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Cross-Platform, Cli App To Carry out Varied Operations On String

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Cross-Platform, Cli App To Carry out Varied Operations On String




Cross-Platform, Cli App To Carry out Varied Operations On String

sttr is command line software program that permits you to rapidly run numerous transformation operations on the string.

// With enter immediate
sttr
// Direct enter
sttr md5 "Good day World"
// File enter
sttr md5 file.textual content
sttr base64-encode picture.jpg
// Studying from completely different processor like cat, curl, printf and so forth..
echo "Good day World" | sttr md5
cat file.txt | sttr md5
// Writing output to a file
sttr yaml-json file.yaml > file-output.json

Fast set up

You may run the beneath curl to put in it someplace in your PATH for straightforward use. Ideally will probably be put in at ./bin folder

curl -sfL https://uncooked.githubusercontent.com/abhimanyu003/sttr/fundamental/set up.sh | sh

Webi

MacOS / Linux

curl -sS https://webi.sh/sttr | sh

Home windows

curl.exe https://webi.ms/sttr | powershell

See right here

Homebrew

If you’re on macOS and utilizing Homebrew, you’ll be able to set up sttr with the next:

brew faucet abhimanyu003/sttr
brew set up sttr

Snap

sudo snap set up sttr

Arch Linux

yay -S sttr-bin

Scoop

scoop bucket add sttr https://github.com/abhimanyu003/scoop-bucket.git
scoop set up sttr

Go

go set up github.com/abhimanyu003/sttr@newest

Manually

Obtain the pre-compiled binaries from the Launch! web page and duplicate them to the specified location.

  • After set up merely run sttr command.
// For interactive menu
sttr
// Present your enter
// Press two enter to open operation menu
// Press `/` to filter numerous operations.
// May also press UP-Down arrows choose numerous operations.
sttr -h
// Instance
sttr zeropad -h
sttr md5 -h
  • Working with information enter.
sttr {command-name} {filename}
sttr base64-encode picture.jpg
sttr md5 file.txt
sttr md-html Readme.md
sttr yaml-json file.yaml > file-output.json
  • Taking enter from different command.
curl https: //jsonplaceholder.typicode.com/customers | sttr json-yaml
  • Chaining the completely different processor.
sttr md5 hi there | sttr base64-encode
echo "Good day World" | sttr base64-encode | sttr md5

Encode/Decode

  • [x] ascii85-encode – Encode your textual content to ascii85
  • [x] ascii85-decode – Decode your ascii85 textual content
  • [x] base32-decode – Decode your base32 textual content
  • [x] base32-encode – Encode your textual content to base32
  • [x] base64-decode – Decode your base64 textual content
  • [x] base64-encode – Encode your textual content to base64
  • [x] base85-encode – Encode your textual content to base85
  • [x] base85-decode – Decode your base85 textual content
  • [x] base64url-decode – Decode your base64 url
  • [x] base64url-encode – Encode your textual content to url
  • [x] html-decode – Unescape your HTML
  • [x] html-encode – Escape your HTML
  • [x] rot13-encode – Encode your textual content to ROT13
  • [x] url-decode – Decode URL entities
  • [x] url-encode – Encode URL entities

Hash

  • [x] bcrypt – Get the Bcrypt hash of your textual content
  • [x] md5 – Get the MD5 checksum of your textual content
  • [x] sha1 – Get the SHA1 checksum of your textual content
  • [x] sha256 – Get the SHA256 checksum of your textual content
  • [x] sha512 – Get the SHA512 checksum of your textual content

String

  • [x] camel – Rework your textual content to CamelCase
  • [x] kebab – Rework your textual content to kebab-case
  • [x] decrease – Rework your textual content to decrease case
  • [x] reverse – Reverse Textual content ( txeT esreveR )
  • [x] slug – Rework your textual content to slug-case
  • [x] snake – Rework your textual content to snake_case
  • [x] title – Rework your textual content to Title Case
  • [x] higher – Rework your textual content to UPPER CASE

