Castore  DeRose

Castore DeRose


What is Footprint Analytics | Platform Visualize Blockchain Data

In this post, you'll learn What is Footprint Analytics (Platform Visualize Blockchain Data), How to Use Footprint Analytics?

1. What is Footprint Analytics

Footprint Analytics is an all-in-one analysis platform to visualize blockchain data and discover insights.

It cleans and integrates on-chain data so users of any experience level can quickly start researching tokens, projects and protocols. With over a thousand dashboard templates plus a drag-and-drop interface, anyone can build their own customized charts in minutes. Uncover blockchain data and discover the value trend behind the project.

Footprint Analytics Basics

Data Analysis

  • Footprint lets users convert raw data tables into charts without any code with an easy-to-use drag-and-drop interface.
  • Anyone to get started with blockchain analytics quickly; Footprint provides thousands of datasets that encourage curiosity, creativity and data-driven decision making.
  • Users can find the dashboard they need based on topic, chain, or category of data. Footprint supports forking any open analytics table on the platform with one click, helping users create and manage personalized dashboards easily.
  • Quickly replicate and find new inspiration with hundreds of community-created dashboards.

Data available on Footprint Analytics

Footprint Analytics gets logs directly from chains and parses the contracts:

  • Blockchain logs are parsed into events & transactions
  • Row data is aggregated into a cashflow table
  • Data is constantly updated

Data you can explore on Footprint Analytics currently:

16 Chains: Ethereum, BNB Chain, Polygon, Avalanche, Fantom, Arbitrum,Celo, Harmony , loTeX, DFK Chain, Hive, Moonriver, Moonbeam, Boba , Solana and ThunderCore.

Multiple fields data: NFT, GameFi, DeFi

Functionality and Features

Create charts with no coding or technical requirements

Footprint makes exploring blockchain data simple with its drag-and-drop user experience. No need for SQL queries or coding to explore blockchain data—anyone can discover and present actionable DeFi insights.

Data visualization: Storytelling with data

Our solution for visualizating data lets you analyze the market and present your findings, no matter what your audience's experience with blockchain. There are more than types of charts to choose from. Fork charts with one click Our solution provides rich data analytics templates that support forks with any open analytics table on the platform with one click, helping you easily create and manage your personalized dashboards. You can also share your data tables and dashboards with your partners or social channels.

Data Features

  • Supports cross-chain data: Subscribers get access to cross-chain and multi-project data, enabling them to create and analyze charts on all aspects of the blockchain.
  • Support address analysis: Analysts can easily analyze wallet addresses on the chain to see active addresses, new users, user retention rates, and more.

2. How to Use Footprint Analytics

2.1. How to search and view dashboard on Footprint Analytics(Viewer)

Quick search for community dashboards, charts, and articles.

Subscribe, copy and share your favorite dashboards.

Add your favorite dashboards to your watch list.

Follow your favorite analysts.

Footprint Analytics Dashboard Features

Multiple chains of data can be seen in one dashboard

Cross-chain analysis.

You can do drill down analysis, from overviews to single chain data to individual project.

2.1. How to create a dashboard on Footprint(Creator)

For beginners:



Advanced creation feature


On-chain and off-chain cross analysis

CSV upload, on-chain and off-chain analysis.

CSV download.

2.3. Get Started with Creating a Chart

Step 1: On the top right hand-side of the screen, click on Create. A pop-up menu will appear.

The image above shows where the Create button is on Footprint Analytics.

Step 2: Select New Chart.

The image above shows users where to find the New Chart option.

The gif above demonstrates how to make a new chart based on Steps 1 and 2.

Step 3: Select the template Avalanche: Token Price & Trading Volume on the bottom on the screen.

The image above shows users which template we are using for this example.

Step 4: Click on the text next to the blue filter icon to change the Filters of your chart in the upper left corner of the screen

Step 5: Type in your desired filters. For this example, we will be changing AVAX to sol. Type in sol into the search bar and select sol. Don’t forget to click on the X mark on AVAX to exclude it from our filters.

