Last updated on
Jan 22, 2024

Looker Features – Table of Content

What is Looker?

Looker is a cloud-based business intelligence tool that is used to explore, share and analyze data. It helps the businesses to analyze and capture data from various sources and critical decisions. Looker provides the ability to analyze the supply chain, quantify customer value, market digitally, evaluate distribution process and interpret customer behaviour. To understand how data is manipulated, users can “view source”. It provides dashboards through which data and insights are presented using customizable graphs, charts and reports. Users can explore relationships between various datasets and define data metrics with the help of Looker’s data modeling language. Users can present data analysis via data-rich visualizations to the stakeholders with the help of the feature storytelling. With the help of Looker, you can connect to different data sources and build customized dashboards. Looker is used in various sectors like e-commerce, finance, education, construction, technology, media and healthcare.

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Why Looker?

Looker is a powerful data analytics tool that makes collecting, visualizing and analyzing data much easier. It is a browser-based tool that offers a unique modeling language. Following are some of the reason to use Looker:

  • It is simple to use
  • It works on PC as well as Mac.
  • It is Mobile, desktop and tablet friendly.
  • It has great customization.
  • It is very intuitive
  • It provides excellent customer support.
  • It provides custom install options with its hosted solutions.
  • It can be integrated with big data platform and databases
  • It offers a visualization library with heatmaps, bubble charts, chord diagrams, etc.
  • It offers analytical code blocks with SQL patterns that can be customized as per your requirement.

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Looker Features:

Looker has many beautiful features. Following are some of the features of Looker. Let us go through them.

  • Looker provides better optimize costs, manage enterprise-scale deployments better and improve performance.
  • Using inbuilt UI components of Looker, new types of data experiences can be unlocked and can speed up development workflows. 
  • Looker boosts revenue growth and improves the competitive advantage of the product at a low cost.
  • You can obtain valuable information by filtering data from the dashboard. You can also initiate data in every possible conversation with the option of finding a solution you require on the fly from slack. It provides you with the ability to compare data in multiple sources from anywhere.
  • Using Looker, you can perform Data analysis and visualization across AWS, Azure, Google cloud, and on-premise databases. It is an end-to-end multi-cloud integrated platform. 
  • Business intelligence can be operated for anybody with powerful data modeling that will abstract underlying data at any scale and will create a standard data model for the whole organization.

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  • Looker helps us to integrate analytics from anywhere at a rapid time and personalize the look and feel of our data experience and a popular integration library that controls us.
  • Lookers Augment BI with artificial intelligence, leading-edge machine learning and advanced analytical capabilities built into Google cloud platform.
  • Using Looker, you can Develop data-driven applications from supply chain logistics to sales support across various sectors via integrated machine learning and interactive data visualizations.
  • It has a feature that provides alerts to the analyst about small issues to ensure that these problems will not lead to complicated problems which are critical to solve.
  • Looker presents fully customizable and exportable graphs, reports and charts.
  • It works properly on real-time data analytics to enquire and make effective business decisions. 
  • It creates direct connections with any of the SQL database or other infrastructure. It is considered as a self-learning database that includes some self-service functionalities.
  • Looker includes customized dashboards and a browser-based interface. We can build the dashboards easily in Lookers. These dashboards are suitable to work on any device.
  • According to the queries in SQL, the data transformation in Looker takes place.
  • To define Dimensions and measures, Looker uses the extensible modeling language called LookLM.
  • Users can select visualization templates, and the forms used to create visualization are customizable. The visualization templates make our data impressive and maximize impact delivering a compelling story by using tools that allow deeper analysis.
  • Lookers dashboards and interactive and dynamic data visualization give the flexibility to drill secure data.
  • It has a good analytics feature that performs exclusive functions maintaining customized blocks.
  • For integration with SQL, Looker uses API and allows third-party applications. 
  • With the help of Looker’s simple configuration, we get more valuable data faster.
  • For data extraction, Looker uses sources like Amazon Redshift, snowflake.
  • Looker provides decision services. It helps the user to understand the functionality of the system with expert assistance, workshop facilities and training.
  • In Looker, information is converted into HTML, CSV, TXT, PNG format and stored in a container. This information in the container comes as a result of any search query.

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Conclusion: 

All the above are some of the important features of Looker. In this blog, we have covered information about Looker, why we can choose Looker and some of the important features of Looker. I hope you found the information helpful. If you think any information is missing or anything to be included, feel free to contact us or drop a comment in the comment section. Happy Learning.

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QlikView Mapping – Table of content

What is QlikView Mapping?

QlikView Mapping is incidentally creating a table (mapping table) utilizing information or field values from prior tables from various models and sources. The mapping tables are put away in QlikView’s memory just till the content is implemented and from that point forward, it is naturally removed. Mapping makes a table with arranged information fields and values whose script can be availed in various manners via statements or as functions (Rename Field, MapSubstring(), ApplyMap(), Map… Using and so forth) You can supplant field names or esteems during content execution utilizing mapping.

A Mapping table is made to plan the column values among two tables. It is also known as a Lookup table, that is simply used to search for a value from some other table. There are numerous functions accessible in this Mapping strategy to deal with the database table mapping. Mapping tables or Mapping load fills in as a choice to Join statements in the data set. The lookup value and the mapping value are the two columns in the mapping table. Mapping Tables are transitory tables as they are naturally taken out from the information model before the end of the implementation of the content. 

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QlikView Mapping Load Statement

The Mapping Load statement is utilized to stack fields and values into a newly made mapping table in QlikView. 

