Domo Tutorials | Domo Online Tutorials for Beginners


DOMO Tutorial – Table of Content

What is Domo?

Domo is a cloud-based business intelligence software company first founded in 2010 by Josh James, located in American fork, Utah, United States. The main purpose of Domo is to provide business intelligence tools and data visualizations. This cloud-based platform helps users to integrate the business data from multiple sources. Domo supports collaborating with users, data, and systems to form a digitally-connected platform. Domo is one of the SaaS (software-as-a-service) software ventures. Domo is mainly used to perform operations like; data-analytics, data visualization, reporting, and digitalization. Up to five users can access the Demo tool free of cost. This software tool provides fully integrated business data for many textual programming languages.

Why Domo?

As I said earlier the main purpose of using Domo is to mainly perform data analytic and visualization operations. Apart from these usages, Domo has been used for other real-time activities. So these make Domo as one of the popular business intelligence tools. I would like to explain the main reasons why we need Domo in our business organizations;

  • By using Domo tool you can run your company with the help of Mobile phones.
  • This software tool lets the user perform real-time data in the real environment.
  • Domo is available in the form application (app) so that you can access it easily.
  • This offers data-driven business intelligence and analytics.
  • Offers a high-level of consumerization and mobility.
  • Matured levels of data monitoring also possible.
  • Decision making: This Domo converts the business data into information. This helps the management to make fact-based decisions.
  • This supports dashboard services and business assistance.
  • Mobile services are getting easier because of the Domo business tool.

Take your career to next level in Domo with HKR. Join Domo online training now

Domo Architecture

Domo Architecture

Domo architecture explains the components and their applications. The below diagram will give you some idea about real-time Domo components usage and their functions. So let’s check with Architecture;

Firstly have a look into the Domo advanced components;

  • Implementation in windows: This sever is the best to run your application without any use of java web application servers. This traffic allows traffic between the client browser and Domo advanced to pass through and appear as though it is coming from Microsoft IIS.
  • Domo architecture is made up of several applications such as;
  • Domo builder: It is a kind of desktop application written in java language mainly used to create and design dashboard applications.
  • Any of these Domo application servers can be configured to use SSL as an added measure of security.
  • Supported browsers: Domo advanced administrator is used to configure and manage the multiple Domo servers.
  • Mobile Servers: This Domo mobile server is a specialized mobile application browser mainly used for iPhone, Android, and other smartphones.
  • These components, servers, and applications can able the user to access them on the Domo dashboard.

Domo Software:

Domo Software is a cloud-based business intelligence platform designed to offer easiest, real-time, and direct access to the business data. And also make them run on dashboard browsers to help several mobile application servers. It is a SaaS (software-as-a-service) software venture. This software also used to perform data analysis, data visualization, and reporting operations. Domo software is available for free and helps to run the business application everywhere. Domo’s freemium allows up to five users to access it and if you need it for advanced application, you can change it from freemium to premium.

Domo Training

  • Master Your Craft
  • Lifetime LMS & Faculty Access
  • 24/7 online expert support
  • Real-world & Project Based Learning

Details of Domo pricing:

Domo Pricing is nothing but the total cost of the functionality.

Domo is the app to help you truly “showcase” the numbers, without needing to write gazillion lines of code in the process. The data visualizations are staggering. The ease of incorporating data sources into the platform is very simple. Domo is as simple as connecting your data and building a “card” – a sort of mis easy to create and read reports on analytics and social media for the client. The only limit you may find in the Domo is the price point- while the basic functions are free to come up with a generous 100 GB of space, maybe a deal-breaker for many consultants. Finally, there is a Domo for developers that allow you to customize just about everything you can think of. Domo tool’s connectors and user interfaces help users to  access other apps namely QuickBooks, Excel, or social media platform for better communication with the clients. Users can able to create customized indicators from the usage of external data sources analysis purpose and present the overall result on the Domo dashboards.

