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What is Pentaho BI?Pentaho is a Business Intelligence tool which provides a wide range of business intelligence solutions to the customers. It is capable of reporting, data analysis, data integration, data mining, etc. Pentaho also offers a comprehensive set of BI features which allows you to improve business performance and efficiency.
In this Pentaho tutorial for beginners, you will learn:
Features of PentahoFollowing, are important features of Pentaho:
ETL capabilities for business intelligence needs
Understanding Pentaho Report Designer
Product Expertise
Offers Side-by-side subreports
Unlocking new capabilities
Professional Support
Query and Reporting
Offers Enhanced Functionality
Full runtime metadata support from data sources
Pentaho BI suiteNow, we will learn about Pentaho BI suite in this Pentaho tutorial:
Pentaho BI Suite
Pentaho BI Suite includes the following components:
Pentaho ReportingPentaho Reporting depends on the JFreeReport project. It helps you to fulfill your business reporting needs. This component also offers both scheduled and on-demand report publishing in popular formats such as XLS, PDF, TXT, and HTML.
AnalysisIt offers a wide range of analysis a wide range of features that includes a pivot table view. The tool provides enhanced GUI features (using Flash or SVG), integrated dashboard widgets, portal, and workflow integration.
Dashboards
The dashboard offers Reporting and Analysis, which contribute content to Pentaho Dashboards. The self-service dashboard designer includes extensive built-in dashboard templates and layout. It allows business users to build personalized dashboards with little training.
Data MiningData mining tool discovers hidden patterns and indicators of future performance. It offers the most comprehensive set of machine learning algorithms from the Weka project, which includes clustering, decision trees, random forests, principal component analysis, neural networks.
It allows you to view data graphically, interact with it programmatically, or use multiple data sources for reports, further analysis, and other processes.
Pentaho Data IntegrationThis component is used to integrate data wherever it exists.
Rich transformation library with over 150 out-of-the-box mapping objects.
It supports a wide range of data source which includes more than 30 open source and proprietary database platforms, flat files. It also helps Big Data analytics with integration and management of Hadoop data.
Who are using Pentaho BI?Pentaho BI is a widely used tool by may software professionals like:
Open source software programs
Business analyst and researcher
College students
Business intelligence councilor
How to Install Pentaho in AWSFollowing is a step by step process on How to Install Pentaho in AWS.
On next page, Accept License Agreement
Proceed for Configuration
Check the usage instructions and wait
Copy Public IP of the instance.
Paste public IP of the instance to access Pentaho.
Prerequisite of Pentaho
Hardware requirements
Software requirements
Downloading and installing Bl suite
Starting the Bl suite
Administration of the Bl suite
Hardware requirement:The Pentaho Bl Suite software does not have any fix limits on a computer or network hardware as long as you can meet the minimum software requirements. It is easy to install this Business intelligence tool. However, a recommended set of system specifications:
RAM Minimum 2GB
Hard drive space Minimum 1GB
Processor Dual-core EM64T or AMD64
Software requirements
Installation of Sun JRE 5.0
The environment can be either 32-bit or 64-bit
Supported Operating systems: Linux, Solaris, Windows, Mac
A workstation that has a modern web browser interface such as Chrome, Internet Explorer, Firefox
To start Bl-server
On Linux OS run start-pentaho script on /biserver-ce/directory
To start the administrator server:
For Linux: goto the command window and run the start-up script in /biserver-ce/administration-console/directory.
To Stop administrator server:
On Linux. You need to go to the terminal and goto installed directory and run stop.bat
Pentaho Administration Console Report Designer: Design Studio:It is an Eclipse-based tool. It allows you to hand-edit a report or analysis. It is widely used to add modifications to an existing report that cannot be added with Report Designer.
Aggregation Designer:This graphical tool allows you to improve Mondrian cube efficiency.
Metadata Editor:It is used to add custom metadata layer to any existing data source.
Pentaho Data Integration:The Kettle extract, transform, and load (ETL) tool, which enables
Pentaho Tool vs. BI stackPentaho Tool BI Stack
Data Integration (PDI) ETL
It offers metadata Editor It provides metadata management
Pentaho BA Analytics
Reports Designer Operational Reporting
Saiku Ad-hoc Reporting
CDE Dashboards
Pentaho User Console (PUC) Governance/Monitoring
Advantages of Pentaho
Pentaho BI is a very intuitive tool. With some basic concepts, you can work with it.
Simple and easy to use Business Intelligence tool
Offers a wide range of BI capabilities which includes reporting, dashboard, interactive analysis, data integration, data mining, etc.
Comes with a user-friendly interface and provides various tools to Retrieve data from multiple data sources
Offers single package to work on Data
Has a community edition with a lot of contributors along with Enterprise edition.
The capability of running on the Hadoop cluster
JavaScript code written in the step components can be reused in other components.
