Looker Documentation | Step by Step Guide to Learn LookML


Looker Documentation – Table of Content

What is a looker?

Looker is a big data analytics platform and business intelligence software that allows you to easily explore, analyze, and share real-time business analytics.

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Why looker?

The Looker platform unleashes the importance of your data, allowing you to deliver impactful insights and create data experiences for every part of your organization. Business executives, data analysts, application developers, data scientists, customers, and business partners are all examples of people who work with data. Looker’s capabilities can help anyone who needs data to do their job.

Looker documentation

Documentation is very essential for any software or tool to analyze it to fullest. The documentation reveals all the prospects, functionalities, operations and tasks, involved modules in that particular tool.In this looker documentation tutorial we are going to cover all the concepts in depth such as looker features, organizing the content, creating the dashboards, lookML, and explore on how to make it ready for the development, etc.

Next we will go through the complete looker documentation in a more detailed way.

Finding and organizing the content:

Looker gives you access to the data in your organization. Many users begin by perusing the reports which we call Looks and dashboards created by others in their organization. You will learn how to create content and organize it to meet your needs in this section.

Finding and viewing the content:

  • Finding Content in Looker: Locate content created by others, such as Looks and dashboards.
  • Viewing and Interacting with Dashboards: View and interact with dashboards.
  • Viewing Looks : Browse and interact with previously saved reports (which we call Looks).

Organizing the content:

  • Using Folders to Organize : Discover how to move, copy, and delete items within folders.
  • Using Boards to Present Content : Create boards that display curated sets of Looks and dashboards for your various teams.
  • Deleted and Unused Content for Administrators: Discover how you, as an administrator, can clean up deleted and unused content.

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Sending and sharing the content:

One of Looker’s benefits is the variety of ways you can send and share content with colleagues and others outside your organization. You will learn how to send and share content in this section.

  • Data Sharing: Learn how to share Looker data with others.
  • Data Sharing via URLs: Share data via browser, short, or expanded URLs.
  • Downloading Content: Download and format data or visualizations from Looks and Explores.
  • Public Sharing, Importing, and Embedding of Looks : Provide public access to data via URLs, embedding, or spreadsheet placement.
  • Delivering content with the Looker Scheduler: Deliver Explores, Looks, legacy user-defined dashboards, and LookML dashboards instantly or on a recurring basis to email, webhook, Amazon S3, SFTP, and some integrated services.
  • Dashboard Scheduling and Distribution: Schedule and distribute dashboards via email, webhook, Amazon S3, SFTP, and some integrated services. Schedules can be edited, deleted, or duplicated.
  • Scheduling Deliveries to the Slack Integration :Use Looker’s Slack integration to schedule Looker data delivered directly to Slack.

Retrieving the data and turning into charts:

Many users want to take the next step after learning how to browse through other people’s reports and dashboards and learn how to query data for themselves. This enables them to use their organization’s data for their own purposes.

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Retrieving data:

  • Exploring data in Looker : Discover how to use Looker to build queries, display results, and discover insights.
  • Filtering and limiting data: Limit your results to the information you’re looking for.
  • Merging Explore results: Combine information from multiple queries.
  • Looker filter expressions: Discover the patterns of Looker’s filter expressions.
  • Looker functions and operators : Examine the functions and operators that appear in Looker expressions.
  • Adding custom formatting to numeric fields: In Looks and charts, apply Excel-style formatting to numeric data.

Related Article: Looker Data Visualization

Customizing the data:

  • Using table calculations : Add new fields by calculating the results of your query.
  • Adding custom fields: Create new field picker options by adding custom dimensions and measures.
  • Creating Looker expressions: Use Looker expressions to perform custom calculations.
  • Looker functions and operators: Learn about the functions and operators that are used in Looker expressions.

Charting the data:

  • Creating visualizations and graphs: Based on the results of your queries, create visualizations and graphs.
  • Types of visualization: Discover Looker’s native visualization types.
  • Color palettes: View the palettes for Looker’s pre-installed color collections.
  • Chart time formatting: Examine the time formatting options for Looker charts.

Creating reports and dashboards:

Many users want to learn how to create and edit their own reports which we call Looks and dashboards after learning how to query and chart data. This enables them to organize and present data in a logical and cohesive manner.

  • Creating and editing the saved reports.
  • Creating and editing the saved dashboards.
  • Creating, enabling and filtering cross filtering dashboards.
  • Creating and managing LookML dashboards, etc.

Related Article: Looker Analytics

Looker development environment:

  • To unlock Looker’s magic, data experts at each organization describe their data in LookML, a lightweight modeling language. LookML instructs Looker on how to query data, allowing everyone in the organization to create reports and dashboards without having to understand the underlying complexities.
  • Explore different development basics such as understanding how a project works in looker, development mode, understanding model, working with files, etc.
  • Explore how lookers generate SQl from LookML.
  • Establishing the git and version control system.
  • Setting up the database connection and using the looker marketplace in order to install, deploy, manage applications, and visualization types to a great extent.