Before you start learning the Domo Course we suggest you learn Tableau Online Course

Difference between Domo Vs Tableau

Here I am going to explain the main differences between Tableau and Domo:

Topic Tableau Domo
Data sources and Data connectors Holds a good comprehensive list of data sources and also custom connectors. Domo holds a huge list of data sources mainly based on company business roles and also custom connectors.
Data team Large ecosystem of users and talent available being one of the oldest BI toolsData team Good options and community due to ease of use of the tool
End users Can be accessed using Tableau cloud/servers in browsers and able on Tableau Reader in local environments Completely cloud-based, it has cards, collections and dashboards all can be accessed by using single URLs
Data Size and growth All its connectors handle the data very well In Domo, all the connectors handle the data very well and Domo software team update them regularly
Timelines and Cost To manage a large amount of data, backend work and SQL data flows can be added to optimize it. Expensive tool.

To manage a large amount of data, backend work and ETL/SQL data flows can be added to optimize it. Cost-effective tool.

 

Domo Datatypes:

Domo Datatypes are distinguished into two types;

  • Common data type: Under this type, there are four data types added such as,

o   LONG

o   DOUBLE

o   STRING

o   DATE and TIME

  • Different data types: Under this data type, two types added such as,

o   BINARY

o   DECIMAL

Let me explain one by one;

LONG: This datatype stores numbers as a numeric value, including integers, floating-point, and whole numbers.

DOUBLE: this data type stores double-decision floating point number value.

STRING: String in Domo stores alphanumeric characters as a text.

DATE: store the values like the year, month, and day value.

TIME: This data type stores hour, minute, and second values.

BINARY: This store addition, subtraction, multiplication, and division values.

DECIMAL: This datatype stores the value ranges from 0 to 1.

Domo Functions:

Domo supports many types of functions to perform specific tasks. The following are the main functions used in Domo;

o   APPROXIMATE COUNT (DISTINCT) -> Returns the approximate count of several unique values in the column.

o   AVG -> Returns the average value for each series in a column.

o   CEILING -> Returns the highest value for each series in a column.

o   COUNT -> Returns the number of row values in a column.

o   FLOOR -> Returns the lowest value for each series in a numeric column.

o   ABS -> Returns the absolute value for all values in a numeric column.

o   MOD -> Returns the reminder of each value in a numeric column divided by some specified number.

o   POWER-> Returns each value in a numeric column raised to a given power.

o   RAND-> Returns random values between 0 and 1.

o   ROUND -> Returns the values in a numeric column rounded to the nearest specified decimal place.

o   CASE-> use to begin logical statements such as when…. Then or when… then, else.

o   IFNULL -> Used in logical statements in which you want to specify a replacement for null values.

o   NULLIF -> Returns the null if the value in the first column equals the value in the second column; otherwise it returns the first column if this condition is not satisfied.

o   CONCAT -> Combines strings from two or more string columns.

o   INSTR -> Returns the positon of first string instances, mostly starting from the first letter in any given columns.

o   LEFT -> Returns the specified number of characters in each string in the given column, starting from the left.

o   LENGTH -> Returns the number of characters in each string in the given column.

o   LOWER -> Converts strings from one or more string columns into lower-case.

o   ADD DATE -> adds date or date-time values to date values in a date column.

o   ADDTIME -> adds a specified number of seconds to all values in a time column.

o   CURDATE -> returns the current date.

o   CURTIME -> returns the current time.

o   HOUR -> returns the hour for all values in a date/time column.

o   MONTH -> returns the month number.

Business Intelligence & Analytics, domo-tutorial-description-1, Business Intelligence & Analytics, domo-tutorial-description-2

Subscribe to our YouTube channel to get new updates..!

DOMO Features and capabilities:

  • The following are the feature summary of DOMO platform;
  • This platform supports business cloud operations and helps you to connect all your existing business data/ applications.
  • This also helps users to integrate all the existing business data
  • Domo platform available in the form apps that are designed by third-party app developers
  • Social media communications and messaging features also added
  • You can operate this Domo from anywhere by using your mobile phone.