Step 6: Click on Update and your chart will rearrange itself accordingly.

Step 7: Make sure you click on Save to finalize your chart.

The image above walks users through choosing filters for charts based on Steps 4 to 7.

The gif above demonstrates the use of filters for charts as shown in Steps 4 to 7.

Step 8: Change the chart Name and the Description before clicking Save again.

The image above highlights the Name, Description and Save button that users can change when saving a chart.

The gif above demonstrates how to save a chart as shown in Steps 7 and 8.

Then, the chart is done. You can access charts you’ve made under My Analytics at the top of your screen. Click on Chart and Select your chart.

The image above shows users where to find any charts they have created.

The gif above demonstrates how users can find previously-made charts.

Using a Dataset for a Chart

Besides the above one-click duplication method, you can also create your own charts step by step. For creative graphs or data, this is a more flexible way to go.

Use case: Let's make a line chart of the number of Ethereum transactions per day.

Step 1: Click on Create and select New Chart.

The image above shows users how to create a new chart based on Steps 1 and 2.

Step 2: Select Ethereum. A drop-down menu will appear.

Step 3: Select the data table ethereum_transactions.

The image above shows users which graph to select and where to find the graph for the purpose of our example in this article.

The gif above demonstrates Steps 1 to 3, the process of getting to ethereum_transactions for the purpose of this guide.

Step 3: In the “ethereum_transactions” table, there is one record for each transaction, which means you can directly count the number of records and group by day. Click Summarize in the upper right corner, and then pick Count of rows.

The image above shows users how to summarize by count of rows according to Steps 1 to 3.

Step 4: Under the Group-by field, scroll down and select block_timestamp, which needs to be grouped by day.

The image above shows users to scroll down for block_timestamp under the Group-by field for Step 4.

The image above is a continuation of Step 4, showing users how to select by day under block_timestamp.

The gif above combines Steps 3 and 4, navigating how to select Count of rows and block_timestamp by day.

Always make sure to save your chart.

2.4. Visualize Your Data with Different Charts

Before we start, we should note: you don’t have to use every chart type to do proper analysis. Most of the time you’ll only need tables and line charts.


In many cases, you want to see many metrics at once, list their exact values, and be able to sort those metrics. You can do this by adding or removing columns, or adding multiple filters to make it easier for you to find certain values.

The image above shows users what a Table looks like.

If you also want to summarize the grouping of rows or switch columns and rows, using a Pivot Table would be better.

Line Charts

You may also often want to present data as a time series to see how a particular metric has changed over time, and you would use a line chart for in this case. A time series is any chart that involves tracking data by time, such as day, month or year. A line chart provides a simple shape for the data, making it easy to see if the numbers are trending up, or if they are cyclical, or what the maximum value was for the past X days, among other things.

The image above shows users what a Line Chart looks like.

Let’s walk through some common situations to help you choose the right visualization chart to show your findings.

When You Only Have One Value

Number Visualization

When you only have one value, especially for static numbers, or at least a number that doesn’t change too frequently, use the (appropriately named) Number Visualization, which is good for at-a-glance values.

The image above shows an example of what Number Visualization looks like.

Keep in mind that a single number may lack context, so it’s best used on a dashboard that provides that context.

The image above shows an example of a Dashboard combining different charts together to provide context for your data.

Comparing Metrics

Often, we want to see how multiple values stack up against each other. The most common comparisons are a single metric’s performance over time and a single metric across other dimensions.

Static Comparisons

For measures that won’t change, like responses on a survey or an annual report, you can compare values with a Bar Chart (sometimes called a Column Chart). If you have a lot of different items you need to compare, you could try switching to a row chart to see if that makes the bars more legible for you.

The image above shows users an example of what a Bar Chart looks like.

One Measure Over Time

When you want to compare and emphasize two sequential values of a metric, or have a number that can be broken down by time, you can use a Trend Chart. Trend Charts show the current value of the metric and the previous value of that metric at whichever interval you’re tracking it at.

The image above shows users an example of what a Trend Chart looks like.

