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In this blog I’ll show you how you can compare data from different timeframes within the same visualization. You can get amazing Power BI insights using this great technique with time-related DAX. You may watch the full video of this tutorial at the bottom of this blog.
I discovered this approach from the Enterprise DNA Forum. One of the members actually suggested this technique as a solution to a question on how to deal with a certain stock report and compare data. Let’s look at the sample data given in the forum.
The stock report in question was designed to show the stock position at the end of the month for the last 6 months based on a Stock History table.
The report allows you to look at data within a certain timeframe using a date slicer. Basically, you can see everything prior to the date on the slicer.
The user wants to maximize the amount of Power BI insights he can get by looking at the results from the current date and comparing it to a different timeframe. But he also wants to see an extended time period for that 2nd timeframe.
Here’s the problem. There is a natural context occurring within the report page, making it difficult to do that. There has to be a different solution other than just using the Power BI page-level filters.
Let’s look at the problem using a line graph.
With the usual filters, the two different timeframes will look like the visualization below. The blue and yellow lines each represent the same dates, but from different timeframes.
Our user only wants to show the 1st set of data points up to a certain point, while also showing a 2nd set of data points over an extended timeframe. This means that the blue line will have to be much shorter than the yellow line. The blue line basically stops showing data at a certain point.
In a way, this could be seen in terms of a forecast. It’s like projecting data into the future for the 2nd timeframe but only seeing data up to a certain point in the current timeframe.
The strategy suggested in the forum involves using two date tables. The 1st date table will represent the blue line, while the 2nd one will represent the yellow line in the visualization.
The Stock History table is the fact table in this model. The 1st Dates table has an active relationship with Stock History.
But the Dates2 table also has an active relationship with the fact table. This means that the natural context will be coming from these two places. This is the key to this solution.
You have to remember that the timeframe depends on the slicer. This means that the slicer determines the data shown by the lines.
Looking at what’s going on behind the 1st line, you’ll see a simple SUM function applied to the Stock History.
As for the 2nd line, a time intelligence function is applied.
It’s a basic time intelligence pattern that uses the CALCULATE function on the Stock History table. Then, it uses SAMEPERIODLASTYEAR referencing the Dates2 table.
Though we’re not using the 1st Dates table and are only using the 2nd one, both of them will still show up in the visualization. That’s because again, both of them have active relationships with the fact table. Any time a filter is applied, both date tables will be filtered as well.
Now, you can also simplify things within your model. For example, you can choose to take just the Date column and get rid of the others.
That was a unique way to compare data from two different timeframes. It opens up other possibilities in getting as many Power BI insights as possible.
You can use this approach when working with budgeting or scenario analysis, for example. You can run different scenarios across different timeframes and try to see the expected returns. This shows how much you can do with Power BI if you add some creativity to your approach.
All the best,
Sam
You're reading Comparing Data Points From Different Timeframes
How Is Aws Athena Different From Other Databases?
This article was published as a part of the Data Science Blogathon.
IntroductionAmazon Athena is an interactive query service based on open-source Apache Presto that allows you to analyze data stored in Amazon S3 using ANSI SQL directly. In addition, it is serverless, so there is no infrastructure to manage and maintain, and you only pay for the queries you run.
To start with Athena, you need to define the schema of your data stored in Amazon S3; you’re ready to start querying it with SQL . The schema is determined using the Amazon Glue Data Catalog, which allows you to create a unified metadata repository across multiple services.
It can be used alongside or instead of traditional databases depending on the specific business and technical scenario. But, first, it is essential to understand the differences and why you would choose one over the other.
Differentiating Athena from Database and WarehouseAthena works more like a query engine than a particular database. This means that:
Compute and storage are decoupled: Databases store data at rest and provide the resources needed to perform queries and calculations. Each of these comes with direct and indirect overhead costs. It does not store data – instead, storage is managed entirely on Amazon S3. The Athena query service is fully managed, so resources are automatically allocated by AWS as needed to execute a query.
No DML interface: No need to model data with Athena. I/O is the bottleneck of virtually every database, but it’s not a problem with Athena. And since you don’t have to waste I/O bandwidth on data modelling, you can focus all computing resources on query processing.
Advantages of Using AthenaServerless Design Reduces IT Overhead: Amazon Athena is serverless, meaning there is no user-side infrastructure to manage or configure. Using Athena is as simple as defining a query, and you only pay for the questions you run. As a result, there are no additional IT costs and no clusters to manage.