Domo Features and Capabilities in-depth:

Holds multiple connecting sources:

  • Data transformation: The Domo platform has an option to combine multiple data that are available from multiple sources. Before it was difficult to transform business data due to the usage of codes. Now recent upgrade has come up with an advanced feature to helps non-technical background people can also access this tool.
  • The Domo platform holds a large number of dashboards and connectors, which helps to connect the multiple business data available from multiple sources. This Domo tool contains more than 40 one-click connectivity apps and 300-500 total connectors.
  • Data Visualization:Domo card builder helps to create meaningful data insights and visualizations. Navigation is as follows: Choose the chart types -> array -> card builder to visualize your business data. The Domo will automatically highlight the key along with important figures.

Domo Platform in detail:

Domo is a fully cloud-based business intelligence tool. This is one of the most demanding software tools when compare to tableau because of its enormous advantages, features, and capabilities. The main purpose of using Domo is to provide data visualizations, analysis, and data integration for all the business data.

Domo enables us to give a 360-degree view of your business and it enables you to access data and also visible to everyone in the company. You can convert the data into actionable steps. The existing integration features are available which makes it easy to combine our daily data routines. The Domo platform also provides a web-based hub for your data and helps us to visualize where the team focuses to interact with their data. One of the main things Domo provides is the -ETL tool. So this makes users integrate, extract, transform, and loading of multiple business data. Easy to use the platform as it doesn’t require any coding experience and also offers API interfaces to build customized user queries. Data analysis with Domo is very easy to do and understandable tool available in the most recent years. Effective Communication on a social media platform and affordable tool. Up to 5 users can access Domo freemium and for any advanced usage, we need to go with Domo premium subscriptions.

Lastly but very important Domo is available in the form of Apps, easy to install from app stores and you can access it from anywhere with the help of Mobile phones.

Top 30 Real Time Domo Interview Quuestions & Answers for 2020

Creating Domo stories

The main purpose of Domo stories is to design customizes story pages and enables them to work smoothly across the device. Domo stories are available in the form of layout or chart they have been optimized for web services, mobile devices, tablets, or IPods.

Converting standard pages to a Domo software page is as follows:

Steps:

1) Choose the Design Dashboard -> from the settings menu -> located in the top right corner of the page.

2) Then now click the design dashboard button

If this Domo design page contains any collections, all these collections become Domo stories pages automatically. If the page doesn’t contain any collections that will be added into the Appendix section.

3) If you are happy with this Domo stories layout -> click Save -> then close it.

Converting a Domo stories page back to a standard page:

To converts, a Domo stories page back to the standard page, either you should be an owner or Admin.

Steps:

1) Now select the Convert to standard page button -> from the setting menu -> located in the top right corner of the page.

2) Choose -> convert page-> confirm.

If any page contains Domo stories, all these Domo stories will be converted into collections.

Adding Domo stories/ layout components:

  • Add your new domo card layouts on the base of the Domo stories template.
  • Now you need to drag and drop KPI domo cards into the layout slots.
  • Now crop the template and cards to make them in the correct shape or size.
  • You can also swap domo cards by dragging one card after the other.
  • You can also add headers to your domo stories.
  • If you want you can drag or drop layout and headers on the domo page.
  • You can also remove/delete the layouts or headers as per your requirements.
  • You can also add borders and images to the domo page.
  • You can change the background color of your domo page/domo card.

Domo Training

Weekday / Weekend Batches

Adding a new domo stories pages:

Steps:

1) Click on the ‘+’ in the top right corner of the domo screen.

2) Choose a new story to add.

3) Now enter the name for the new stories page.

Adding a Domo stories layout into the page:

Steps:

1) Go to the Domo page -> to add a domo stories layout -> choose to edit the dashboard button -> in the settings menu.

2) Now just click and drag the icon -> located in the right corner of the page.

You can see that by dragging the layout icon located in the corner, this highlights the layout page.

3) Now by using the filter option to see the location of the desired template.

4) Once you locate the template -> now just click on it.

Adding headers:

Steps:

1) Select the page, to add header text to any page->click on the edit dashboard -> from the setting menu

2) Click the page and drag the icon toolbar to add the header page.

Advantages of using Domo tool:

There are lots of advanced features and tools that make Domo as one of the demanding cloud-based business intelligence tools. Let’s get into know the advantages of Domo tool.

