#real-time data pipeline

[moolamore]

Moolamore data integration and analysis with leading accounting software

In today's fast-paced business world, having an accurate, up-to-date picture of your ins and outs can mean the difference between flourishing and barely scraping by.

Good thing there's Moolamore—a powerful solution designed to help you seamlessly integrate and analyze real-time data, providing your SMEs with the information they need to stay ahead and resilient in any economic climate. Make sure you keep reading this blog!

[rajaniesh]

Unveiling the Power of Delta Lake in Microsoft Fabric

Discover how Microsoft Fabric and Delta Lake can revolutionize your data management and analytics. Learn to optimize data ingestion with Spark and unlock the full potential of your data for smarter decision-making.

In today’s digital era, data is the new gold. Companies are constantly searching for ways to efficiently manage and analyze vast amounts of information to drive decision-making and innovation. However, with the growing volume and variety of data, traditional data processing methods often fall short. This is where Microsoft Fabric, Apache Spark and Delta Lake come into play. These powerful…

[river-taxbird]

AI hasn't improved in 18 months. It's likely that this is it. There is currently no evidence the capabilities of ChatGPT will ever improve. It's time for AI companies to put up or shut up.

I'm just re-iterating this excellent post from Ed Zitron, but it's not left my head since I read it and I want to share it. I'm also taking some talking points from Ed's other posts. So basically:

We keep hearing AI is going to get better and better, but these promises seem to be coming from a mix of companies engaging in wild speculation and lying.

Chatgpt, the industry leading large language model, has not materially improved in 18 months. For something that claims to be getting exponentially better, it sure is the same shit.

Hallucinations appear to be an inherent aspect of the technology. Since it's based on statistics and ai doesn't know anything, it can never know what is true. How could I possibly trust it to get any real work done if I can't rely on it's output? If I have to fact check everything it says I might as well do the work myself.

For "real" ai that does know what is true to exist, it would require us to discover new concepts in psychology, math, and computing, which open ai is not working on, and seemingly no other ai companies are either.

Open ai has already seemingly slurped up all the data from the open web already. Chatgpt 5 would take 5x more training data than chatgpt 4 to train. Where is this data coming from, exactly?

Since improvement appears to have ground to a halt, what if this is it? What if Chatgpt 4 is as good as LLMs can ever be? What use is it?

As Jim Covello, a leading semiconductor analyst at Goldman Sachs said (on page 10, and that's big finance so you know they only care about money): if tech companies are spending a trillion dollars to build up the infrastructure to support ai, what trillion dollar problem is it meant to solve? AI companies have a unique talent for burning venture capital and it's unclear if Open AI will be able to survive more than a few years unless everyone suddenly adopts it all at once. (Hey, didn't crypto and the metaverse also require spontaneous mass adoption to make sense?)

There is no problem that current ai is a solution to. Consumer tech is basically solved, normal people don't need more tech than a laptop and a smartphone. Big tech have run out of innovations, and they are desperately looking for the next thing to sell. It happened with the metaverse and it's happening again.

In summary:

Ai hasn't materially improved since the launch of Chatgpt4, which wasn't that big of an upgrade to 3.

There is currently no technological roadmap for ai to become better than it is. (As Jim Covello said on the Goldman Sachs report, the evolution of smartphones was openly planned years ahead of time.) The current problems are inherent to the current technology and nobody has indicated there is any way to solve them in the pipeline. We have likely reached the limits of what LLMs can do, and they still can't do much.

Don't believe AI companies when they say things are going to improve from where they are now before they provide evidence. It's time for the AI shills to put up, or shut up.

[lazeecomet]

The Story of KLogs: What happens when an Mechanical Engineer codes

Since i no longer work at Wearhouse Automation Startup (WAS for short) and havnt for many years i feel as though i should recount the tale of the most bonkers program i ever wrote, but we need to establish some background

WAS has its HQ very far away from the big customer site and i worked as a Field Service Engineer (FSE) on site. so i learned early on that if a problem needed to be solved fast, WE had to do it. we never got many updates on what was coming down the pipeline for us or what issues were being worked on. this made us very independent

As such, we got good at reading the robot logs ourselves. it took too much time to send the logs off to HQ for analysis and get back what the problem was. we can read. now GETTING the logs is another thing.