Here, are cons/drawbacks of using Pentaho BI tool:
The design of the interface can be weak, and there is no unified interface for all components.
Much slower tool evolution compared to other BI tools.
Pentaho Business analytics offers a limited number of components.
Poor community support. So, if you don’t get a working component, you need to wait till the next version is released.
Summary:
Pentaho is a Business Intelligence tool which provides a wide range of business intelligence solutions to the customers
It offers ETL capabilities for business intelligence needs.
Pentaho suites offer components like Report, Analysis, Dashboard, and Data Mining
Pentaho Business Intelligence is widely used by 1) Business analyst 2) Open source software programmers 3) Researcher and 4) College Students.
The installation process of Pentaho includes: 1)Hardware requirements 2) Software requirements, 3) Downloading Bl suite, 4) Starting the Bl suite, and 5) Administration of the Bl suite
Important components of Pentaho Administration console are 1) Report Designer, 2) Design Studio, 3) Aggregation Designer 4) Metadata Editor 5) Pentaho Data Integration
Pentaho is a Data Integration (PDI) tool while BI stack is an ETL Tool.
The main drawback of Pentaho is that it is a much slower tool evolution compared to other BI tools
You're reading Pentaho Data Integration Tutorial: What Is, Pentaho Etl Tool
Sqoop Tutorial: What Is Apache Sqoop? Architecture & Example
What is SQOOP in Hadoop?
Apache SQOOP (SQL-to-Hadoop) is a tool designed to support bulk export and import of data into HDFS from structured data stores such as relational databases, enterprise data warehouses, and NoSQL systems. It is a data migration tool based upon a connector architecture which supports plugins to provide connectivity to new external systems.
An example use case of Hadoop Sqoop is an enterprise that runs a nightly Sqoop import to load the day’s data from a production transactional RDBMS into a Hive data warehouse for further analysis.
Next in this Apache Sqoop tutorial, we will learn about Apache Sqoop architecture.
Sqoop ArchitectureAll the existing Database Management Systems are designed with SQL standard in mind. However, each DBMS differs with respect to dialect to some extent. So, this difference poses challenges when it comes to data transfers across the systems. Sqoop Connectors are components which help overcome these challenges.
Data transfer between Sqoop Hadoop and external storage system is made possible with the help of Sqoop’s connectors.
Sqoop has connectors for working with a range of popular relational databases, including MySQL, PostgreSQL, Oracle, SQL Server, and DB2. Each of these connectors knows how to interact with its associated DBMS. There is also a generic JDBC connector for connecting to any database that supports Java’s JDBC protocol. In addition, Sqoop Big data provides optimized MySQL and PostgreSQL connectors that use database-specific APIs to perform bulk transfers efficiently.
Sqoop Architecture
In addition to this, Sqoop in big data has various third-party connectors for data stores, ranging from enterprise data warehouses (including Netezza, Teradata, and Oracle) to NoSQL stores (such as Couchbase). However, these connectors do not come with Sqoop bundle; those need to be downloaded separately and can be added easily to an existing Sqoop installation.
Why do we need Sqoop?Analytical processing using Hadoop requires loading of huge amounts of data from diverse sources into Hadoop clusters. This process of bulk data load into Hadoop, from heterogeneous sources and then processing it, comes with a certain set of challenges. Maintaining and ensuring data consistency and ensuring efficient utilization of resources, are some factors to consider before selecting the right approach for data load.
Major Issues:1. Data load using Scripts
The traditional approach of using scripts to load data is not suitable for bulk data load into Hadoop; this approach is inefficient and very time-consuming.
2. Direct access to external data via Map-Reduce application
Providing direct access to the data residing at external systems(without loading into Hadoop) for map-reduce applications complicates these applications. So, this approach is not feasible.
3. In addition to having the ability to work with enormous data, Hadoop can work with data in several different forms. So, to load such heterogeneous data into Hadoop, different tools have been developed. Sqoop and Flume are two such data loading tools.
Next in this Sqoop tutorial with examples, we will learn about the difference between Sqoop, Flume and HDFS.
Sqoop vs Flume vs HDFS in HadoopSqoop Flume HDFS
Sqoop is used for importing data from structured data sources such as RDBMS. Flume is used for moving bulk streaming data into HDFS. HDFS is a distributed file system used by Hadoop ecosystem to store data.
Sqoop has a connector based architecture. Connectors know how to connect to the respective data source and fetch the data. Flume has an agent-based architecture. Here, a code is written (which is called as ‘agent’) which takes care of fetching data. HDFS has a distributed architecture where data is distributed across multiple data nodes.
HDFS is a destination for data import using Sqoop. Data flows to HDFS through zero or more channels. HDFS is an ultimate destination for data storage.