Based on SQL: You can use Athena to run SQL queries against the desired table that is configured in the Glue data catalogue or data sources that you can connect to using the Athena Query Federation SDK. For users who already know SQL, there is no learning curve to get started.
Open architecture (no vendor lock-in): Athena enables open access to data rather than lock-in to a specific tool or technology. This manifests itself in various ways;
Ubiquitous Access: Because your data is stored in an S3 bucket and the schema is defined in the Glue Data Catalog, you can switch between query engines that can read from these sources without redefining the schema or creating a separate copy of the data.
Separated storage and computing resources: Athena has a complete separation of computing and memory resources. Data is stored in your Amazon S3 account, while Amazon Web Services provide Athena computation as a shared resource among all Athena users.
Open file formats: Unlike many high-performance databases, Athena does not use a proprietary file format but supports standard open source formats such as Apache Parquet, ORC, CSV, and JSON.
Low cost: Athena’s pricing model is based on terabytes of scanned data. You can control and keep costs down by checking only the data you need to answer a specific query (this can be done using data splitting – see below).
Access to all your data: Most organizations process only 30 to 35 percent of their data into a traditional data warehouse due to the high operational and infrastructure costs of constantly resizing database clusters. Because this storage costs a fraction of what you would pay to keep the same data in a data warehouse, you can handle larger volumes of data without worry.
Custom Connectors: Amazon Athena lets you run SQL queries across multiple data sources, which can drive various business intelligence and analytics processes. You can use JDBC to connect Athena with BI and machine learning tools.
Limitations of AthenaNo built-in insert/update/delete operations: Because Athene is a query engine with no DML interface, upsets can be difficult.
The optimization is limited to queries: You can optimize your questions, not your data. However, your data is already stored in Amazon S3; performing transformations to use Athena may affect other users using the exact information for other purposes.
Multi-tenancy means pooled resources: All Athena users receive a similar SLA for queries at any time. In other words, the entire global user base is “competing” for the same resources – and although AWS provides more as needed, this could mean that query performance fluctuates depending on other people’s usage.
No indexing: Indexes are integrated into traditional databases but do not exist in Athena. This makes joining large tables a demanding operation that increases the load on Athena and negatively impacts performance. For example, running a query by key requires scanning all the data and searching for the desired key in the result list. This is solved using Upsolver lookup tables.
Partitioning: Efficient queries in Athena require partitioning of the data. Maintaining the number of partitions in the park that meet your performance needs is essential. Every 500 divisions scanned will add 1 second to your query.
Other Products Required with AthenaAthena is never a standalone product but rather always part of a package that includes:
Amazon S3: Athena queries run directly on Amazon S3, so this is where your data will be stored.
Glue Data Catalog: A centralized managed schema that allows you to replace or augment Athena with other services as needed (for example, with Amazon Redshift Spectrum).
ETL Tools: While Athena can run almost any query out of the box, reducing costs and improving performance requires following a set of performance tuning best practices. The traditional way is to use Spark, which can process large volumes of unstructured data; however, this option requires considerable coding knowledge. Some solutions offer managed Spark as a service that simplifies the infrastructure aspects but doesn’t remove the coding overhead.
Use CaseAthena helps analyze unstructured, semi-structured, and structured data stored in Amazon S3. Data can be stored in CSV, JSON or columnar formats such as Apache Parquet and Apache ORC. It can also be used to run queries using ANSI SQL, and this does not require the user to aggregate or load data into Athena.
It can be integrated with Amazon Quick Sight for data visualization purposes to help generate reports and explore data using business intelligence tools such as SQL clients that interface with JDBC or ODBC driver.
Athena can also be integrated with the AWS Glue Data Catalog. AWS Data Catalog provides persistent metadata storage for user data in Amazon S3. This way, tables can be created, and data can be queried in Athena, all based on a centralized metadata repository available throughout the user account. It can also be integrated with ETL (Extract, Transform, Load) and data discovery features included in the AWS glue catalog.
Conclusion
It does not store data. Instead, storage is managed entirely on Amazon S3. The Athena query service is fully managed, so resources are automatically allocated by AWS as needed to execute a query.