the early robots we cut our teeth on used 2.4 gHz wifi to communicate with FSE's so dumping the logs was as simple as pushing a button in a little application and it would spit out a txt file

later on our robots were upgraded to use a 2.4 mHz xbee radio to communicate with us. which was FUCKING SLOW. and log dumping became a much more tedious process. you had to connect, go to logging mode, and then the robot would vomit all the logs in the past 2 min OR the entirety of its memory bank (only 2 options) into a terminal window. you would then save the terminal window and open it in a text editor to read them. it could take up to 5 min to dump the entire log file and if you didnt dump fast enough, the ACK messages from the control server would fill up the logs and erase the error as the memory overwrote itself.

this missing logs problem was a Big Deal for software who now weren't getting every log from every error so a NEW method of saving logs was devised: the robot would just vomit the log data in real time over a DIFFERENT radio and we would save it to a KQL server. Thanks Daddy Microsoft.

now whats KQL you may be asking. why, its Microsofts very own SQL clone! its Kusto Query Language. never mind that the system uses a SQL database for daily operations. lets use this proprietary Microsoft thing because they are paying us

so yay, problem solved. we now never miss the logs. so how do we read them if they are split up line by line in a database? why with a query of course!

select * from tbLogs where RobotUID = [64CharLongString] and timestamp > [UnixTimeCode]

if this makes no sense to you, CONGRATULATIONS! you found the problem with this setup. Most FSE's were BAD at SQL which meant they didnt read logs anymore. If you do understand what the query is, CONGRATULATIONS! you see why this is Very Stupid.

You could not search by robot name. each robot had some arbitrarily assigned 64 character long string as an identifier and the timestamps were not set to local time. so you had run a lookup query to find the right name and do some time zone math to figure out what part of the logs to read. oh yeah and you had to download KQL to view them. so now we had both SQL and KQL on our computers

NOBODY in the field like this.

But Daddy Microsoft comes to the rescue

see we didnt JUST get KQL with part of that deal. we got the entire Microsoft cloud suite. and some people (like me) had been automating emails and stuff with Power Automate

This is Microsoft Power Automate. its Microsoft's version of Scratch but it has hooks into everything Microsoft. SharePoint, Teams, Outlook, Excel, it can integrate with all of it. i had been using it to send an email once a day with a list of all the robots in maintenance.

this gave me an idea

and i checked

and Power Automate had hooks for KQL

KLogs is actually short for Kusto Logs

I did not know how to program in Power Automate but damn it anything is better then writing KQL queries. so i got to work. and about 2 months later i had a BEHEMOTH of a Power Automate program. it lagged the webpage and many times when i tried to edit something my changes wouldn't take and i would have to click in very specific ways to ensure none of my variables were getting nuked. i dont think this was the intended purpose of Power Automate but this is what it did

the KLogger would watch a list of Teams chats and when someone typed "klogs" or pasted a copy of an ERROR mesage, it would spring into action.

it extracted the robot name from the message and timestamp from teams

it would lookup the name in the database to find the 64 long string UID and the location that robot was assigned too

it would reply to the message in teams saying it found a robot name and was getting logs

it would run a KQL query for the database and get the control system logs then export then into a CSV

it would save the CSV with the a .xls extension into a folder in ShairPoint (it would make a new folder for each day and location if it didnt have one already)

it would send ANOTHER message in teams with a LINK to the file in SharePoint

it would then enter a loop and scour the robot logs looking for the keyword ESTOP to find the error. (it did this because Kusto was SLOWER then the xbee radio and had up to a 10 min delay on syncing)

if it found the error, it would adjust its start and end timestamps to capture it and export the robot logs book-ended from the event by ~ 1 min. if it didnt, it would use the timestamp from when it was triggered +/- 5 min

it saved THOSE logs to SharePoint the same way as before

it would send ANOTHER message in teams with a link to the files

it would then check if the error was 1 of 3 very specific type of error with the camera. if it was it extracted the base64 jpg image saved in KQL as a byte array, do the math to convert it, and save that as a jpg in SharePoint (and link it of course)

and then it would terminate. and if it encountered an error anywhere in all of this, i had logic where it would spit back an error message in Teams as plaintext explaining what step failed and the program would close gracefully

I deployed it without asking anyone at one of the sites that was struggling. i just pointed it at their chat and turned it on. it had a bit of a rocky start (spammed chat) but man did the FSE's LOVE IT.