Traces

  • [x] count-lines – Rely the variety of traces in your textual content
  • [x] reverse-lines – Reverse traces
  • [x] shuffle-lines – Shuffle traces randomly
  • [x] sort-lines – Type traces alphabetically
  • [x] unique-lines – Get distinctive traces from listing

Areas

  • [x] remove-spaces – Take away all areas + new traces
  • [x] remove-newlines – Take away all new traces

Rely

  • [x] count-chars – Discover the size of your textual content (together with areas)
  • [x] count-lines – Rely the variety of traces in your textual content
  • [x] count-words – Rely the variety of phrases in your textual content

RGB/Hex

  • [x] hex-rgb – Convert a #hex-color code to RGB
  • [x] hex-encode – Encode your textual content Hex
  • [x] hex-decode – Convert Hexadecimal to String

JSON

  • [x] json – Format your textual content as JSON
  • [x] json-escape – JSON Escape
  • [x] json-unescape – JSON Unescape
  • [x] json-yaml – Convert JSON to YAML textual content
  • [x] json-msgpack – Convert JSON to MSGPACK
  • [x] msgpack-json – Convert MSGPACK to JSON

YAML

  • [x] yaml-json – Convert YAML to JSON textual content

Markdown

  • [x] markdown-html – Convert Markdown to HTML

Extract

  • [x] extract-emails – Extract emails from given textual content
  • [x] extract-ip – Extract IPv4 and IPv6 out of your textual content
  • [x] extract-urls – Extract URls your textual content ( we do not do ping verify )

Different

  • [x] escape-quotes – escape single and double quotes out of your textual content
  • [x] completion – generate the autocompletion script for the required shell
  • [x] interactive – Use sttr in interactive mode
  • [x] model – Print the model of sttr
  • [x] zeropad – Pad a quantity with zeros
  • [x] and including extra….

These are the few places the place sttr was highlighted, many due to all of you. Please be happy so as to add any blogs/movies you could have made that debate sttr to the listing.



Barack Obama DNC 2024 speech: What it was actually about

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Barack Obama DNC 2024 speech: What it was actually about


Throughout his 2024 Democratic Nationwide Conference handle on Tuesday evening, former President Barack Obama made no secret of his disdain for Donald Trump. However his speech was greater than only a partisan broadside: It was a philosophical temporary in protection of liberalism, a form of first-principles ethical argument that no different main conference speaker provided.

Liberal, on this context, doesn’t seek advice from the time period’s use in partisan American politics. It refers as an alternative to the centuries-old philosophical custom that sees politics as essentially oriented across the values of freedom and equality. Authorities, for liberals, exists to allow folks to stay based on their very own imaginative and prescient for his or her lives; it has no enterprise telling folks what god to worship or giving sure teams of individuals extra rights than others.

Obama was a totally liberal president, and Trump a totally intolerant one. This clearly troubled Obama — troubled him a lot, in actual fact, that he devoted this most high-profile speech to explaining why Trump have to be defeated not simply politically but additionally philosophically.

The stakes of 2024, based on Barack Obama

Obama is hardly the primary individual to declare Trump a mortal enemy of liberalism. Ever since 2016, there’s been a library’s price of books printed on liberalism’s Trump-induced disaster and the way it should be resolved.

A few of Obama’s remarks adopted this literature relatively intently. Like many, Obama sees Trump’s divisive political type versus liberalism’s core precept of equality: that every one residents need to be handled equally, every free to pursue a great life in the way in which they see match (so long as they don’t damage others in doing so).

Trump, Obama says, attracts an elemental distinction “between the true People, who in fact assist [Trump], and the outsiders who don’t.” And that Trump and his allies imagine “one group’s achieve is one other group’s loss,” and that “freedom means the highly effective can do just about no matter they please.”

This, Obama says, is unsuitable. It’s unsuitable not only for Democrats and progressives, however for People — residents of a rustic whose very existence grew out of liberal thought. The Harris marketing campaign, in his telling, is tapping right into a elementary liberal impulse that permeates the American physique politic.