Scalability:

Domo tool enables users to store the billions of data rows, and also help to connect multiple data sources. Domo delivers effective and safe business services across the world.

Architecture design:

Domo architecture has made up of millions of connectors and dashboards. These connectors and dashboards help the business team to increase the operational performances.

Very secured to use:

Domo tool offers the most secure and authorized data usage. So there is less chance of data leakage kind of issues.

Cost-effective:

Domo is a cost-effective, affordable, and easy-to-maintain tool with different capabilities. It also allows you to take a free trial for basic purposes. 

Conclusion

Domo is one of the effective and commonly used cloud-based business intelligence tools. Domo features and capabilities tend to use them in many applications like web services, mobile devices, and social media platforms for better communications and performance. This article covers the topics, this may help beginners to know as well as learn the tool in a more effective way. I think this article may help a few of the business intelligence developers and also for the domo community forums.

Other related articles:

1. Domo Interview Questions



Source link

Leave a Reply

Subscribe to Our Newsletter

Get our latest articles delivered straight to your inbox. No spam, we promise.

Recent Reviews


What is Hadoop? 

As data generation grew over time, higher volumes and more formats appeared. To save time, multiple processors were needed to process data. However, due to the network overhead caused, a single storage unit became the bottleneck. As a result, each processor now has a distributed storage facility, which makes data access much simpler. Parallel processing with distributed storage is the term for this system, in which multiple computers run processes on different storages.

This article provides a comprehensive overview of Big Data problems, as well as what Hadoop is, what its components are, and how it can be used. Next, we’ll look at the components of Hadoop to get a better understanding of what it is.

Become a  Hadoop Certified professional by learning this HKR Hadoop Training 

Why Hadoop?

It’s quick to get Hadoop contagious. Its adoption in one organization may contribute to the adoption of similar practices in other organizations. Handling massive data seems to be much simpler today, thanks to this piece of technology’s robustness and cost-effectiveness. Another great function is the ability to incorporate HIVE into an EMR workflow. It’s extremely easy to start a cluster, install HIVE, and begin running basic SQL analytics in no time. Let’s take a closer look at why Hadoop is so strong.

Key features of Hadoop

1. Flexible:

Since only 20% of data in enterprises is organized and the remaining is unstructured, managing unstructured data that goes unattended is critical. Hadoop is a software platform that handles various kinds of Big Data, whether structured or unstructured, encoded or formatted, or some other kind of data, and makes it usable for decision-making. Hadoop is also easy, appropriate, and schema-free! Though Hadoop is better known for supporting Java programming, the MapReduce technique allows any programming language to be used in Hadoop. Hadoop is better suited for Windows and Linux, but it can also run on BSD and OS X.

2.  Scalable

Hadoop is a flexible framework in the sense that new nodes can be introduced to the system as required without having to change data formats, data loading practices, program writing methods, or even current applications. Hadoop is free and open-source software that runs on commodity hardware. Hadoop is also fault resistant, which ensures that if a node fails or goes out of operation, the machine will simply reallocate work to another place in the data and resume processing as if nothing has happened!

3. Building a more efficient data economy:

Hadoop has revolutionized big data mining and analysis all over the world. Until now, businesses have been concerned with how to handle the constant inflow of data into their applications. Hadoop is more akin to a “dam,” collecting an infinite number of data and generating a great deal of power in the form of related data. Hadoop has fully altered the economics of data storage and analysis!

4. Robust Ecosystem:

Hadoop provides a rather versatile and rich environment that is well-tailored to developers, web start-ups, and other organizations’ computational needs. The Ecosystem is made up of several similar initiatives, including MapReduce, Hive, HBase, Zookeeper, HCatalog, and Apache Pig, which make it capable of delivering a wide range of services.

5. Hadoop is getting more “Real-Time”!

Have you ever wondered how to feed data into a cluster and test it in real-time? It’s a problem for which Hadoop has a solution. Yes, skills are becoming more real-time. It also offers a standardized approach to a diverse range of big data analytics APIs, such as MapReduce, query languages, and database access, among others.