about 6 months later software deployed their answer to reading the logs: a webpage that acted as a nice GUI to the KQL database. much better then an CSV file

it still needed you to scroll though a big drop-down of robot names and enter a timestamp, but i noticed something. all that did was just change part of the URL and refresh the webpage

SO I MADE KLOGS 2 AND HAD IT GENERATE THE URL FOR YOU AND REPLY TO YOUR MESSAGE WITH IT. (it also still did the control server and jpg stuff). Theres a non-zero chance that klogs was still in use long after i left that job

now i dont recommend anyone use power automate like this. its clunky and weird. i had to make a variable called "Carrage Return" which was a blank text box that i pressed enter one time in because it was incapable of understanding /n or generating a new line in any capacity OTHER then this (thanks support forum).

im also sure this probably is giving the actual programmer people anxiety. imagine working at a company and then some rando you've never seen but only heard about as "the FSE whos really good at root causing stuff", in a department that does not do any coding, managed to, in their spare time, build and release and entire workflow piggybacking on your work without any oversight, code review, or permission.....and everyone liked it

[covid-safer-hotties]

Also preserved in our archive

A new study by researchers at Zhejiang University has highlighted the disproportionate health challenges faced by sexual and gender-diverse (SGD) individuals during the COVID-19 pandemic. By analyzing over 471 million tweets using advanced natural language processing (NLP) techniques, the study reveals that SGD individuals were more likely to discuss concerns related to social connections, mask-wearing, and experienced higher rates of COVID-19 symptoms and mental health issues than non-SGD individuals. The study has been published in the journal Health Data Science.

The COVID-19 pandemic has exposed and intensified health disparities, particularly for vulnerable populations like the sexual and gender-diverse (SGD) community. Unlike traditional health data sources, social media provides a more dynamic and real-time reflection of public concerns and experiences. Zhiyun Zhang, a Ph.D. student at Zhejiang University, and Jie Yang, Assistant Professor at the same institution, led a study that analyzed large-scale Twitter data to understand the unique challenges faced by SGD individuals during the pandemic.

To address this, the research team used NLP methods such as Latent Dirichlet Allocation (LDA) models for topic modeling and advanced sentiment analysis to evaluate the discussions and concerns of SGD Twitter users compared to non-SGD users. This approach allowed the researchers to explore three primary questions: the predominant topics discussed by SGD users, their concerns about COVID-19 precautions, and the severity of their symptoms and mental health challenges.

The findings reveal significant differences between the two groups. SGD users were more frequently involved in discussions about "friends and family" (20.5% vs. 13.1%) and "wearing masks" (10.1% vs. 8.3%). They also expressed higher levels of positive sentiment toward vaccines such as Pfizer, Moderna, AstraZeneca, and Johnson & Johnson. The study found that SGD individuals reported significantly higher frequencies of both physical and mental health symptoms compared to non-SGD users, underscoring their heightened vulnerability during the pandemic.

"Our large-scale social media analysis highlights the concerns and health challenges of SGD users. The topic analysis showed that SGD users were more frequently involved in discussions about 'friends and family' and 'wearing masks' than non-SGD users. SGD users also expressed a higher level of positive sentiment in tweets about vaccines," said Zhiyun Zhang, the lead researcher. "These insights emphasize the importance of targeted public health interventions for SGD communities."

The study demonstrates the potential of using social media data to monitor and understand public health concerns, especially for marginalized communities like SGD individuals. The results suggest the need for more tailored public health strategies to address the unique challenges faced by SGD communities during pandemics.

Moving forward, the research team aims to develop an automated pipeline to continuously monitor the health of targeted populations, offering data-driven insights to support more comprehensive public health services.

More information: Zhiyun Zhang et al, Sexual and Gender-Diverse Individuals Face More Health Challenges during COVID-19: A Large-Scale Social Media Analysis with Natural Language Processing, Health Data Science (2024). DOI: 10.34133/hds.0127 spj.science.org/doi/10.34133/hds.0127

[aro-culture-is]

Seen this mentioned by aces but at least for me this applies to both being ace and being aro so:

Aro culture is thinking I'm bi for a really long time because of a mix of allonormativity and amatonormativity hence convincing myself I have several crushes because that's what everyone else was doing in middle and high school so you should be having crushes too. And since most if not all of these crushes are aesthetic attraction, platonic attraction, or really just stretching to say I have a crush so I don't seem weird, well, it's not that hard to imagine how I convinced myself I was bi. Because since they weren't real romantic crushes I couldn't and I suppose that if I sought a label I'd be pan or biplatonic then yeah I was confusing platonic for romantic so pretty easy to think I was bi.