“The overwhelming majority of us don’t need to stay in a rustic that’s bitter and divided,” Obama stated. “We wish one thing that’s higher. We need to be higher. And the enjoyment and pleasure surrounding this marketing campaign tells us that we’re not alone.”

Dwelling this liberal imaginative and prescient, for Obama, means accepting the range inherent to a big society made up of individuals with all kinds of beliefs and worldviews: recognizing that “our fellow residents deserve the identical grace we hope they’ll lengthen to us.” It means understanding “true freedom” as one thing that provides all of us the precise “to make choices about our personal life [and] requires us to acknowledge that different folks have the precise to make choices which can be completely different than ours.” And it means seeing democracy as greater than “only a bunch of summary ideas and a bunch of dusty legal guidelines in a e-book someplace,” however relatively “the values we stay by.”

This, to be clear, shouldn’t be a case that Trump is un-American and thus will certainly lose: Obama took nice pains to emphasise that the race was nonetheless shut and will go both means. Somewhat, Obama is saying that what Trump stands for contradicts lots of the values that People declare to carry expensive — our core sense of what our nation is about and what it stands for. That America’s truest identification is liberal, and that this identification can transcend these issues that divide us.

I need to agree with that. However as I argue in my current e-book, there’s a deeply intolerant pressure in American politics — an ideology born out of the important contradiction between America’s state liberal beliefs and the truth of chattel slavery at its founding.

This custom, like Trump, rejects core liberal-democratic beliefs about equality. It’s no extra and no much less American than our loftier said beliefs; each symbolize genuine features of America’s identification, and each have triumphed at completely different instances all through our nation’s historical past.

The query, then, shouldn’t be whether or not American liberalism can reassert its naturally dominant place. It’s whether or not the America that Obama believes in will triumph on this spherical of a centuries-old battle towards an authoritarian twin.

Apple, Main League Baseball announce September “Friday Evening Baseball” schedule

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Apple, Main League Baseball announce September “Friday Evening Baseball” schedule



Report Highlights Rising Dangers in Delicate Knowledge Administration

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Report Highlights Rising Dangers in Delicate Knowledge Administration


The quantity of delicate knowledge that corporations are harbouring in non-production environments, like improvement, testing, analytics, and AI/ML, is rising, based on a brand new report. Executives are additionally getting extra involved about defending it — and feeding it into new AI merchandise is just not serving to.

The “Delphix 2024 State of Knowledge Compliance and Safety Report” discovered that 74% of organisations that deal with delicate knowledge elevated the quantity stored in non-production, also referred to as decrease, environments within the final yr. What’s extra, 91% are anxious about their expanded publicity footprint because of this, placing them prone to breaches and non-compliance penalties.

The quantity of client knowledge that corporations maintain is rising general as a result of progress within the variety of on-line customers and their ongoing digital transformation efforts. The IDC forecasts that by 2025, the worldwide datasphere will develop to 163 zettabytes, ten occasions the 16.1 zettabytes of information generated in 2016.

In consequence, the quantity of delicate knowledge, akin to private identifiable data, protected well being data, and monetary particulars, being saved can also be growing.

Delicate knowledge is usually created and saved in manufacturing, or dwell, environments just like the CRM or ERP, which have tight controls and restricted entry. Nevertheless, customary IT operations typically end in knowledge being copied a number of occasions into non-production environments, permitting extra personnel entry and growing the danger of breach.

The report’s findings have been the results of a survey of 250 senior-level workers at organisations with a minimum of 5,000 workers that deal with delicate client knowledge. It was carried out by software program supplier Perforce.

SEE: Nationwide Public Knowledge Breach: 2.7bn Data Leaked on Darkish Net

Over half of companies have already skilled a knowledge breach

Over half of respondents mentioned they’d already skilled a breach of delicate knowledge stored in non-production environments.

Different proof helps that the problem is worsening: a examine by Apple discovered that there was a 20% enhance in knowledge breaches from 2022 to 2023. Certainly, 61% of Individuals have discovered their private knowledge had been breached or compromised sooner or later.