Sqoop data load is not event-driven. Flume data load can be driven by an event. HDFS just stores data provided to it by whatsoever means.
In order to import data from structured data sources, one has to use Sqoop commands only, because its connectors know how to interact with structured data sources and fetch data from them. In order to load streaming data such as tweets generated on Twitter or log files of a web server, Flume should be used. Flume agents are built for fetching streaming data. HDFS has its own built-in shell commands to store data into it. HDFS cannot import streaming data
Etl (Extract, Transform, And Load) Process In Data Warehouse
What is ETL?
It’s tempting to think a creating a Data warehouse is simply extracting data from multiple sources and loading into database of a Data warehouse. This is far from the truth and requires a complex ETL process. The ETL process requires active inputs from various stakeholders including developers, analysts, testers, top executives and is technically challenging.
In order to maintain its value as a tool for decision-makers, Data warehouse system needs to change with business changes. ETL is a recurring activity (daily, weekly, monthly) of a Data warehouse system and needs to be agile, automated, and well documented.
In this ETL tutorial, you will learn-
Why do you need ETL?
There are many reasons for adopting ETL in the organization:
It helps companies to analyze their business data for taking critical business decisions.
Transactional databases cannot answer complex business questions that can be answered by ETL example.
A Data Warehouse provides a common data repository
ETL provides a method of moving the data from various sources into a data warehouse.
As data sources change, the Data Warehouse will automatically update.
Well-designed and documented ETL system is almost essential to the success of a Data Warehouse project.
Allow verification of data transformation, aggregation and calculations rules.
ETL process allows sample data comparison between the source and the target system.
ETL process can perform complex transformations and requires the extra area to store the data.
ETL helps to Migrate data into a Data Warehouse. Convert to the various formats and types to adhere to one consistent system.
ETL is a predefined process for accessing and manipulating source data into the target database.
ETL in data warehouse offers deep historical context for the business.
It helps to improve productivity because it codifies and reuses without a need for technical skills.
ETL Process in Data WarehousesETL is a 3-step process
ETL Process
Step 1) ExtractionIn this step of ETL architecture, data is extracted from the source system into the staging area. Transformations if any are done in staging area so that performance of source system in not degraded. Also, if corrupted data is copied directly from the source into Data warehouse database, rollback will be a challenge. Staging area gives an opportunity to validate extracted data before it moves into the Data warehouse.
Three Data Extraction methods:
Full Extraction
Partial Extraction- without update notification.
Partial Extraction- with update notification
Irrespective of the method used, extraction should not affect performance and response time of the source systems. These source systems are live production databases. Any slow down or locking could effect company’s bottom line.
Some validations are done during Extraction:
Reconcile records with the source data
Make sure that no spam/unwanted data loaded
Data type check
Remove all types of duplicate/fragmented data
Check whether all the keys are in place or not
Step 2) TransformationData extracted from source server is raw and not usable in its original form. Therefore it needs to be cleansed, mapped and transformed. In fact, this is the key step where ETL process adds value and changes data such that insightful BI reports can be generated.
It is one of the important ETL concepts where you apply a set of functions on extracted data. Data that does not require any transformation is called as direct move or pass through data.
In transformation step, you can perform customized operations on data. For instance, if the user wants sum-of-sales revenue which is not in the database. Or if the first name and the last name in a table is in different columns. It is possible to concatenate them before loading.
Data Integration Issues
Following are Data Integrity Problems:
Different spelling of the same person like Jon, John, etc.
There are multiple ways to denote company name like Google, Google Inc.
Use of different names like Cleaveland, Cleveland.
There may be a case that different account numbers are generated by various applications for the same customer.
In some data required files remains blank
Validations are done during this stage
Filtering – Select only certain columns to load
Using rules and lookup tables for Data standardization
Character Set Conversion and encoding handling
Conversion of Units of Measurements like Date Time Conversion, currency conversions, numerical conversions, etc.
Data threshold validation check. For example, age cannot be more than two digits.
Data flow validation from the staging area to the intermediate tables.
Required fields should not be left blank.
Cleaning ( for example, mapping NULL to 0 or Gender Male to “M” and Female to “F” etc.)
Split a column into multiples and merging multiple columns into a single column.
Transposing rows and columns,
Use lookups to merge data
Using any complex data validation (e.g., if the first two columns in a row are empty then it automatically reject the row from processing)
Step 3) LoadingLoading data into the target datawarehouse database is the last step of the ETL process. In a typical Data warehouse, huge volume of data needs to be loaded in a relatively short period (nights). Hence, load process should be optimized for performance.
Types of Loading:
Initial Load — populating all the Data Warehouse tables
Incremental Load — applying ongoing changes as when needed periodically.
Full Refresh —erasing the contents of one or more tables and reloading with fresh data.
Load verification
Ensure that the key field data is neither missing nor null.
Test modeling views based on the target tables.