Even after I learned I'm ace and demiromantic it took me years to realize I'm not bi. Like I only accepted it a couple months ago. It was a whole mess because I'd called myself bi for 6 years and also I had this weird anxiety because biphobes always say bis will "choose a side" and that's kinda what I felt like was happening when I realized I'm heteroromantic but now that I'm through that I actually feel really comfortable now.

Also considering that maybe I'm not demiromantic and am rather greyromantic or something like that. Turns out I've only had one IRL crush ever. Thought I was demiromantic before because I'd have friends then convince myself after a while it was a crush. But now that I know I've had 1 IRL crush (also only 2 fictional crushes) I don't know if I necessarily have enough data if that makes sense. Because I'm a very friendly person so the fact I haven't had any other crushes suggests that maybe it's not that I need a close bond but that I just don't experience this attraction a lot. But who knows 🤷. And im not too worried. I remember debating between demi & grey years ago too. Either way I'm still aro & I'm okay with either or. Guess we'll see what happens in the future.

But yeah sorry for the rant. But I see aces talking about the bi to ace pipeline but I at least def experienced a bi to aro pipeline alongside that.

yeah the multi-spec attraction to a-spec "wait same amounts of attraction, wait, zero = zero" pipeline is real

[grison-in-space]

I did a bit of de novo genome assembly way, way back in the day which I have never been able to use professionally because my PI refused to spend $2000 more on getting new read depth. He had ordered the reads before actually learning anything about the pipeline and only about half of the libraries he had ordered were usable in any given pipeline, see. (Some had been for older assembly methods and others had been for newer ones, basically.)

Rather than find the money to fucking get me the reads to do it right, he heard about an open source project called RACA that was some dude's dissertation arguing that you COULD use some of the worthless libraries to fill in the gaps of the assembly and get a functional genome out of it. I spent two years trying to move massive quantities of data through that fuckhead's pipeline on the campus supercomputer to get the assembled genome out, and then I got to the end and found there was no output as fastq files or ant other format recognizable to me.

(Give me a break, I was 23 and had also been frantically learning acoustic analysis, basic electrical engineering, and technical equipment maintenance in the two years since I had started learning to code. Plus I was figuring out what I wanted my dissertation to be. I'd never grappled with anything more complicated than our home-written library of matlab acoustic analysis before, and it simply hadn't occurred to me that anyone would publish a non-functional pipeline to achieve a goal quickly anyone verifying that anyone else had done anything yet.)

Anyway, eventually he collaborated with someone else who ponied up $2000 and a postdoc to get new reads. My name was not on the paper, so that's two years of my life developing a particular and fairly unique skill set that I will almost certainly never use.

In retrospect it's less surprising than you might think that the PhD took eight years and absolutely shattered my confidence.

And the best part is that it was just about impossible to predict at the time that shit would go quite this bad, except that some people handle power well when they're stressed and some people maintain a strong layer of cognitive dissonance over their knowledge of power such that it's never real enough to be responsible about but always real enough to win a dispute.

Anyway I think every student should have two advisors so that everyone in the department should have to immediately know about it when a PI is floundering and have a strong direct incentive to do something about it. A LOT of my problems could have been fixed with one look with a gimlet eye from a senior, more experienced researcher being not impressed at a student under their supervision running on an endless treadmill to nothing. Frankly a lot of my problems could have been solved if my mentor had formal training or literally any supervision that could deliver metrics faster than "how close am I to my previous mentees?"

I know a lot of dual advised students wind up in a tug of war between two advisors, but like: that's the point. If one of them turns out to be insane and malicious then a) the students all have clear lines to bail, b) the other ones all realize quickly that bailing out the chaos and career damage of someone who is fucking it up is way more work than resolving the problem, and c) the one with more tethers to reality has a way bigger likelihood of formally retaining the student when and if a third party has to examine the contract.

Just. It was such a fucking waste. And not because anyone necessarily wanted it to be wasteful, either, or any malice, but because I was... mm, I think the fifth PhD student in that lab and that's actually not that many to be learning on. Systems that set you up to play with decades of people's lives should have more fail-safes and places for people to learn before they get to be the sole director of someone else's career for five fucking years, not less. And yet!