The Perforce report discovered that 42% of the respondent organisations have skilled ransomware. This malware, particularly, is a rising menace globally; a examine from Malwarebytes printed this month discovered that international ransomware assaults elevated by 33% within the final yr.

A part of the issue is that international provide chains have gotten longer and extra complicated, growing the variety of potential entry factors for attackers. A report from the Identification Theft Useful resource Middle discovered that the variety of organisations impacted by provide chain assaults surged by greater than 2,600 proportion factors between 2018 and 2023. Moreover, payouts exceeded $1 billion (£790 million) for the primary time in 2023, making it an more and more profitable exploit for attackers.

AI is the most important wrongdoer in relation to insecure client knowledge

With corporations now adopting AI into enterprise processes, it’s turning into more and more troublesome to maintain management of what knowledge goes the place.

AI techniques typically require using delicate client knowledge for coaching and operation, and the complexity of the algorithms and potential integration with exterior techniques can create new assault vectors which are laborious to handle. In truth, the report discovered that AI and ML are the main causes of delicate knowledge progress in non-production environments, as cited by 60% of respondents.

“AI environments could also be much less ruled and guarded than manufacturing environments,” the report’s authors wrote. “In consequence, they are often simpler to compromise.”

Enterprise decision-makers are conscious of this danger: 85% report considerations about regulatory non-compliance in AI environments. Whereas many AI-specific rules are of their infancy, GDPR requires private knowledge utilized in AI techniques to be processed lawfully and transparently, and there are numerous relevant state level-laws within the U.S..

SEE: AI Govt Order: White Home Releases 90-Day Progress Report

The E.U. AI Act got here into pressure in August, which units strict guidelines on using AI for facial recognition and safeguards for general-purpose AI techniques. Corporations that fail to adjust to the laws face fines starting from €35 million ($38 million USD) or 7% of worldwide turnover to €7.5 million ($8.1 million USD) or 1.5% of turnover, relying on the infringement and measurement of the corporate. It’s thought that extra related AI-specific rules will spring up in different areas within the close to future.

Different considerations about delicate knowledge in AI environments, cited by over 80% of the respondents to the Perforce examine, embody utilizing low high quality knowledge as enter into their AI fashions, private knowledge re-identification, and theft of mannequin coaching knowledge, which might embody IP and commerce secrets and techniques.

Companies are anxious in regards to the monetary value of insecure knowledge

One other foremost purpose giant companies are so involved about insecure knowledge is the prospect of a hefty non-compliance positive. Client knowledge is extensively topic to increasing rules, like GDPR and HIPAA, which may be complicated and alter incessantly.

Many rules, like GDPR, apply penalties based mostly on annual turnover, so greater corporations face greater fees. The Perforce report discovered that 43% of respondents have already needed to pay up or modify non-compliances, and 52% have skilled audit points and failures associated to non-production knowledge.

However the price of a knowledge breach can go previous the positive, as a portion of the misplaced income comes from halted operations. A current Splunk report discovered that the largest explanation for downtime incidents was cybersecurity-related human errors, akin to clicking a phishing hyperlink.

Unplanned downtime prices the world’s largest corporations $400 billion a yr, with contributors together with direct income loss, diminished shareholder worth, stagnant productiveness, and reputational injury. Certainly, ransomware injury prices are predicted to exceed $265 billion by 2031.

In response to IBM, the common value of a knowledge breach in 2024 is $4.88 million, a ten% enhance over 2023. The tech big’s report added that 40% of breaches concerned knowledge saved throughout a number of environments, like public cloud and on-prem, and these value greater than $5 million on common and took the longest to determine and include. This reveals that enterprise leaders are proper to be involved about knowledge sprawl.

SEE: Almost 10 Billion Passwords Leaked in Greatest Compilation of All Time

Taking steps to safe knowledge in non-production environments may be resource-intensive

There are methods that knowledge saved in non-production environments may be secured, like by masking the delicate knowledge. Nevertheless, the Perforce report discovered that companies have a number of the reason why they’re reluctant to take action, together with that respondents discover it troublesome and time-consuming, and since it could decelerate the organisation.