Check that combined values and calculated measures.
Data checks in dimension table as well as history table.
Check the BI reports on the loaded fact and dimension table.
ETL ToolsThere are many ETL tools are available in the market. Here, are some most prominent one:
1. MarkLogic:
MarkLogic is a data warehousing solution which makes data integration easier and faster using an array of enterprise features. It can query different types of data like documents, relationships, and metadata.
2. Oracle:
Oracle is the industry-leading database. It offers a wide range of choice of Data Warehouse solutions for both on-premises and in the cloud. It helps to optimize customer experiences by increasing operational efficiency.
3. Amazon RedShift:
Amazon Redshift is Datawarehouse tool. It is a simple and cost-effective tool to analyze all types of data using standard SQL and existing BI tools. It also allows running complex queries against petabytes of structured data.
Here is a complete list of useful Data warehouse Tools.
Best practices ETL processFollowing are the best practices for ETL Process steps:
Never try to cleanse all the data:
Every organization would like to have all the data clean, but most of them are not ready to pay to wait or not ready to wait. To clean it all would simply take too long, so it is better not to try to cleanse all the data.
Never cleanse Anything:
Always plan to clean something because the biggest reason for building the Data Warehouse is to offer cleaner and more reliable data.
Determine the cost of cleansing the data:
Before cleansing all the dirty data, it is important for you to determine the cleansing cost for every dirty data element.
To speed up query processing, have auxiliary views and indexes:
To reduce storage costs, store summarized data into disk tapes. Also, the trade-off between the volume of data to be stored and its detailed usage is required. Trade-off at the level of granularity of data to decrease the storage costs.
Summary:
ETLstands for Extract, Transform and Load.
ETL provides a method of moving the data from various sources into a data warehouse.
In the first step extraction, data is extracted from the source system into the staging area.
In the transformation step, the data extracted from source is cleansed and transformed .
Loading data into the target datawarehouse is the last step of the ETL process.
Google Looker Studio Tutorial (Ex Data Studio) 2023
Want to convert your data into a format that allows you to gain informative, easy-to-read, and easy-to-share insights on your business?
Google Looker Studio, formerly Google Data Studio, is a data visualization platform that allows you to connect, visualize, and share your data story.
Turn your analytics data into easy-to-understand reports for free with Looker Studio.
In this Google Looker Studio tutorial, we’ll show you how to create a dashboard that visualizes traffic data.
We’ll cover the basics, which include connecting to your data and creating charts from Looker Studio’s arsenal.
We’ll also look at tips that will help you get started to become a pro-Looker Studio user.
Here is an overview of what we’ll cover in this Google Looker Studio tutorial:
Let’s get started!
How to Start Building a DashboardFor this Google Looker Studio tutorial, we’ll be working towards recreating the following dashboard:
This dashboard is a simplified version of the following dashboard which we’ll discuss in more detail later on.
Don’t worry if you don’t have a website because everything we’ll cover will apply to any kind of data you have.
To start, log in to your Gmail account and head to chúng tôi In Looker Studio, there are multiple ways to start building a dashboard.
The first method is to start with a template from the Template Gallery.
We’ll be creating a dashboard from scratch in this Google Looker Studio tutorial, so we’ll select Create → Report.
Alternatively, you could also select Blank Report with the + sign thumbnail from the list of templates in the Template Gallery.
From here, we’ll send our data to Looker Studio with the help of Connectors. You can pull data from 1000+ data sets with the use of over 730 connectors available (as of the time of this article).
💡 Top Tip: See the list of all available connections by going to the Looker Studio Connector Gallery.
In our window, we can easily see the list of connectors starting with the Google Connectors at the top, followed by the Partner Connectors.
Here you can connect to various other tools in Google’s platform, like Google Analytics, Google Ads, Google Sheets, BigQuery, and more.
🚨 Note: If your dashboards are using UA, you might want to consider migrating Looker Studio dashboards data from UA to GA4.
For this Google Looker Studio tutorial, our data is in Google Sheets, so we’ll use the Google Sheets connector.
🚨 Note: If you would like to follow along with our Google Looker Studio tutorial, you can go ahead and copy our dataset to verify if you’re doing the steps correctly.
Great! We have successfully added our data to Looker Studio.
From here, we’ll see a default table which is Looker Studio’s way of showing you that it has pulled data from your data source successfully.
Now, let’s explore the interface a bit.
Adding Charts to a DashboardWhen building a dashboard, we’ll utilize various visualization types that Looker Studio collectively calls charts.
Here, we have all the common types like Tables, Scorecards, Time series, Bar charts, Pie charts, Geo charts, and more.
Next, let’s change how our dashboard looks by opening the Theme and Layout pane.
Going to the Themes tab, we are presented with a list of themes that offer different default colors for your background, text, and charts.