Because your data is stored in an S3 bucket and the schema is defined in the Glue Data Catalog, you can switch between query engines that can read from these sources without redefining the schema or creating a separate copy of the data.
Indexes are integrated into traditional databases but do not exist in Athena. This makes joining large tables a demanding operation that increases its load and negatively impacts performance. For example, running a query by key requires scanning all the data and searching for the desired key in the result list. This is solved using Upsolver lookup tables.
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Google May Treat Press Releases Different From Other Content
Google tries to distinguish press releases from other types of content and may treat them differently in search results, says Search Advocate John Mueller.
This topic is discussed during the Google Search Central SEO hangout recorded on February 19.
SEO Michael Lewittes mentions to Mueller that he regularly sees news outlets cover press releases and rank above the original source.
Sometimes the original source is a reputable organization such as the Associated Press or Reuters.
Lewittes asks how can news outlets republish the same information and rank above those sources?
In response, Mueller says it may have to do with how Google processes press releases. He shares that they may be handled differently from other types of content.
Google’s John Mueller on Press Releases in Search ResultsMueller says Google tries to recognize situations where the same article is being republished and tries to treat it “accordingly” in search.
“It’s hard to say. I think in most cases we try to recognize situations where exactly the same article is being republished and then to treat that accordingly in search by showing the original or the one we think it might have come from.
But there are lots of cases where we can’t recognize that completely. And it’s sometimes a matter of – this content is here but someone also wrote about the same topic somewhere else – and then we have those two viewpoints.
I don’t think there’s anything technical or anything specific that is happening there where it’s like – if it gets republished here then we just take that one.
But any time you have content that is syndicated it can happen that our systems don’t recognize that we should be showing this version instead of the other version.”
SEO Robb Young joins the conversation and tries to pull more information out of Mueller regarding this topic.
He plainly asks if there is any difference between a press release and other types of content. In Google’s estimation, that is.
This is where Mueller says Google tries to recognize press releases from other content. It’s understood that press releases are pieces of content that get republished across many sites.
Without being too specific, Mueller says Google acts “accordingly” to the understanding that press releases will get published elsewhere.
“I think to some extent we probably try to recognize press releases and understand that these pieces of content that are just republished in lots of places and try to act accordingly to that.
Mueller then goes on to offer some insight into how press releases are treated in Google News.
He’s not sure if Google News treats press releases differently from search results. But, at one point, Google News did try to understand when multiple sites were writing about the same topic.
“I don’t know if Google News does something slightly different than web search in that regard though. So that might be something that kind of plays in there.
I know from some book about Google a long time back where, in the early days of Google News, they definitely tried to recognize the situation where people were writing about the same topic, or writing with the same content, and trying to understand does that make this topic or this article more important than other topics.
But within web search it’s mostly a matter of these are different HTML pages and we find content there and we try to index it.”
Hear Mueller’s response in the video below:
For more coverage of this hangout please refer to the articles below:
Guide To Different Laravel Commands
Laravel Commands
Laravel command is the most popular and widely used PHP framework, which is based on MVC (Model View Controller) Architecture. It is an open-source web application development framework and was created by Taylor Otwell. As of now, the most recent release of the laravel framework is Laravel 5.7, which was released in September 2023.
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Web development, programming languages, Software testing & others
Prerequisites for starting with laravelYou should know basic/intermediate of:
PHP
HTML / CSS
Working of MVC Model
DB knowledge
Composer and ArtisanA composer is a tool which allows the user to create any project with respect to a given framework. It consists of all the dependencies and libraries.
Artisan is the command-line interface of Laravel. It has a set of commands which will be discussing now in details which helps in building a web application.
Artisan command syntax:
php artisan [ options] [ arguments] Basic Laravel CommandsSome of the basic laravel commands are mentioned below:
1. To list out all the Artisan commandsIt starts with giving the syntax of executing the command i.e.
php artisan [command] [options] [arguments]where,
options: It can be used like –h (for help), -q (for quiet), -v (for version) etc.
commands: It can be used as per command name followed by options and arguments. Few of the commands are migrate, serve, make, help, etc.