Write LookML:

When a data modeler has mastered the fundamentals of creating projects and developing in Looker, it is time to write LookML! The flexibility of LookML allows developers to work with their organization’s data and create custom experiences for Looker users.

As part of LookML you need to learn the basic and advanced concepts such as lookML, content validation, looker blocks, working with joins, reusing the code with extends, localizing your LookML model, etc.

Using the API and Embedding:

Looker can be obtained in ways other than through the application. You can also use Looker through the API or embed Looker content in websites, both publicly and privately, if you have the necessary permissions.

In the looker AP section you will explore the looker API, Looker APi authentication, Looker API troubleshooting, exploring on how to embed the looker content into web pages considerably.

  • Security Best Practices for Embedded Analytics: When creating embedded content, keep these best practices in mind.
  • Private Embedding: Learn how to make embedded Looks, Explores, or dashboards require a Looker login.
  • SSO stands for Single Sign-On (SSO) Embedding: Create Looker embeds that use the sign-on functionality of your application for authentication.
  • Viewing Embedded Looks, Explores, and Dashboards : Discover how to access, save, download, and schedule embedded content.
  • Embedded JavaScript Events : Use JavaScript events to interact with Looker embeds.
  • Timezone Reference for SSO Embedding: View a list of time zones for use in the user timezone parameter of SSO embeds.

Related Article: Looker Features

Setting up and administrator looker

Looker offers the possibility of having Looker host your instance or hosting it yourself. A Looker Sales Engineer or Professional Services consultant will assist you with the setup process.

There are also numerous settings that will assist you in administering your Looker instance. These options include options for customizing Looker for your organization, configuring authentication for users and groups, controlling user and group access to data and Looker features, and monitoring Looker usage and health.

  • First need to explore the installation of the looker hosted instance and then customer hosted instances.
  • Then set up the database connection and test the connectivity.
  • You can explore the different administrative subjects that are needed by the lokker administrators.
  • Next, we need to explore the various looker administration functions such as accessing the general pages, system pages, database, platform pages, authentication and server pages in depth to find out any issues.

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Looker features

  • Looker really does have a lot of lovely features. The accompanying are a few of Looker’s features. Let us go over them.
  • Looker helps to reduce costs, effectively manage corporate deployments, and boost efficiency.
  • New types of data experiences can be accessed and innovation processes can be sped up by using Looker’s built-in Interactive elements.
  • Looker increases revenue growth and improves the product’s competitive advantage at a low cost.
  • Filtering data from the dashboard can provide you with useful information. You can also initiate data in any possible conversation, with the option of using Slack to find a solution on the fly. It allows you to compare data from multiple sources from any location.
  • Looker allows you to analyze and visualize data from AWS, Azure, Google Cloud, and on-premise databases. It is a multi-cloud integrated platform from start to finish.
  • With powerful data modeling that abstracts underlying data at any scale and creates a standard data model for the entire organization, business intelligence can be operated for anyone.
  • Looker is a popular integration library that allows us to quickly integrate analytics from anywhere and personalize the look and feel of our data experience.
  • Lookers augments business intelligence with artificial intelligence, cutting-edge machine learning, and advanced analytical capabilities built into the Google cloud platform.
  • Looker allows you to create data-driven applications from supply chain data.
  • It has a component that notifies the analyst of minor issues, ensuring that these minor issues do not lead to larger, more difficult-to-solve issues.
  • Looker’s graphs, reports, and charts are fully customizable and exportable.
  • It relies on real-time data analytics to investigate and make sound business decisions.
  • It establishes direct connections to any SQL database or other infrastructure. It is regarded as a self-learning database with some self-service capabilities.
  • Looker features customizable dashboards as well as a browser-based interface. Lookers makes it simple to create dashboards. These dashboards are compatible with any device.

Related Article: Looker Data Actions

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Conclusion

In the above blog post all points are explained with neat subheadings. Wants to get immense knowledge on the looker. Please go ahead with this looker documentation first that helps you to gain decent knowledge. Had any doubts drop your comments below, our experts will get back to you shortly.

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Incremental Load in QlikView – Table of content

What is Incremental load?

The practice of loading only new or modified records from a database into an existing QVD is known as an incremental load. As compared to complete loads, incremental loads are more effective, which is especially useful for large data sets. In QlikView, an incremental load occurs when new data from a source database is loaded while previously retrieved data is loaded from a local store. QVD files or the QVW format used with a binary load are commonly used to save data. 

Why incremental load?

Is your BI application storing large amounts of data in a  atabase? Is it happening regularly, if so? Because BI applications are expected to handle larger data sets, frequent refreshes must obtain the most up-to-date information. In both cases, loading all of the data historically every time to get the most recent updated records on a timely basis is inefficient. This is where the concept of “Increment Load” comes in handy for making BI applications more efficient.

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What is the intention of the incremental load?

The “Incremental Load” is the answer to all of the previous questions. The loading process’s performance is improved by pulling only new and updated records rather than the entire data set and appending them to the existing data set (QVD). To keep it simple, incremental load updates old table/QVD data with newly modified records at each refresh. It increases the loading process 100 times over conventional loads in this manner.

How exactly incremental load works?