For example, if we select the Constellation theme, you’ll see the background change to a dark gray.
For this Google Looker Studio tutorial, we’ll use the Edge theme.
Next, we’ll increase the size of our dashboard so that we have more space to play with our data.
Now, let’s look at how to build and modify a chart.
Start with creating a Table.
Next, pay attention to the two columns on the right side of the screen.
Let’s start with discussing the properties panel. Since we currently selected a table, you’ll see that we have the Chart as the title.
Our properties panel is usually divided further into the Setup and Style tabs. The setup tab is where to build the chart – what data is displayed, while the style tab is where we format the chart.
Next, we also have the Data panel.
The Data panel is where you can access data from your data source. It is organized into dimensions, metrics, and other types of data.
Dimensions are attributes of your data. Think of categories, colors, or anything that is in text form. You can easily distinguish different data types by looking at the icon next to them.
Looking at the browsers our visitors were using to get to our website, you’ll see that it has an icon that says ABC. This is how you know your data is a dimension.
On the other hand, metrics are the data that you can use for your calculations. These are usually the data that have numbers.
If we look at the revenue, you’ll see that metrics have a 123 icon.
Other data types include geolocation with a globe icon, dates with a calendar icon, and links with a chain icon.
In our dataset, examples of each are the Country, Date, and Landing page, respectively.
Next, let’s learn how we can modify the data in this table.
If we wanted to see the number of total users by city, we need to replace the session source/medium dimension and record count metric.
A helpful tip in finding the data you want to add to your chart, especially if you’re working with a large dataset, is by using the search bar at the top of the data panel.
To demonstrate, let’s search for the total users.
This opens a mini data panel where we can search for or select the data we want to display directly.
Great! Let’s delete this chart for now and move on to recreating the dashboard we showed earlier.
Adding Scorecards to a DashboardLooking at the reference dashboard for this Google Looker Studio tutorial, we have a banner on top with a group of scorecards.
Scorecards are like snapshots of your key performance indicators or KPIs. You usually use them when you want to highlight a specific metric.
To start, let’s create the banner.
Next, we’ll get to adding the Scorecards to our dashboard.
First, let’s replace the metric displayed with the Total users. You can use any of the methods we showed earlier.
Now, we need to change the format of our scorecard to fix this contrast issue.
Changes to what we want to be displayed are done in the setup tab, while changes to how they are displayed are done in the style tab.
Go to the Background and Border section in the Style tab and change the background color to Transparent.
Great! Our background is gone. However, we can’t see the numbers because both the banner and text color are dark.
To fix this, go to the Labels section and change the font color to white.
Great! Now, rather than rebuilding all these scorecards one by one, try this little trick.
Doing this allows you to retain the formatting we changed in the style tab, and you only need to change the metric displayed in the setup tab.
Alternatively, you can also utilize the copy-paste functionality using your keyboard.
Since we have 4 scorecards on our banner, let’s select the two scorecards we have so far by holding down the CTRL key, then copy and paste them using the keyboard shortcuts.
Let’s move on to the next section of our Google Looker Studio tutorial.
How to View the Number of Website VisitorsThe first section of our dashboard mainly has a scorecard showing the number of website visitors and a line graph showing the trends in the number of users per month.
The big number displayed is just another scorecard, but with a few more tweaks than what we made earlier.
Start another scorecard with the Total users metric.
Lastly, if you started with a new scorecard, set the background color to Transparent.
Next, let’s add our line chart. While we have a section for line charts in the list of available charts, we’ll be using a Time series chart since we’ll be looking at trends per month.
Stretch the chart a bit to cover a decent portion of our dashboard, then make the lines a bit heavier. Set the line weight to about 5, and change the series color to black.
Next, we also want to remove the grid lines and the background.
Go to the Grid section and change the grid color to Transparent.
For the background, try and test if you can remove it on your own. Don’t worry if you need to go back to the previous section of our Google Looker Studio tutorial! (Hint: Look at the section names.)
To complete this section, let’s put a section header by adding Text.
Add the text “How many users visited our website?” in all caps, then increase the font size to 20px, and bold the text.
You can reorder, resize, reformat, or reposition the charts that we have so far, but at this point, we can see that we have successfully recreated the first section of our reference dashboard.
Now, let’s move on to the second section.
Here, we have a bar chart showing the top 5 cities that bring the most visitors to our website, along with a geo chart highlighting the number of users per country.
Bar and geo chart in the second section of the reference dashboard
Let’s start by inserting the Bar Chart.
Insert this bar chart at the bottom of our scorecard. Set the dimension to City and the metric to Total users.
Next, remove the background and gridlines, which we are confident that you can do at this point in our Google Looker Studio tutorial.
Next, we’ll reduce the number of bars shown in our chart because we are only interested in showing the top 5 cities.
