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.
Organizations small and large, carry out several processes or transactions which will result in generating humongous data. The data holds valuable information that could help improve business. That’s where the Business Intelligence tools come into the picture and help us explore data in meaningful ways. Processing the data in time and proper reporting enhances the ability to make more informed and data-driven decisions.
Looker's business intelligence software helps in exploring and analyzing data. We can combine data from different sources and create a unified view. We can then build real-time analytics on top of the data and share them easily. It offers great visualizations and drill-down dashboards.
Here are some of the many advantages of Looker BI.
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.
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:
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.
The following parameters help you to know the differences between Looker and Data Studio:
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.
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.
The full form of SSIS in SQL Server Integration Services. SSIS is an element of the Microsoft SQL Server that we use to generate the workflows for data migrating tasks. It is an ETL tool used for retrieving data from different sources and, after that, transforms and loads the data into various destinations.
There are different kinds of data flows they are:
In Looker, we have three cache modes they are:
Pivoting is defined as a mechanism of shifting the data from column to row and vice versa. Pivoting assures that no data is abandoned in either column or row while exchanging the same data.
Online Analytical Processing is an approach that we use to organize multidimensional data.
For data analysis, we can deploy the following tools:
Drilling is an approach that we use for studying the data details that is useful. We can also consider it for removing the issues like authenticity and copyright.
Following are the critical steps of the analytics project:
We have six types of looker blocks, they are:
The full form of NDTs is Native Derived Tables. We can create the NDTs by specifying the explore parameter on the base table through desired columns.
Tableau provides data security at any level.
In Looker, we have to change the security settings based on our requirements.
Looker supports the following operating systems:
DTS refers to Data transformation services, while SSIS refers to SQL Server Integration Services.
| DTS | SSIS |
| The error handling capabilities of DTS is restricted. | SSIS handles plenty of errors regardless of source, size, and difficulty, |
| In DTS, we don’t have business intelligence functionality. | In SSIS, we have complete business intelligence integration. |
| DTS does not have a development wizard. | SSIS has an excellent development wizard. |
| DTS supports X scripting. | SSIS supports the .NET scripting. |
We use no-cache mode when the reference data is very huge for loading into the memory. We use the partial cache mode when the data site is comparatively low. The lookup index in the partial cache mode provides rapid responses.