[janeeseelizabeth]

Blog post #8 - Week 11

How has social media contributed to protest? 

Social media has allowed activists to be able to spread information more quickly and with more people at a time. They are able to share information and locations of protests and also create plans of action for the future. Social media has also allowed for people to bring attention to certain issues that mainstream news companies may try to ignore. Many news channels are very biased and tend not to tell the entire story when it comes to people of color being innocent, they shape the story in order to make white people seem to be the victim even when that is not the truth. Such as in the death of George Floyd in 2020, many news channels did not tell the true extent and severity of the situation and it was not until the public began to post videos that everyone was able to see the truth of the situation and was able to see what actually happened. 

What were some long term effects from the #NoDAPL movement? 

The NoDAPL movement shaped a new unity within indigenous groups and brought people together in order to fight against the long history of stolen land and the constant disregard of indigenous tribes rights. The movement also showed how social media can play a significant role in organizing protests and getting global attention. By using the hashtag #NoDAPL activists were able to attach information, pictures, and videos to the hashtag. The movement led many people to switch to renewable energy considering how pipelines, like DAPL,  could be harmful to the water supply and also could cause more environmental problems in the future. 

How does online activism differ from in-person activism? 

Online activism allows for information to be posted and widely spread almost instantly, while in-person activism tends to require more planning and is usually only spread more locally. This allows online activism to gain much attention more quickly, although there is also a downside to this attention. Considering most online platforms are open to the public, false information can also get in the way which can cause the real meaning of the protest to be lost or altered. In-person activism tends to get a lot of attention as well, especially when there is a large group of people involved. In-person movements allow for people to get more involved and also cause people to get more emotional and passionate about the topics by seeing how many supporters there are. 

What barriers still exist that prevent people from engaging in online activism? 

One significant barrier that causes people not to engage with online activism has to do with government restrictions. For example, Tiktok is a large social media platform that people would use to spread their political views and morals, although after the ban. Certain words and phrases are restricted. There is now an extreme amount of censorship on the app. Another barrier has to do with people's inability to access online data, such as not having internet and no technology. A large amount of online activism comes from the newer generations because the many members of the older generations are not comfortable with technology and are still easily confused on how it works. 

[philosophicalparadox]

Itachi is a Weird Kid(tm)

These need to exit my tired brain so I am releasing the wild, naked baby plot bunnies upon ye:

Some of these points are paragraphs because that’s just my style, but this one’s not super long (for me 😅) so I’ll leave it no-cut.

He is more fond of plants and birds than people. That he’s an introvert surprises nobody but it’s worth mentioning.

He doesn’t know how to have a “normal”conversation with normal people; he’s been so conditioned by his Shinobi centric/Child soldier life that he literally doesn’t know how to talk to civilians at their level, so to speak. An ordinary conversation about ordinary, every day things is either boring and meaningless, or completely lost on him in most cases.

He is somewhat accidentally piscivarian/ predominantly vegetarian. This is more an observation of canon than a HC. His character/data sheet does mention that he’s fond of vegetables, particularly crunchy ones like cabbage and peppers, and utterly hates red meat, but we do see him eating fish and I don’t think he’d be opposed to chicken or eggs either, whatever that would make him. (I could go on and on about how this, among other things, queer codes tf out of him, but I will refrain)

To that effect, he has absolutely stellar foraging skills, particularly with regards to edible and medicinal plants. He could survive on his own for quite some time if he needed to, and I suspect he did for a while before joining the Akatsuki.

Many of Itachi’s “weaknesses” in fact make him more dangerous, not less — that is to say, they make him unpredictable. To whit, in my opinion, his weaknesses are:

Small Children (protective brother instinct, or maybe a paternal one) — these will give him pause in most situations, but lord help whoever thinks it’s a good idea to try using that against him. He will do anything he can to avoid hurting or killing a child, but that does not work out to mean they make good leverage — he’s much too clever and knows how to put on a show to fake his opponents out a little too well. He’s not necessarily afraid to frighten a child either if it means they live to see another day, though if it can be avoided all the better. But what makes it dangerous is that his whole disposition changes when there’s children involved. If you don’t know him well one might not notice, but it’s a palpable change, a charged kind of energy he doesn’t typically have otherwise — an opponents threat level to him personally ceases to matter quite as much as their relative threat to the child. One of the few situations in which he might go out of his way to start a fight with a civilian or other typical non-combatant.