To do this, go to the Bar chart section and change the number of bars to 5. Next, change the color in the Color bar section to black.
Great! Now, we could start building our geo chart from scratch, but we’ll show you another trick you can use.
Select the geo chart and watch Looker Studio’s magic in how it easily transforms your data.
Now, let’s change the color settings of our geo chart.
Set the maximum color value to black, and the minimum color value to pink to have some contrast.
Let’s recreate the last part of our dashboard.
How to See the Most Popular Content on a WebsiteThe last portion of our dashboard has a table that shows us the most popular content on our website.
To create this in our dashboard, let’s start with a table.
Next, put the Page path and screen class in the dimensions section, then the Total users, Views, Engaged sessions, and Revenue in the metrics section.
Adding Data ControlFinally, we only have two things left to do to fully recreate our Google Looker Studio tutorial reference dashboard.
If you had been paying close attention to the elements of our reference dashboard, then you’ve noticed that it has a date range control at the top of the banner.
A date range control is a type of data control that helps you to only display the data you want based on the date range you specify.
Essentially, a data control filters your data and a date range control filters them by date.
Notice the various changes this date range control made to our dashboard.
First, the scorecard showing the number of users displays a smaller value due to the narrower date range.
Next, the four scorecards on our banner have an additional line showing if there were improvements or dips in our KPIs.
Lastly, the line chart date range changed and another series is shown comparing the number of total users from the previous 93 days.
Insert the date range control at the top-right portion and let’s style it a bit.
Set the background color to transparent, change the border color to white, then set the border radius to 15.
Finally, change the font color to white to fix the contrast issue.
Now, the final thing to add is an image of our logo at the top-left corner of the dashboard.
After you select the logo and insert it into your dashboard, you’ll see that there is a contrast issue again. To change this, let’s set the background to transparent again.
Note that this only works if your image or logo already has a transparent background.
💡 Top Tip: While Google provides a myriad of charts we can use in our dashboards, you should check out the Google Looker Studio Community Visualizations to customize your reports further and show your data more clearly.
There you have it! We have completely recreated our reference dashboard, and have learned how to build a basic dashboard in Looker Studio.
There is one last thing we’d like to share with you before finishing this Google Looker Studio tutorial.
Dashboards with Actionable InsightsThere are a lot of other things we could do with Looker Studio. Apart from building dashboards that display information, we also want to build dashboards that provide actionable insights.
This means creating dashboards that can guide your users and let them not only understand the state of the data, you’re analyzing but also give them an idea of where it is heading.
Remember the more comprehensive dashboard we showed earlier in this Google Looker Studio tutorial?
This is an example of a dashboard with actionable insights, as we not only can see the number of visitors coming to our site but also if we’re hitting our targets.
This tiny bit of information makes all the difference and can elevate your dashboard from a regular one to a dashboard that provides actionable insights.
💡 Top Tip: Check out our guide on the Google Looker Studio Calculated Fields to help you build dashboards with actionable insights.
FAQ How can I start building a dashboard in Google Looker Studio?To start building a dashboard, log in to your Gmail account and go to chúng tôi From there, you can create a dashboard from scratch or use a template from the Template Gallery.
What data sources can I connect to in Looker Studio?Looker Studio offers over 730 connectors, including Google Analytics, Google Ads, Google Sheets, BigQuery, and more. You can connect to various data sources and combine data from different places into a single location.
How do I view the number of website visitors in Looker Studio?To view the number of website visitors, you can use scorecards to display key performance indicators (KPIs). You can customize the scorecards by selecting the desired metric, changing the formatting, and duplicating them for multiple metrics.
SummaryTo summarize, in this Google Looker Studio tutorial, we looked at how to connect our data to Looker Studio and recreated a dashboard to learn how to build one ourselves.
We learned how to add various chart types to the dashboard like tables, scorecards, time series charts, bar charts, and geo charts, as well as other elements like shapes, texts, and images.
We also learned how to format what they show and how the data is shown.
If you would like to go further, why not make your dashboards interactive? Maybe you would also like to check out our top 3 Looker Studio dashboard enhancements to take your dashboards up a notch.
What Is Data Lineage And Why Use Data Lineage?
Introduction
Are you too busy fixing bugs in your C-level dashboards or are you spending too much time chasing them down? Do different departments struggle to agree on the data that is required throughout the company? Are you having trouble assessing the potential impact of a possible migration?
A data lineage may be the solution to your data quality problems. A data lineage system improves data visibility and traceability across the entire data stack. It also simplifies the task of communicating about the data your organization relies on.
Also read: Top 7 Work Operating Systems of 2023
What is Data Lineage?The Data Lineage shows the data flow through various systems and transformations. Data in modern data stacks is not only stored in application databases.
It flows from one application and then to the next, and finally to data warehouses where it can be transformed and consumed by any number of downstream applications.
