or have more advanced analysis skills.
Following are the skills required for Looker Developer:
Although the specifics will vary based on the exact role that a person has, the Looker job role will usually include some or all of the following key responsibilities:
To a hiring manager, your answer to this question will disclose a lot about how you think about your job and the value you bring to a firm. In your response, you may describe how Looker requires a distinct set of skills and competencies. A skilled Looker Developer should be able to combine technical abilities such as parsing data and constructing models with business sense such as understanding the problems they're tackling and recognizing actionable insights in their data.
Here are the few tips to shine in your Looker interview:
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Ravindra Savaram is a Content Lead at Mindmajix.com. His passion lies in writing articles on the most popular IT platforms including Machine learning, DevOps, Data Science, Artificial Intelligence, RPA, Deep Learning, and so on. You can stay up to date on all these technologies by following him on LinkedIn and Twitter.
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