His Stress Response: Itachi is not someone who ever had the option to flee in most cases as a child. So he’s developed a Freeze-Fight pipeline. When confronted with something that scares him, he will, situationally, freeze, first — not bad for a Shinobi, really, as freezing makes one less noticeable and might give one time to assess. Given the option, he would like to do that, but in the event he can not, whether because the opponent is moving or the situation is overwhelming or too stressful, he will immediately switch to Fight. Good when you’re being attacked. Not so good when the PTSD flashbacks get you or a nightmare wakes you up ready to throw hands. The real danger of the Freeze-Fight pipeline is that it presents the illusion that his Threshold of Tolerance is higher than it actually is; He can therefore seem very, explosively unpredictable when he’s under extreme stress. If he’s actually terrified, you will not typically know it until he is so scared he lashes out. And the lash-out/freak out might seem disproportionate as a result of whatever is going on in his head.

6. He is surprisingly invisible. Despite being an Uchiha, despite having a widely known name, despite having fairly wide ranging reputation, he can be very effective as an undercover agent because he just… blends in. He’s not loud. He’s not aggressive. He’s not very noticeable at all, especially without his cloak. Adding to this, I personally believe in making him at least partially abide by his name, and thus posit that he has an unusual gift for the use of Henge, despite canon not mentioning it. It would be all too suitable for someone named after a shape-shifter to be skilled in the art of transforming himself into not only other people, but animals as well (which is supposedly the hardest kind of Henge to accomplish consistently).

7. He is…basically a cat, lol. Is borderline obsessively clean, keeps things tidy, doesn’t like being touched without permission, is moody as all hell, and very…demanding, in his own quiet way. Communicates with his body language more than one would assume, in that if he’s there, he’s there for a reason. Example being if he wanted to comfort someone— rather than making a fuss over them he’d probably just offer his company by moving closer, paralleling, etc. giving them space to bring it up or set it down. He never had much personal freedom as a kid, so he tries to be generous with others and not pry into their business unless he really needs to. He also communicates with his body in other ways related to staging — where he is in proximity to others — by drifting closer to those he trusts and keeping away from those he does not, I.e if he were traveling with a contractor or other akatsuki members in addition to his own partner, he may drift toward Kisame and stay next to him as opposed to trying to be in any particular position within the group.

8. This one’s more focused on other characters perception of him, but he is Weird in that for all his nasty reputation and abilities, he’s very demure and non-confrontational. He would rather not use his powers if he doesn’t strictly need to, and with regards to wayward animals he’s quite gentle, when he can afford to be.

9. On that note: his crows are his family damn near. He inherited these crows from Shisui, so they mean a good deal to him on the whole, although he will sacrifice them if he has to. But this is something he does with deep regret and respect, and the crows are just smart enough to sort of understand. They love him, he is their flock friend, and they will defend him/look after him of their own volition to the best of their ability. He could use his sharingan to trick them, and will if he must, but crows are much too clever to make a habit of it; it’s better for everyone if he goes out of his way to be their friend. Crows are a very difficult familiar/summons to have, because they’re insanely smart for an animal and they communicate with each other much like people do. You’ve no choice but to carefully cultivate your relationship with them, because if they decide they don’t like you, they will never let you forget it. They’re perfect for someone who is meticulous and kind like Itachi. You can not be mean or careless to a crow and expect them to forgive you.

I’m sure I’ll think of more, but until then Enjoy.

And please, Anti people of any flavor, don’t even bother. I won’t play those games.

[sujitchaulagainblogs]

How to Choose the Best CRM Software for Your Business

Choosing the right CRM software for your business is a big decision — and the right one can make a world of difference. Whether you’re running a small startup or managing a growing company, having an effective CRM (Customer Relationship Management) system helps you keep track of customers, boost sales, and improve overall productivity. Let’s walk through how you can choose the best CRM for your business without getting overwhelmed.

Why Your Business Needs a CRM

A CRM isn’t just a tool — it’s your business’s central hub for managing relationships. If you’re still relying on spreadsheets or scattered notes, you’re probably losing time (and leads). A good CRM helps you:

Keep customer data organized in one place

Track leads, sales, and follow-ups

Automate routine tasks

Get insights into sales performance

Improve customer service

The goal is simple: work smarter, not harder. And with an affordable CRM that fits your needs, you’ll see faster growth and smoother processes.