This data flow allows each system access to data in a way that makes sense. Source applications can optimize to improve the performance of read/write transactions. Reporting clients have access to denormalized data, which makes it easy for queries.
This convenience comes at a cost of visibility and traceability. After the data leaves the source database, it is subject to any number of transformations.
This layer can mask the true data. Many reporting teams struggle to determine the source of their data or to identify the correct data to use in a report. They might ask the application team to clarify the situation.
The team may tell them that the data isn’t there because the terms used to describe a piece of data have changed after the transformation.
Solving bugs and problems can take longer and will require the involvement of three teams, the reporting team, and the data warehouse team. The data team typically takes on the task of finding the root cause of the problem. They will then have to go through the version control and try to solve it. This can also slow down the development process for new reports.
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Data Lineage: Why?A data lineage system allows you to have your cake while still having it. You can have both separation of roles, the performance of a data warehouse, and clear data understanding across all of your systems and teams.
You can trace data throughout the system with clear data understanding and traceability. This can be used to confirm that no personally identifiable data (PII), is being exported from the system and being consumed in places it shouldn’t.
You can also see which data is being used downstream and the impact of possible changes or migrations. You can also identify any unutilized information and allow for easy cleanup of columns or tables.
Data lineage systems improve communication and reduce incident response time by increasing data understanding.
The data lineage system eliminates confusion about the source of data in reports and makes it easy for all parties to understand where it came from. This system speeds up the resolution of errors as well as new development.
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Different types of data lineageThere are two major types of data lineage systems: Active and Passive.
An active data-lineage system is considered “active” as you have to create it. This can be done by either programming the necessary source and transformation information into your system or tagging the data with the appropriate metadata.
Apache Atlas is an example of such an active system. An active data lineage system that is properly configured can give you traceability of your data down to the smallest detail.
These benefits require constant maintenance and updating. This can add complexity to your overall data infrastructure, and can also be time-consuming.
A passive system that attempts to understand your data by itself is the alternative. Passive systems examine the data coming out of the data warehouse.
A passive system uses pattern recognition to identify where the data comes from and what it is being transformed. This can be useful for simple data sets and simpler transformations. However, it can produce inaccurate results.
Another type of passive data lineage system is the parsing-based system. This generates lineage data through reverse-engineering your database warehouse.
A parsing-based system allows you to see exactly where your data is coming from and what it is being used for. Datafold illustrates this type of system. Datafold analyses all DQL code within your data warehouse and generates lineage graphs.
This lineage is much more detailed than table-level and allows you to see which column a piece of data was sourced from, and where it was consumed.
This detail allows for quicker outage response times, faster troubleshooting, as well as reducing the number of production-ready changes.
Datafold has many integrations. Datafold is easy to use and accessible via the Datafold HTTP1_ API. A parsing-based data lineage system, as long as it supports your data warehouse or related systems, is the best choice for implementation and maintenance.
It’s all great but how does it affect my day-to-day? Let’s take a look at this.
Solving bugs or problems can take longer, and it will require the involvement three teams: the reporting team and data warehouse team.
The data team typically takes on the task of finding the root cause of the problem. They will then have to go through the version control and try to solve it. This can also slow down the development process for new reports.
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Data Lineage: Why?A data lineage system allows you to have your cake while still having it. You can have both separation of roles , the performance of a data warehouse, and clear data understanding across all of your systems and teams.
You can trace data throughout the system with clear data understanding and traceability. This can be used to confirm that no personally identifiable data (PII), is being exported from the system and being consumed in places it shouldn’t.
You can also see which data is being used downstream and the impact of possible changes or migrations. You can also identify any unutilized information and allow for easy cleanup of columns or tables.
Data lineage systems improve communication and reduce incident response time by increasing data understanding.
The data lineage system eliminates confusion about the source of data in reports and makes it easy for all parties to understand where it came from. This system speeds up the resolution of errors as well as new development.
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Different types of data lineageThere are two major types of data lineage systems: Active and Passive.
An active data-lineage system is considered “active” as you have to create it. This can be done by either programming the necessary source and transformation information into your system or tagging the data with the appropriate metadata.
Apache Atlas is an example of such an active system. An active data lineage system that is properly configured can give you traceability of your data down to the smallest detail.
These benefits require constant maintenance and updating. This can add complexity to your overall data infrastructure, and can also be time-consuming.
A passive system that attempts to understand your data by itself is the alternative. Passive systems examine the data coming out of the data warehouse.
A passive system uses pattern recognition to identify where the data comes from and what it is being transformed. This can be useful for simple data sets and simpler transformations. However, it can produce inaccurate results.
Another type of passive data lineage system is the parsing based system. This generates lineage data through reverse-engineering your database warehouse.
A parsing-based system allows you to see exactly where your data is coming from and what it is being used for.