  • Almost a 3rd are involved that it could decelerate software program improvement, as replicating manufacturing databases to non-production environments securely can take weeks.
  • 36% say masked knowledge may be unrealistic and due to this fact impression software program high quality.
  • 38% assume the safety protocols might inhibit the corporate’s potential to trace and adjust to rules.

The report additionally discovered that 86% of organisations enable knowledge compliance exceptions in non-production environments to keep away from the trouble of storing it securely. These embody utilizing a restricted knowledge set, knowledge minimisation, or gaining consent from the info topic.

Suggestions for securing delicate knowledge in non-production environments

The Perforce group outlined the highest 4 methods companies can safe their delicate knowledge in non-production environments:

  1. Static knowledge masking: Completely changing delicate values with fictitious, but lifelike equivalents.
  2. Knowledge loss prevention (DLP): A fringe-defence safety strategy that detects potential knowledge breaches and theft and makes an attempt to forestall them.
  3. Knowledge encryption: Quickly converts knowledge into code, permitting solely authorised customers to entry the info.
  4. Strict entry management: A coverage that categorises customers by roles and different attributes and configures these customers’ entry to datasets based mostly on these classes.

The authors wrote: “Defending delicate knowledge generally is just not simple to do. AI/ML provides to that complexity.

“Instruments that specialize in defending delicate knowledge in different non-production environments — improvement, testing, and analytics, for instance — are well-positioned that can assist you shield your AI surroundings.”

Tala: An Energetic Metadata Pioneer – Atlan

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Tala: An Energetic Metadata Pioneer – Atlan


Supporting a World-class Documentation Technique with Atlan

The Energetic Metadata Pioneers sequence options Atlan prospects who’ve accomplished a radical analysis of the Energetic Metadata Administration market. Paying ahead what you’ve realized to the subsequent knowledge chief is the true spirit of the Atlan group! So that they’re right here to share their hard-earned perspective on an evolving market, what makes up their fashionable knowledge stack, revolutionary use circumstances for metadata, and extra.

On this installment of the sequence, we meet Tina Wang, Analytics Engineering Supervisor at Tala, a digital monetary companies platform  with eight million prospects, named to Forbes’ FinTech 50 checklist for eight consecutive years. She shares their two-year journey with Atlan, and the way their sturdy tradition of documentation helps their migration to a brand new, state-of-the-art knowledge platform.

This interview has been edited for brevity and readability.

Might you inform us a bit about your self, your background, and what drew you to Knowledge & Analytics?

From the start, I’ve been very involved in enterprise, economics, and knowledge, and that’s why I selected to double main in Economics and Statistics at UCLA. I’ve been within the knowledge house ever since. My skilled background has been in start-ups, and in previous expertise, I’ve all the time been the primary individual on the info group, which incorporates establishing all of the infrastructure, constructing experiences, discovering insights, and plenty of communication with folks. At Tala, I get to work with a group to design and construct new knowledge infrastructure. I discover that work tremendous fascinating and funky, and that’s why I’ve stayed on this discipline.

Would you thoughts describing Tala, and the way your knowledge group helps the group?

Tala is a FinTech firm. At Tala, we all know immediately’s monetary infrastructure doesn’t work for a lot of the world’s inhabitants. We’re making use of superior know-how and human creativity to unravel what legacy establishments can’t or gained’t, so as to unleash the financial energy of the World Majority.

The Analytics Engineering group serves as a layer between back-end engineering  groups and varied Enterprise Analysts. We construct infrastructure, we clear up knowledge, we arrange duties, and we make certain knowledge is straightforward to search out and prepared for use. We’re right here to verify knowledge is clear, dependable, and reusable, so analysts on groups like Advertising and marketing and Operations can give attention to evaluation and producing insights.

What does your knowledge stack seem like?

We primarily use dbt to develop our infrastructure, Snowflake to curate, and Looker to visualise. It’s been nice that Atlan connects to all three, and helps our strategy of documenting YAML recordsdata from dbt and robotically syncing them to Snowflake and Looker. We actually like that automation, the place the Analytics Engineering group doesn’t want to enter Atlan to replace info, it simply flows by way of from dbt and our enterprise customers can use Atlan immediately as their knowledge dictionary.