If you don’t need to emphasize the most recent data, consider a time series instead, so that the shape of the metric over time can be seen. This is especially useful if the most recent data is uncharacteristic of larger trends. Trends are also great for situations in which teams need to look at a metric weekly and roughly know its behavior.

Line charts are the classic format for time series, but you can also present series values as a Bar Chart or an Area Chart.

Multiple Measures Over Time

You can layer two time series on a line chart, with each line sharing the y-axis. If your measures have different scales or units of measurement, then you can use a combined graph with two different y-axes to highlight this difference.

This image shows users an instance where a chart has two y-axes, both showing different data being tracked despite them both revolving around a monthly basis.

Showing The Relationship Between Measures

Sometimes you’ll want to see how two different measures correlate with each other.

The most basic way to see a relationship is to plot one variable along the x-axis and the other variable along the y-axis, and see if a pattern emerges. This graph is known as a Scatter Plot Chart. You’ll often see scatter plots used with data that hasn’t been summarized or aggregated. Each dot on the chart represents an individual record in the data.

The image above shows how each dot on the chart represents its own individual data separate from the other dots.

If you want to introduce a third variable, you could change the size of each dot to reflect the value of the additional variable, turning a scatter plot into a Bubble Chart.

The image above shows users an example of a Bubble Chart.


A breakout shows the composition of a measure. Breakouts are categories that make up a bigger set of data and can include Pie Charts, Area Charts, and Bar Charts.

A Metric With Several Groups or Categories

Pie Charts show how several parts make up a whole.

The above image is an example of a Pie Chart.

While the Line Chart is more suitable for data with a trend over time, the Pie Chart shows the percentage of a category in the whole. You could even use a Dynamic Pie Chart to combine them both together. You can see how the values and pie charts change over time, instead of a new pie chart needing to be created every day.

The gif above shows users how the values change over time in the graph as reflected by the Pie Chart.

Categorical Breakouts Over Time

If you need to show how a number changes over time, and show the composition of that number at each interval, then consider using a Stacked Area Chart or Stacked Bar Chart.

The above image is an example of a Stacked Bar Chart.

Relative Changes Among Categories Over Time

If you need to see how different categories change relative to each other over time, regardless of the specific numbers, you could use percentages in your stacked bar charts.

The image above shows the percentage option being used to represent the data on the y-axis in the Stacked Bar Chart.

There are so many different customization options and real-life uses for each of the mentioned types of charts. 

2.5. How to Create a New Dashboard

Creating Your First Dashboard

Let's begin. To create our first dashboard:

Step 1: Click on the Create button on the top right hand-side of the screen.

The image above shows where the Create button is.

Step 2: Once the pop-up menu appears, select New Dashboard.

The image above shows where the New Dashboard button is.

Step 3: The Dashboard editor opens in a new tab, select the Add chart button.

The image above shows where the Add chart option is.

Step 4: The add chart tab appears on the right, showing several options to add charts quickly. In this example, we will be demonstrating how to add existing charts made by others but usable by everyone. Click on Community charts.

The image above shows where the Community Charts tab is.

Step 5: Select the charts one at a time to add them into our dashboard by clicking on the +Add button next to each respective chart.

The image above shows where the charts are in the Community charts tab.

The gif above demonstrates how to create a dashboard following Steps 1 to 5.

Step 6: You can also add charts in the My charts tab. This will pull up and display any charts you have created in your account.

The image above shows where the My charts tab is.

Step 7: You can also search for a specific chart by name or by its URL link in the search box.

The image above shows where the Search box is.

Step 8: Make sure to click onH Save to finalize your completed dashboard.

The image above shows where the save button is.

With the dashboard in place, we’re ready to design the dashboard in the next part.

Designing Your Dashboard

With the dashboard ready, we can start designing your dashboard to personalize and organize for a cleaner and more legible look.

Editing Positions and Sizes

You can move your charts and text boxes by simply clicking on them and then dragging them anywhere within the dashboard. Organize your dashboard items accordingly to improve readability.