Datafold illustrates this type of system. Datafold analyses all DQL code within your data warehouse and generates lineage graphs.
This lineage is much more detailed than table-level and allows you see which column a piece of data was sourced from, and where it was consumed. This detail allows for quicker outage response times, faster troubleshooting, as well as reducing the number of production-ready changes.
Datafold has many integrations. Datafold is easy to use and accessible via the Datafold HTTP1_ API. A parsing-based data lineage system, as long as it supports your data warehouse or related systems, is the best choice for implementation and maintenance.
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How can Data Lineage ensure day-to-day data quality?A data lineage system provides visibility and traceability that is better than ever. Three clear benefits can be seen in your day-to-day operations.
It increases your team’s response time. It doesn’t take hours to find the root cause of an error in a reporting. This is possible with the cooperation of multiple teams. Errors can be quickly identified and corrected if you have complete visibility of the data flow across your entire data stack.
It allows the creation and maintenance of a common vocabulary. The application team understands what views are and where they come from when the report team discusses them.
The application team can see what data has been aggregated to create the dashboard that informs company outlook and decisions.
Over time, terminology discrepancies can be reduced or eliminated, which allows for better communication throughout the company.
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Wrap-upThis article explains what data lineage is and why you might use it. We also explain the various types of data lineage available, as well as how data lineage can help improve data quality every day. The addition of a data-lineage system to your data stack will increase transparency and reduce headaches for the entire organization.
Subquery Unveils Cosmos Data Indexing Via Juno Integration
Singapore – June. 9th, 2023 – Fresh from its recent announcement of its beta version implementation for Avalanche, SubQuery has delivered its latest iteration of multi-chain connectivity with the addition of support for the Cosmos ecosystem, starting with Juno.
From today, Juno and other CosmWasm developers will be able to access the beta of the same fast, flexible, and open indexing solution widely used across the Polkadot and Avalanche ecosystems.
This includes the open-source SDK, tools, documentation, developer support, and other benefits developers receive from the SubQuery ecosystem, including eligibility to participate in SubQuery’s Grants Programme. Additionally, Juno is accommodated by SubQuery’s managed service, which provides enterprise-level infrastructure hosting and handles over 400 million requests each day.
Juno is a decentralised, public, and permissionless layer 1 for cross-chain smart contracts. Via its powerful hub employing a standardised communication protocol, it aims to be the internet of blockchains, benchmarking inter-chain security for network participants. Built on Cosmos, Juno facilitates blockchain interoperability in an ever-growing multi-chain environment.
Jake Hartnell, the founder of another up-and-coming Cosmos chain, Stargaze, as well as a core Juno contributor, has shared “We were elated to learn that SubQuery was expanding their invaluable data indexing services over to Juno. Our shared mission is to provide new teams with an environment to scale without hindrance and we know that SubQuery saves developers time and effort, allowing them to accelerate even faster.”
SubQuery provides decentralised data indexing infrastructure to developers building applications on multiple layer-1 blockchains. As an open data indexer that is flexible and fast, the open indexing tool helps developers build APIs in hours and quickly index chains with the assistance of dictionaries (pre-computed indices).
Engineered for multi-chain applications, SubQuery’s tools allow developers to organize, store, and query on-chain data for their protocols and applications. SubQuery eliminates the need for custom data processing servers, helping developers focus on product development and user experience.
SubQuery has already established itself as a data indexing solution on Polkadot, serving hundreds of millions of queries daily for projects like Moonbeam and Acala. This growth has spurred SubQuery to develop a priority list of six other Layer-1 blockchains they intend to serve in 2023.
The addition of the Cosmos ecosystem alongside Polkadot and Avalanche aligns with SubQuery’s focus on networks that are also designed with a multi-chain outlook. While SubQuery’s Cosmos implementation begins with Juno, the product will eventually work with any CosmWasm-based chain, including Cronos, OKExChain, Osmosis, Secret Network, Stargaze, and Injective.
In just a few months, Juno applications will be able to decentralise their SubQuery infrastructure completely with the SubQuery Network. The SubQuery Network will index and serve project data to the global community in an incentivised and verifiable manner. Designed to support any SubQuery project from any layer-1 network, including Juno and Cosmos, developers can leverage the scale of the unified SubQuery Network from launch.
Key ResourcesA full developer onboarding guide will be released tomorrow
About SubQuerySubQuery is a blockchain developer toolkit facilitating the construction of Web3 applications of the future. A SubQuery project is a complete API to organise and query data from Layer-1 chains. Currently servicing Polkadot, Substrate, Avalanche, Terra, and Cosmos (starting with Juno) projects, this data-as-a-service allows developers to focus on their core use case and front-end without wasting time building a custom backend for data processing activities. In the future, the SubQuery Network intends to replicate this scalable and reliable solution in a completely decentralised manner.
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