Might you describe your journey with Atlan, thus far? Who’s getting worth from utilizing it?

We’ve been with Atlan for greater than two years, and I consider we had been one among your earlier customers. It’s been very, very useful.

We began to construct a Presentation Layer (PL) with dbt one yr in the past, and beforehand to that, we used Atlan to doc all our previous infrastructure manually. Earlier than, documentation was inconsistent between groups and it was usually difficult to chase down what a desk or column meant.

Now, as we’re constructing this PL, our aim is to doc each single column and desk that’s uncovered to the top person, and Atlan has been fairly helpful for us. It’s very simple to doc, and really simple for the enterprise customers. They’ll go to Atlan and seek for a desk or a column, they will even seek for the outline, saying one thing like, “Give me all of the columns which have folks info.”

For the Analytics Engineering group, we’re usually the curator for that documentation. After we construct tables, we sync with the service house owners who created the DB to know the schema, and once we construct columns we set up them in a reader-friendly method and put it right into a dbt YAML file, which flows into Atlan. We additionally go into Atlan and add in Readmes, in the event that they’re wanted.

Enterprise customers don’t use dbt, and Atlan is the one means for them to entry Snowflake documentation. They’ll go into Atlan and seek for a selected desk or column, can learn the documentation, and may discover out who the proprietor is. They’ll additionally go to the lineage web page to see how one desk is said to a different desk and what are the codes that generate the desk. The perfect factor about lineage is it’s totally automated. It has been very useful in knowledge exploration when somebody will not be acquainted with a brand new knowledge supply.

What’s subsequent for you and your group? Something you’re enthusiastic about constructing?

Now we have been wanting into the dbt semantic layer previously yr. It’ll assist additional centralize enterprise metric definitions and keep away from duplicated definitions amongst varied evaluation groups within the firm. After we largely end our presentation layer, we’ll construct the dbt semantic layer on prime of the presentation layer to make reporting and visualizations extra seamless.

Do you will have any recommendation to share together with your friends from this expertise?

Doc. Undoubtedly doc.

In one among my earlier jobs, there was zero documentation on their database, however their database was very small. As the primary rent, I used to be a robust advocate for documentation, so I went in to doc the entire thing, however that would stay in a Google spreadsheet, which isn’t actually sustainable for bigger organizations with hundreds of thousands of tables.

Coming to Tala, I discovered there was a lot knowledge, it was difficult  to navigate. That’s why we began the documentation course of earlier than we constructed the brand new infrastructure. We documented our previous infrastructure for a yr, which was not wasted time as a result of as we’re constructing the brand new infrastructure, it’s simple for us to refer again to the previous documentation.

So, I actually emphasize documentation. If you begin is the time and the place to actually centralize your information, so every time somebody leaves, the information stays, and it’s a lot simpler for brand spanking new folks to onboard. No one has to play guessing video games. It’s centralized, and there’s no query.

Typically totally different groups have totally different definitions for related phrases. And even in these circumstances, we’ll use the SQL to doc so we are able to say “That is the method that derives this definition of Revenue.”

You wish to go away little or no room for misinterpretation. That’s actually what I’d like to emphasise.

Anything you’d prefer to share?

I nonetheless have the spreadsheet from two years in the past after I appeared for documentation instruments. I did numerous market analysis, taking a look at 20 totally different distributors and each device I might discover. What was essential to me was discovering a platform that would hook up with all of the instruments I used to be already utilizing, which had been dbt, Snowflake, and Looker, and that had a robust help group. I knew that once we first onboarded, we’d have questions, and we might be establishing numerous permissions and knowledge connections, and {that a} sturdy help group can be very useful.

I remembered once we first labored with the group, everyone that I interacted with from Atlan was tremendous useful and really beneficiant with their time. Now, we’re just about working by ourselves, and I’m all the time proud that I discovered and selected Atlan.

Picture by Priscilla Du Preez 🇨🇦 on Unsplash

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