This command is used to get help on a particular command name. Let’s say if you would like to know more about the usage and meaning of the command, you can get it by making use of the help utility provided by Artisan.
php artisan help makes:authwhere
make: auth: It is the command name for which we would like to know more.
php artisan –versionThis command will list out the version of the Laravel framework which you are using.
php artisan downThis command is used to put the laravel application under maintenance mode.
php artisan upThis command is used to bring back the laravel application up and running.
php artisan envThis command will tell you the environment in which the laravel application is running.
php artisan view: clearThis laravel command will clear all the compiled view files.
php artisan route: listThis command will list all the registered routes.
php artisan route: clearThis command will clear all the route cache file.
php artisan route: cacheThis command creates a route cache file for faster route registration
Intermediate Laravel CommandsSome of those kinds of requiring intermediate laravel commands are mentioned below:
php artisan serveThis command is used to start a laravel project, and by default, the application will be hosted at localhost with port number 8000
php artisan make: model EduCBAThis command is used to create a new model class.
If we execute the command, php artisan list, we will find a couple of makes commands. In order to see the list of make commands, please press the shift + pg down key on your keyboard to navigate through all the pages.
php artisan make: controller UserControllerThis command will create a new controller file in the below folder:
App/Http/Controllers
php artisan make- request EduCBA_BlogPostThis command is used to create a new form request class in the below folder:
app/Http/Requests
php artisan make seeder EduCBASeederThis command is used to create a new database seeder class.
php artisan make middleware Middleware_NameThis command is used to create a new middleware class.
php artisan make: policy OurPolicyThis command is used to create a new policy class.
php artisan make: mail [email protected]This command is used to create a new email class.
php artisan make: event EduCBA_Analytics_Enrolled php artisan make: command compose_emailThis command is used to create a new artisan Laravel command
Advance Laravel Commands php artisan make: model Project --migration --controller --resourceThis command is used to create a new migration file for the model(migration), create a new controller for the model(controller) and to have a resource controller for the generated controller.
php artisan make:listener SendEnrollement_NotificationThis command is used to create a new event listener class.
php artisan migrate [--bench="vendor/package"] [--database[="..."]] [--path[="..."]] [--package[="..."]] [--pretend] [--seed]This command is used to do Database migration.
php artisan vendor: publishThis command is used to publish any publishable assets from vendor packages.
php artisan make provider OurServiceProviderThis command is used to create a new service provider class.
php artisan migrate:make name [--bench="vendor/package"] [--create] [--package[="..."]] [--path[="..."]] [--table[="..."]]This command is used to create a new migration file
php artisan make job [email protected]This command is used to create a new job class.
Recommended ArticlesWe hope that this EDUCBA information on “laravel commands” was beneficial to you. You can view EDUCBA’s recommended articles for more information.
10 Cybersecurity Practices To Protect Data Centers From Attacks
The top cybersecurity practices that data centers should start adopting to protect from cyber attack
Do you have the impression that a company is apologizing for a security violation involving sensitive data or revealing a hacker attack every day? Not just you, either. The frequency of cyberattacks and cybercrimes is alarmingly rising. Data centers are most under cyberattack and protecting data centers from attacks is important.
And not just large conglomerates are experiencing data breaches; attacks on small firms are also on the rise as hackers become aware that these companies may not have put in place a strong cybersecurity defense. According to cybersecurity defense startup BullGuard, 43 percent of small enterprises have no cybersecurity practices strategy at all. These hazards increased as remote employment became the norm during the pandemic. In this article, we shall discuss some of the top cybersecurity practices that data centers have to imply to protect their data and prevent any kind of cyberattack. Let us look into those now.
1.EducationA hack may be avoided much more easily than it can be fixed. Recovery of sensitive data that has been lost due to a ransomware attack can be a difficult and time-consuming task. Ransomware attacks can be effectively stopped before they cause serious harm by educating employees on fundamental security, personal cybersecurity, and the frequency of cyber dangers. Your staff members need to be aware that they can be the object of malicious individuals looking to gain access to your business.
2.Better Passwords and Authentication 3.Secure WiFiWith the rise of remote working, it’s critical that your staff securely encrypt their networks as well. It may seem obvious for a business to have a secured, encrypted, and hidden WiFi network. Your security and that of your employees go hand in hand. The company’s mainframe can easily be accessed by hacking into a worker’s distant network.
4.Know Your CompanyUtilize a simple resource: your knowledge. Consider your business and the areas that hackers are most likely to target. Are they more interested in your customer databases or intellectual property than they are in the private information of your employees? The most likely targets should be located and well-protected.