The gif above demonstrates how to move your dashboard items around.You can also resize your dashboard items using their bottom right corner handles. Click and drag the handle to resize and adjust your charts and text boxes.

The gif above demonstrates how to resize your dashboard items.

Adding a Text Box

A Text Box provides a space for you to display plain text in your dashboard. This is a great tool to illustrate the logic of metrics calculation, add introduction text to the dashboard, add notes to correlate your charts, and more.

To add a text box:

Step 1: Click on Add a media box on the top right hand-side of the screen.

The image above shows where you can find the option to add a text box.

Step 2: Select Text. A text box will appear on your dashboard. You can edit its position and size similar to charts, by dragging on their respective bottom-right handles.

The image above shows what a text box would look like in your dashboard.

The gif above demonstrates how to add a text box from Step 1 to 2.

Visualization Options

Dashboard items each have a submenu that pops up on their top right corners upon hovering your mouse on them. One of the submenu items is the Visualization options, which is used for modifying dashboard item attributes, such as its Display, Axes, and Labels. To use the Visualization Option:

Step 1: Hover your mouse over your desired chart or text box. The submenu will pop up on the top right corner of this chart or text box.

Step 2: Click on the Palette icon.

The image above shows users where the palette icon is on the submenu of the hovered chart or text box from Step 2.

Step 3: Pop-up settings will appear. You can set how you want your chart or text box to be presented in your dashboard with settings including Display, Axes and Labels.

The image above shows the different options users can set upon clicking on the Visualization options as indicated by the palette icon in Step 1.

Step 4: Scroll down and make sure you click Done once finished with your settings to save any changes.

The image above shows users where to find the Done button based on Step 4.

The gif above demonstrates how to access and navigate the visualization options in your charts.

Adding Filters to the Dashboard

Filters can be added to our dashboards to allow for a wider range of applications, as well as to avoid any repetitive work.

Filters are usually applied in:

Creating a Dashboard with a Filter

To set up filters on a new dashboard:

Step 1: Create a dashboard without any filter.

Dashboard items can have filters individually, but for more complex applications, it is more practical to just create a dashboard filter that is applied to the items inside of it. You can then set which item you want to associate with the dashboard filter.

Note: If the charts you selected already have filters on them, you can delete the filters manually. To delete filters:

Step 1: Click on Advanced.

Step 2: Under Filter, click on the x mark on your unwanted filters.

Step 3: Make sure you Save to keep any changes made.

The image above shows users how to manually delete filters that may come with some charts.

The gif above demonstrates how to delete filters from charts manually.

Step 2: For multiple charts, you can add a filter linkage. Click on add a filter on the top right corner, and select the type of filter you would like. You can choose Time, ID, Location, and Other Categories. For the purpose of this example, we will select Time.

The image above shows where the Add a filter button is located and the available filters you can use.

Step 3: Select the type of filter. In this case, we will use Month and Year. You will be provided with different options for each filter as well.

The image above shows that you need to further specify your filters, with this one being Month and Year.

Step 4: Select the chart you want to associate with the drop-down filters on each chart, then set your Default Value as needed before clicking Done to finalize your settings.

The image above shows users where to set their filters and default value.

The gif above demonstrates how to link charts together using the same filter based on Steps 1 to 4.

Removing Filters

You can also remove existing filters. To remove filters:

Step 1: Click on the filter name on the top left corner of your screen under your dashboard name.

The image above shows where the filter name is in your dashboard.

Step 2: Click on the Remove button on the right hand-side of the screen under Settings. Don’t forget to click Done to save your settings.

The image above shows where the Remove button is.

Always remember to Save your dashboard, even after saving your settings.

2.6. Share Your Dashboards

Sharing Your Work

All of the charts and dashboards you create are shareable to those with and without Footprint accounts via link. To share your work:

Step 1: Click on the Share icon in the upper right corner of your screen. You can choose the way you want to share your chart with additional settings.

The image above shows users where the share icon is once you’ve accessed your desired chart or dashboard.

The gif above demonstrates the first step to sharing your charts and dashboards based on Step 1.