Define Your Business Goals

Before diving into features, figure out what you actually need. Ask yourself:

Are you trying to increase sales or improve customer service?

Do you need better lead tracking or marketing automation?

How big is your team, and how tech-savvy are they?

What’s your budget?

Knowing your goals upfront keeps you from wasting time on CRMs that might be packed with unnecessary features — or worse, missing key ones.

Must-Have Features to Look For

When comparing CRM options, focus on features that truly matter for your business. Here are some essentials:

Contact Management – Store customer details, interactions, and notes all in one place.

Lead Tracking – Follow leads through the sales funnel and never miss a follow-up.

Sales Pipeline Management – Visualize where your deals stand and what needs attention.

Automation – Save time by automating emails, reminders, and data entry.

Customization – Adjust fields, workflows, and dashboards to match your process.

Third-Party Integrations – Ensure your CRM connects with other software you rely on, like email marketing tools or accounting systems.

Reports & Analytics – Gain insights into sales, performance, and customer behavior.

User-Friendly Interface – If your team finds it clunky or confusing, they won’t use it.

Budget Matters — But Value Matters More

A CRM doesn’t have to cost a fortune. Plenty of affordable CRM options offer robust features without the hefty price tag. The key is balancing cost with value. Don’t just chase the cheapest option — pick a CRM that supports your business growth.

Take LeadHeed, for example. It’s an affordable CRM designed to give businesses the tools they need — like lead management, sales tracking, and automation — without stretching your budget. It’s a smart pick if you want to grow efficiently without overpaying for features you won’t use.

Test Before You Commit

Most CRMs offer a free trial — and you should absolutely use it. A CRM might look great on paper, but it’s a different story when you’re actually using it. During your trial period, focus on:

How easy it is to set up and start using

Whether it integrates with your existing tools

How fast you can access and update customer information

If your team finds it helpful (or frustrating)

A trial gives you a real feel for whether the CRM is a good fit — before you commit to a paid plan.

Think About Long-Term Growth

Your business might be small now, but what about next year? Choose a CRM that grows with you. Look for flexible pricing plans, scalable features, and the ability to add more users or advanced functions down the line.

It’s better to pick a CRM that can expand with your business than to go through the hassle of switching systems later.

Check Customer Support

Even the best software can hit a snag — and when that happens, you’ll want reliable support. Look for a CRM that offers responsive customer service, whether that’s live chat, email, or phone. A system is only as good as the help you get when you need it.

Read Reviews and Compare

Don’t just rely on the CRM’s website. Read reviews from other businesses — especially ones similar to yours. Sites like G2, Capterra, and Trustpilot offer honest insights into what works (and what doesn’t). Comparing multiple CRMs ensures you make a well-rounded decision.

The Bottom Line

Choosing the best CRM software for your business doesn’t have to be complicated. By understanding your goals, focusing on essential features, and keeping scalability and budget in mind, you’ll find a CRM that fits like a glove.

If you’re looking for an affordable CRM Software that checks all the right boxes — without cutting corners — LeadHeed is worth exploring. It’s built to help businesses like yours manage leads, automate tasks, and gain valuable insights while staying within budget.

The right CRM can transform how you run your business. Take the time to find the one that supports your growth, keeps your team organized, and helps you deliver an even better experience to your customers.

[drfuckerm-d]

i love mashing my interests together in my fists and im learning a new process today so here's what types of welding i think the bridge team (+some extras) from tng would do/enjoy

🩶picard does not care for welding, NEEEEXT (but if he did he would enjoy acetylene welding)(because he was born old) i think he should give it a shot anyways bc chipping the slag off a stick weld feels very much like archaeology.

❤️rikers dad made him weld i bet. mig. i don't think he liked it either but he was probably fine at it. like if he had to mig something together it wouldnt fall apart. immediately.

🧡geordi welds for surely, and he's good as fuck at it. mig, tig, arc, and beyond. HOWEVER, he seems like the type to prefer more advanced methods like stir welding and EBW. anything requiring some level of interfacing through a machine rather than stinger to steel. when i write fic i headcanon that since its the future there's cool new ✨futuristic✨ post-advent-of-space-travel welding methods that involve cold welding, and i think he'd like that, too. he is an engineer, after all, and there's overlap between these fields.