6. Cost-Effective:

With so many wonderful features, the icing on the cake is that Hadoop saves money by adding massively parallel processing to commodity servers, resulting in a significant decrease in the cost per terabyte of storage, making it possible to model all of your files. The basic concept here is to do cost-effective data analysis through the internet!

7.  Upcoming Technologies using Hadoop:

Hadoop is contributing to phenomenal technological advances by bolstering its capability. HBase, for example, is quickly becoming a critical platform for Blob Stores (Binary Large Objects) and Lightweight OLTP (Online Transaction Processing). It’s also been a stable basis for new-school graph and NoSQL databases, as well as enhanced relational databases.

8.  Hadoop is getting cloudy!

Hadoop is becoming hazier! In reality, many companies are synchronizing with cloud storage to handle Big Data. Hadoop is going to be one of the most important cloud computing apps. The number of clusters provided by cloud providers in different industries shows this. As a result, it will soon be in the cloud!

Become a Big Data Hadoop Certified professional by learning this HKR Big Data Hadoop Training 

Hadoop Training

  • Master Your Craft
  • Lifetime LMS & Faculty Access
  • 24/7 online expert support
  • Real-world & Project Based Learning

Components of Hadoop

Enterprise data is generating at an accelerated pace these days, and how we use it for a company’s growth is critical.  With its tremendous support for big data storage and analytics, Hadoop is hitting new heights. Companies all over the world began moving their data to Hadoop to join the early adopters of the technology and get the best out of their data.

Hadoop is a Big Data storage and management system that makes use of distributed storage and parallel processing. It is the most widely used program for dealing with large amounts of data. Hadoop is made up of three components.

  • Hadoop HDFS – Hadoop’s storage unit is the Hadoop Distributed File System (HDFS).
  • Hadoop MapReduce – Hadoop MapReduce is the Hadoop processing unit.
  • Hadoop YARN – Hadoop YARN is a Hadoop resource management unit.

Hadoop Common

As it functions as a channel or a SharePoint for all other Hadoop components, it is regarded as one of the Hadoop core components. Hadoop Common is a set of libraries and utilities that help other Hadoop modules work together. Consider the following scenario: To access HDFS, HBase or Hive must first use the Hadoop Common’s Java archives (JAR files).

Hadoop HDFS

HDFS is Hadoop’s default data storage, and data is saved there before it’s required for processing. The data in HDFS is divided into several units called blocks and distributed throughout the cluster. It generates several replicas of data blocks and distributes them through clusters for consistent and convenient access.

Namenode, Data Node, and Secondary Name Node are the other three key components of HDFS. It employs a Master-Slave architecture paradigm. In this architecture, the Namenode serves as a master node to control the storage system, while the Data node serves as a slave node to manage the Hadoop cluster’s various structures.

HDFS is a file system designed specifically for storing large datasets on commodity hardware. For the full processor, an enterprise version of a server costs about $10,000 per terabyte. If you need to purchase 100 of these enterprise-level servers, the cost would exceed a million dollars. Data nodes in Hadoop can be commodity devices. You won’t have to spend millions on data nodes this way. The word node, on the other hand, has always been an enterprise server.

Features of HDFS

  • Distributed storage is provided.
  • It is possible to implement it on product hardware.
  • Provides data protection.
  • Highly fault-tolerant – if one system breaks down, the data from that machine is transferred to the next.

Master and Slave Nodes

HDFS is composed of master and slave nodes. The master is the name node, while the slaves are the data nodes.

Master and Slave Nodes

The name node is in charge of the data nodes’ operations. It also keeps track of metadata.

The data nodes are responsible for reading, writing, processing, and replicating information. They often relay signals to the name node known as heartbeats. The data node’s status is indicated by these heartbeats.

data nodes

Consider the fact that the name node contains 30TB of data. This data is replicated among the data notes by the name node, which delivers it across the data nodes. The blue, grey and red data are replicated among the three data nodes, as seen in the image above.

By default, data replication takes place three times. This is achieved so that if a commodity machine breaks down, a new machine with the same data can be used to replace it.