Note: Dashboards and charts are not shareable in editing mode. Ensure you have saved your work before sharing it.

Step 2: Choose either Public link or Public embed. The descriptions can be found in the image below. Once you have chosen one, click on the Copy icon to the right of the link. Make sure you paste the link where you would like it before copying anything else. You can even share via any of the four social media platforms Facebook, Twitter, Telegram or LinkedIn by clicking on their respective logos.

The image above defines a public link and a public embed and shows users where the copy icon and social media logos are located.

Setting What You’re Sharing

You can set your charts and dashboards to private for when you prefer keeping your work to yourself.

Click on the button under Set to private to activate the feature, setting this particular chart or dashboard to private. The rest of your charts and dashboards are still public to the Footprint Analytics community, making them visible and usable for other members that may search for topics relating to your title.

The image above shows where the Set to private button is.

Note: The privacy option is only available for membership holders.

Removing Watermarks

Watermarks are removable with the option found under the Set to private feature. To activate, just click the button next to the text Remove watermark.

Note: The Remove Watermark function is only available for membership holders.

The image above shows users what the Remove watermark option looks like.

The image above shows a chart with a watermark on it. This is the default product.

The image above shows the same chart after the watermark is removed.

Was this article helpful? If you have any advice or feedback for this tutorial, please feel free to let us know in the community or contact us on Twitter or Telegram, we will try to do more and better.

Click and tell us your ideas!

2.7. Four Steps to Embed Public Link

Step1: Click the share button

Step2: Copy the codes for embedment

Step 3: Choose a template for color scheme (under development)

(coming soon)

Step4: Insert the codes into your website

For more information, please visit: Website / Twitter / Telegram

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I hope this post will help you. Don't forget to leave a like, comment and sharing it with others. Thank you!

#blockchain #cryptocurrency #bitcoin 

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Your Data Architecture: Simple Best Practices for Your Data Strategy

If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.

If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.

In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.

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Getting Started With Data Lakes

Frameworks for Efficient Enterprise Analytics

The opportunities big data offers also come with very real challenges that many organizations are facing today. Often, it’s finding the most cost-effective, scalable way to store and process boundless volumes of data in multiple formats that come from a growing number of sources. Then organizations need the analytical capabilities and flexibility to turn this data into insights that can meet their specific business objectives.

This Refcard dives into how a data lake helps tackle these challenges at both ends — from its enhanced architecture that’s designed for efficient data ingestion, storage, and management to its advanced analytics functionality and performance flexibility. You’ll also explore key benefits and common use cases.


As technology continues to evolve with new data sources, such as IoT sensors and social media churning out large volumes of data, there has never been a better time to discuss the possibilities and challenges of managing such data for varying analytical insights. In this Refcard, we dig deep into how data lakes solve the problem of storing and processing enormous amounts of data. While doing so, we also explore the benefits of data lakes, their use cases, and how they differ from data warehouses (DWHs).

This is a preview of the Getting Started With Data Lakes Refcard. To read the entire Refcard, please download the PDF from the link above.

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Top 10 Big Data Tools for Data Management and Analytics

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What exactly is Big Data? Big Data is nothing but large and complex data sets, which can be both structured and unstructured. Its concept encompasses the infrastructures, technologies, and Big Data Tools created to manage this large amount of information.

To fulfill the need to achieve high-performance, Big Data Analytics tools play a vital role. Further, various Big Data tools and frameworks are responsible for retrieving meaningful information from a huge set of data.

List of Big Data Tools & Frameworks

The most important as well as popular Big Data Analytics Open Source Tools which are used in 2020 are as follows:

  1. Big Data Framework
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  3. Data Visualization Tools
  4. Big Data Processing Tools
  5. Data Preprocessing Tools
  6. Data Wrangling Tools
  7. Big Data Testing Tools
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  9. Security Management Tools
  10. Real-Time Data Streaming Tools

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Visual Analytics basically breaks the complex data in a simple way.

The human brain is fast and is built to process things faster. So Data visualization provides its way to make things easy for students, researchers, mathematicians, scientists e

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