5.More the Backups, the BetterUse a straightforward resource: your knowledge. Think about both the areas of your company and those that hackers are most likely to target. Are they more concerned with your intellectual property or customer databases than they are with the personal data of your employees? Locate and adequately guard the targets that are most likely to be attacked.
6.Anti-Virus SoftwareEven the most skilled employees err on occasions. Computers that have anti-virus and anti-malware software installed are better protected overall, especially from phishing attempts.
7.Updated SoftwareAccording to the National Cyber Security Centre of the UK, obsolete software is indirectly responsible for more than 80% of attacks. The most recent patches are the sole thing keeping the best antivirus and anti-malware software up to date. Failure to apply fixes will give hackers access to the system’s vulnerabilities.
8.Secure Physical DevicesCompany laptops should be secured with passwords or pins, much like you lock the doors when you leave your workplace. Employees who have left the company should get their laptops returned. Consider each computer at work as a potential entrance to your business.
9.Better to Always be Safe 10.Always have a PlanIt costs a lot to hire your cybersecurity team as a small- or medium-sized business owner. Fortunately, several free resources may assist you in creating a fundamental cybersecurity plan and guide what to do in the event of an attack.
From Petroleum Engineering To Data Science: Jaiyesh Chahar’s Journey
Introduction
Let’s get into this thrilling and compelling conversation with Jaiyesh.
Interview Excerpts with Jaiyesh Chahar AV: Please introduce yourself and share your educational journey with us.Jaiyesh: Hi, I am Jaiyesh Chahar, a Petroleum Engineer turned Data Scientist. I have done my bachelor’s in petroleum engineering from the University of Petroleum and Energy Studies, Dehradun. After that, I pursued my master’s from IIT ISM Dhanbad, and there I decided to take Machine Learning as my minor. From there, my journey of becoming a Data Scientist started. After that, I worked as a Petroleum Data Scientist for an Oil and Gas Startup. And later, I joined Siemens as a Data Scientist.
AV: What made you decide to become a Data Scientist? AV: You have a specialization in Petroleum; what made you switch? What steps did you take to gain the necessary skills and knowledge to succeed in the field of Data Science?Jaiyesh: I started by using Data Science as a tool for solving problems in Petroleum Industry. So, I was a Petroleum Engineer who knew Data Science. And regarding the steps, the initial step was learning how to code using python. Started with python, followed by useful libraries of python like Numpy, pandas, and Matplotlib. Then Statistics, machine learning, and at last deep learning. Also, at each step, a lot of practice is required.
AV: What is the biggest challenge you have faced in your career as a Data Scientist? How did you overcome it?Jaiyesh: My biggest challenge was to get a job as a fresher without any experience, but here my petroleum background helped me because there are very few people who have knowledge of oil and gas as well as data science. So that mixture helped me to get my first job.
AV: You are one of the co-founders of “Petroleum From Scratch.” What was the inspiration behind it?Jaiyesh: We started Petroleum from Scratch during covid times 2023. A lot of organizations started during that time, and they were charging hefty amounts for providing training to petroleum students/engineers. And also oil and gas market was at its lowest point, as crude prices went below zero. So a lot of layoffs happened in the oil and gas industry. So, to help professionals and students, we came up with Petroleum From Scratch, where we share knowledge free of cost.
AV: After working at Siemens for over a year, can you describe a recent project you have worked on, and what were some key insights or takeaways?Jaiyesh: So, one of my recent projects is for a giant automotive company, where we built a complete pipeline for the detection of faulty parts in the manufacturing unit. In this project, not only the Data Science part but the software piece was also delivered by us. So, this project showed me the importance of knowing software pipeline development, even being a data scientist.
Tips for Data Scientist Enthusiasts AV: What are habits that you swear by which have led you to be successful?Jaiyesh: Consistency and showing up daily are key habits that can help in achieving success in any area of life. When we consistently show up and put in the effort, we are more likely to make progress and see results over time.
This is especially true when it comes to learning new skills or developing new habits. By consistently practicing or working on something every day, we can build momentum and make steady progress towards our goals. This can help us stay motivated and avoid getting discouraged or giving up too soon.
In addition to consistency, other habits that can contribute to success include setting clear goals, prioritizing tasks, staying organized, and maintaining a positive attitude. By combining these habits with consistency and daily effort, we can create a powerful formula for achieving success in any area of life.
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