In the next section of the What is Hadoop post, we’ll concentrate on Hadoop MapReduce.

Get ahead in your career with our  Hadoop Tutorial!

HKR Trainings Logo

Subscribe to our YouTube channel to get new updates..!

Hadoop MapReduce

Hadoop MapReduce is the Hadoop processing unit. The processing takes place on the slave nodes, and the final output is sent to the master node in the MapReduce approach.

To handle all of the data, a data containing code is used. Concerning the raw data, this coded data is normally very small. To run a heavy-duty operation on computers, you only need to submit a few kilobytes of code.

Apache Hadoop includes MapReduce as a key feature. It allows programmers to handle massive amounts of data while writing programs. MapReduce is a Java program that can process vast volumes of data. Its main function is to divide the data into small, separate bits that can be processed in parallel.

The MapReduce algorithm is made up of two main parts: Map and Reduce. When the Map function completes its mission, the Reduce function begins. The map takes a set of data and converts it into tuples. The Reduce function takes the Map function’s output and combines it with another set of tuples to generate a new set of tuples. Hadoop relies heavily on MapReduce’s parallel processing functionality. It enables big data processing to be performed on several computers in the same cluster.

Hadoop mapreduce

Let’s take a closer look at each feature.

Map Stage:

The input data is converted using the mapper tool. The data can be stored in HDFS in a variety of formats, such as folders or directories. The entire data set is sequentially transferred through the Map Function, which transforms it into tuples. 

Reduce stage:

The data is shuffled and reduced to some extent at this point. It uses the Map function’s output to perform the data processing function. It generates a new output after the reduced operation is completed, which is automatically stored in the Hadoop Distributed File System.

In this article, we’ll focus on Hadoop YARN, which is the next concept we’ll look at.

Hadoop YARN

The YARN’s key concept is to separate the resource control and work scheduling functions into various daemons. YARN is responsible for allocating resources to the Hadoop cluster’s various applications.

Resource manager and Node manager are the two key components of YARN. The data computation system is made up of these two components. The resource manager is in charge of delegating work to all applications in the system, while the node manager is in charge of containers and tracks their resource usage (CPU, disk, memory, and network) and sends the same information to the Resource manager.

Hadoop’s YARN acronym stands for Yet Another Resource Negotiator. It is Hadoop’s resource management unit, and it is used in Hadoop version 2 as a component. 

Hadoop YARN serves as an operating system for Hadoop. It’s a file system that uses HDFS as a foundation.
It’s in charge of handling cluster resources to prevent overloading a single server.
It manages work schedules to ensure that jobs are planned in the right places.

Hadoop YARN

Assume a client computer requires the execution of a query or the retrieval of code for data processing. The resource manager (Hadoop Yarn), who is responsible for the resource allocation and management, receives this job request.

Each node has its node manager in the node section. These node managers are responsible for the nodes and keep track of their resource usage. Physical resources such as RAM, CPU, and hard drives are contained within the containers. The app master requests the container from the node manager whenever a job request is received. The resource is returned to the Resource Manager until the node manager has received it.

Top 30 frequently asked Big Data Hadoop interview questions & answers for freshers & experienced

Hadoop Training

Weekday / Weekend Batches

YARN components : (Yet Another Resource Negotiator) 

Hadoop YARN distributes work among its components and keeps them accountable for completing the task at hand. The tasks assigned to the various Core components of YARN are described below.

  • A global Resource manager is in charge of accepting user work submissions and scheduling them by allocating resources.
  • To the Resource manager, a Node manager is a Reporter. Each Node has a node manager who reports back to the Resource Manager on the functionality of each node.
  • Each framework has its Application Master, which aids the Node Manager in executing and monitoring tasks and smoothing out the resource allocation process.
  • The Resource container, which is operated by Node managers and distributed with the system resources allocated to individual applications, is another aspect of YARN.

Conclusion: 

So far, we have focused on what Hadoop is, why Hadoop is necessary, and what are the various Hadoop components that make it up. Thus you have now learned the essential knowledge to understand different components of Hadoop that will assist you when you start working on Hadoop.

Related articles



Source link