Let’s take a closer look at it by putting it to use. The workflow steps for implementing the same are described below.

1. You must load the whole data without the incremental Load. Either time you need to update new records, you must reload the whole data, which takes a long time to load and save on the local drive (QVD). You can only load new/updated records with incremental loading.

2. In a table, find the last revised record date from the QVW.

3. Connect to the data repository based on the last updated date and pull the recently inserted records that are older than the last modified date. The “where” clause of the load script can be used to do this.

4. To get live data, attach the recently modified records to the current table locally.

5. The incremented table should be added to the BI application.

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Illustration of Incremental Load in Real Time

The practice of loading only new or modified records from a database into an existing QVD is known as an incremental load. As compared to complete loads, incremental loads are more effective, which is especially useful for large data sets. The incremental load can be applied in various ways, with the following being the most common:

  • Insert only (Do not validate for duplicate records).
  • Insert and update.
  • Insert, update and delete.

Illustration of Incremental Load in Real Time

1. Insert Only: 

Let’s assume we have sales raw data (in Excel) updated with necessary details about the transaction by modified date if a new sale is registered. We already had a QVD produced before yesterday because we are working on QVDs (25-Aug-14 in this case). Now you can load incremental data (Highlighted in yellow below).

Insert Only

To begin, build a QVD for data up until August 25, 2014. We need to know the date on which QVD was last changed to find new incremental data. The maximum Modified_date in the available QVD file will be used to determine this. As previously stated, It is concluded that “Sales. qvd” is up to date with data until August 25, 2014. The following code will be used to determine the last updated date of “Sales. qvd”:

QVD file

We have loaded the most recent QVD into memory and then identified the most recent modified date by storing the maximum number of “Modified_Date” values. We then save this date in a variable called “Last_Updated_Date” and delete the “Sales” table. I used the Peek() function to store the maximum number of changed dates in the above code. The syntax is as follows:

Peek( FieldName, Row Number, TableName)

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This function retrieves the contents of a given field from an internal table row. FieldName and TableName must be string values, while Row must be an integer value. The first record is indicated by a 0, the second by a 1, and so on. Negative numbers indicate the order of the table from the top. The last record is indicated by a -1.

We can load incremental records of the data set (Where clause in Load statement) and merge them with available QVD because we know when the records will be considered new records after that date (Look at the snapshot below).

incremental records of the data set

Now, load the most recent QVD (Sales), which will have incremental records.

incremental records

As you can see, two records from August 26, 2014, have been added. However, we’ve also added a duplicate record. Since we haven’t accessed the available records, we may tell that an INSERT is the only approach that will not validate duplicate records.

Furthermore, we are unable to update the value of existing records using this method.

To recap, the steps to load only incremental records to QVD using the INSERT only method are as follows:

1. Recognize and load new records.
2. Combine this data with the QVD file.
3. Replace the old concatenated table with the new QVD file.

2. Insert and Update method:

We can’t search for duplicate records or update existing records, as seen in the previous case. The Insert and Update approach comes in handy here:

Insert and Update method

Assume ID is the primary key, and we should be able to define and distinguish new or updated records based on change date and ID.

To use this process, repeat the steps for identifying new records as in the INSERT the only method. Then, apply the search for duplicated records or change old records’ value when concatenating incremental data with existing records.

incremental data with existing records

We’ve only loaded records where the Primary Key(ID) is new. The Exists() feature prevents the QVD from loading old records because the Latest version is already in memory, so expired record values are immediately updated.

Both specific records are now available in QVD, along with an updated sales value for ID (PRD858).

feature prevents the QVD

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3. INSERT, UPDATE, and DELETE method:

This method’s script is somewhat similar to the INSERT & UPDATE method, except there is an additional step to remove deleted records.

We’ll use an inner join with a concatenated data set (Old+Incremental) to load primary keys for all records in the new data set. Only common records shall be maintained, and unnecessary records will be deleted due to the inner join. Assume that in the previous case, we want to remove a record with the ID PRD1058.

INSERT, UPDATE, and DELETE method

We have a data set of one record added (ID PRD1458), one record modified (ID PRD158), and one record deleted (ID PRD1058).

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Advantages of Incremental Load

The following are the benefits of the incremental load.

  • By removing the maximum load of data, it provides a productive load at any time.
  • As opposed to the standard model, it lowers the time it takes to get complete data by 100 times.
  • Incremental load reduces the database’s traffic load.
  • It reduces the workload for data source drivers.
  • The Incremental load minimizes the load on RAM.
  • It functions as a JIT (Just-In-Time) engine in the Data Extraction layer, fetching data in real-time.
  • It makes use of QVD file formatted tables, which significantly compresses the results.

Data Localization

The incremental load uses newly added data and attaches it to the recently incremented table, resulting in data access that is still local to the BI application.

Conclusion

This blog has addressed how incremental loads are faster and more effective than FULL loads for loading data. You should make regular backups of your data as the best idea, and if there are problems with your database server or network, your data can be affected or lost. It would be best to choose which approach is best for you based on your business and application needs. Insert and Update is used in the majority of BFSI applications. In most cases, records are not deleted.

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