Syntax

Mapping(loadstatement | selectstatement)

The term Mapping is utilized as a prefix to LOAD or SELECT proclamations directing the framework to save the stacked fields in the mapping table. A mapping table includes two columns of which, the first includes values for examination (as a kind of reference point) and second includes the outcome or wanted values dependent on the correlation. For example, when country codes are used as the reference section and second resultant column is  nation names. The nation codes will be supplanted by the comparing nation names upon content implementation.

Example for mapping load in QlikView,

// Load mapping table of country codes:

MapCountry:

mapping LOAD * 

Inline [

CountryCode, Country

Sw, Sweden

Ind, India

Chn, China

Ity, Italy,

Cnd, Canada

Dk, Denmark

No, Norway

];

Along these lines, this will stack a mapping table called MapCountry which has two columns, Country and CountryCode. The elements of the given mapping tables can be utilized in turn by planning statements and other operations like ApplyMap. 

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QlikView ApplyMap Function

The QlikView ApplyMap() function brings content from a current mapping table. It also maps the outcome of an expression to a current field from the mapping table. 

Syntax

ApplyMap(‘map_name’, expression [ , default_mapping ] )

Here, map_name is the name given to the pre-existing mapping table. The expression is the field whose outcome must be mapped to a mapping table field. default_mapping is the value, whenever referenced will be returned if there is no match of the field esteems from the current mapping table. The worth is returned all things considered in the output table, if not referenced.

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Example of Qlikview ApplyMap Function

Allow us to improve comprehension of this by the assistance of an example. Consider the one with a similar nation code example as we utilized in the mapping load area.

// Load list of store managers, mapping country code to country

StoreManagers:

LOAD *, 

ApplyMap(‘MapCountry’, CountryCode,’Others’) As Country

Inline [

CountryCode, StoreManager

Sw, John Dalton

Cnd, Mary Robins

Ity, Andrea Russo 

Arg, Harry Gibson 

Dk, William Gilbert

Ind, Indrani Sen

No, Daniel Larsen

Rom, Emilia Mark];

// We don’t need the CountryCode anymore

Drop Field ‘CountryCode’;

The resultant table will look as given below.

From this table, the name of the nation relating to the nation code composed next to the store supervisor name is mapped into the last table utilizing an applymap function. Likewise, those nations (like Arg and Rom) for which there were no reference country names in the table “MapCountry” are composed as “Others”. 

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Benefit of utilizing Mapping table and ApplyMap Function: 

  • Aids in dodging joins in QlikView. 
  • Aids in diminishing tables from the data model to simplify it, productive and justifiable. 
  • Mapping Table is only there during load. 
  • Missing rows can be dealt with by its third parameter  consequently aids in taking care of the null qualities and utilized in information integrity in ApplyMap Function.
  • It disregards the duplicate rows in the table. 
  • We can utilize composite keys as a critical column in the table. 
  • We can make different maps from a similar table. 
  • It saves processor time and memory. 
  • It cleans the information by eliminating the information disparities.

QlikView Architecture

QlikView is developed with a totally different way to deal with information disclosure than other conventional platforms. It doesn’t initially assemble an inquiry and afterward bring the outcome dependent on the query. It structures relationships between various data objects when it is stacked and prompts the client to investigate the information in any capacity. The information drill down ways can occur toward any path as long as the information is accessible and related. A client can assume a part in making the connection between information components utilizing information modeling approach accessible in QlikView.

Qlikview Mapping Architecture

QlikView’s architecture comprises a front end to picturize the prepared information and a back end to give the security and distribution component for QlikView client records. The image given above portrays the inside working of QlikView. 

Front End

Front end in QlikView can be defined as a browser oriented access point for reviewing the QlikView archives. It includes the QlikView Server, that is fundamentally utilized by the Business clients to get to already made Business Intelligence reports via a web or intranet URL. Business clients investigate and interface with information utilizing this front end and determine decisions about the information. They team up with different clients on a given arrangement of reports by sharing bits of knowledge and investigating information together, continuously or offline. These client archives are in the configuration .qvw, which can be put away in the windows OS as an independent record. The QlikView server in it deals with the customer server correspondence between the client and QlikView backend framework.

Back End 

The QlikView backend comprises QlikView publisher and QlikView desktop. The QlikView desktop can be considered as a wizard-driven Windows environ, that has the highlights to stack and change information from its source. Its simplified element is utilized to make the GUI format of the reports which gets noticeable in the frontend. The record types that are made by the QlikView desktop are put away with an extension of .qvw. These are the documents which are given to the QlikView server in the front end, that serves the clients with other records. .qvw documents can be adjusted to store the information only records, called as .qvd records. They are records that include just the information and not the GUI parts. The QlikView publisher is utilized as a circulation service to convey the .qvw reports among different QlikView servers and clients. It manages the approval and access advantages. It does the immediate stacking of information from the data sources by utilizing the association strings characterized in the .qvw records.

Highlights of QlikView planning 

Some significant highlights of QlikView planning are: 

  • It is like the query function where a field esteem is utilized as a source of reference to another field existing in the mapping table and returns a coordinating outcome. 
  • A mapping table is always made by Mapping Load or Mapping Select prior to applying a Map work. 
  • A mapping table stays briefly in the memory of QlikView till the next time content implementation is finished. It is exited once a content is executed. 
  • The transitory mapping table doesn’t influence the fundamental information tables put away in-memory of QlikView. 
  • Any mapping table can be reviewed and reutilised in a content however many occasions as a client needs. 
  • The principal field is known as a key and the subsequent field or column is alluded to as mapped value ALWAYS. 
  • All the planning necessities can be done in a solitary script.

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Conclusion

The blog dealt with QlikView mapping with QlikView ApplyMap() and mapping load. We get to know more about mapping, how to make mapping tables utilizing the Mapping Select statements or Mapping Load lastly and how to utilize mapping via the ApplyMap() work.

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