💛data would be fantastic at any kind of welding as long as he's studied up on it first, but i don't think he'd find any value in it beyond its function. you wouldn't find him on weld forums ogling any crisp weaves. the only reason he'd find a personal interest in it would be because his friends like it.

🤍tasha welds most definitely, and i think she's pretty good at it. her knowledge starts at mig, thru flux core and ends at arc -- i can't imagine the colony she came from had much use for tig or any of the more advanced methods. to her, i bet welding is just a useful repair skill everyone should know at least a little about, like sewing.

🤎worf has never tried welding but i think he would love arc welding. pipe. in the late summer. for nine hours. it's a real test of strength you can pride yourself on if you don't sweat yourself to death in your leathers. need to know what a klingon welding hood would look like. GET THIS GUY ON A PIPELINE NOW!!!!!

🩵iiiiiii think deanna would be really good at tig if she applied herself but i don't think she'd like welding at all. i don't think she'd like the smells, sounds, wearing the ppe, any of it. the fumes probably give her a headache. and that is ok 🙏

💙beverly can mig weld, i bet. it's neither here nor there for her, just something she can do in a pinch. it comes in handy when you're helping out around a colony, but as a doctor, she finds she's plenty of help without it anyways.

🩷i feel like wesley can hella tig weld. i think he's in the boat with geordi where they don't really care for the 'dirtier' types but overall he's more into the technical bits of engineering rather than the fabrication end of it. honestly, i think more than anything he'd be really into soldering. but anyways, tig welding is hard and impressive and he should be VERY PROUD of himself 🫶✨

💚q can weld stuff together with his mind. whatever. cheater. but if he was stripped of his q powers and made to weld he would hhhhhHHAAAATE it i promise. screaming and jumping at the sparks, touching the hot metal on accident and whinging about it, upset cuz he's too hot to be wearing leathers, etc. he would wilt. womp womp.

💜i bet guinan is secretly a master welder and she anonymously runs a welding forum and posts the most picturesque woven beads you've ever seen in your life. she's secretly the talk of all the techs at every docking station -- like banksy for people who chew dip. its art when she does it.

🖤lore hates welding he can and will bitch about it the entire time if he's made to. the only kind he can tolerate is mig but even then, he finds it all tedious. i think if you ever sat him down to arc weld for an hour and came back to check on him, all you'd find left would be a plate covered in errant strikes, 50 partially coated, bent-up rods all over the floor, and a broken stinger. he also seems like the type to not wear his leathers and then complain when he gets holes burnt into his arms.

[mayerfeldconsulting]

Are manual processes slowing down your business?

Studies show that employees spend nearly 60% of their time on administrative tasks…tasks that could be automated. The biggest culprit? Outdated workflows that rely on manual approvals, excessive paperwork, and disconnected systems.

Automation plays a crucial role in eliminating workflow bottlenecks by:

✅ Reducing human error – Automated workflows prevent mistakes caused by manual data entry and miscommunication.

✅ Accelerating approvals – Workflow automation ensures that tasks move through the pipeline efficiently, without unnecessary delays.

✅ Optimizing resource allocation – Smart scheduling tools and real-time analytics help balance workloads, ensuring no one team is overwhelmed.

Many businesses hesitate to automate because of implementation concerns, but the reality is that even small automation steps can lead to massive efficiency gains. At Mayerfeld Consulting, we help businesses integrate automation tools that eliminate bottlenecks, cut operational costs, and boost overall productivity.

Is your organization leveraging automation effectively? Let’s talk about how technology can transform your workflow and drive efficiency.

#MayerfeldConsulting #Automation #DigitalTransformation #EfficiencyMatters #WorkflowOptimization

[govindhtech]

Google Cloud’s BigQuery Autonomous Data To AI Platform

BigQuery automates data analysis, transformation, and insight generation using AI. AI and natural language interaction simplify difficult operations.

The fast-paced world needs data access and a real-time data activation flywheel. Artificial intelligence that integrates directly into the data environment and works with intelligent agents is emerging. These catalysts open doors and enable self-directed, rapid action, which is vital for success. This flywheel uses Google's Data & AI Cloud to activate data in real time. BigQuery has five times more organisations than the two leading cloud providers that just offer data science and data warehousing solutions due to this emphasis.

Examples of top companies:

With BigQuery, Radisson Hotel Group enhanced campaign productivity by 50% and revenue by over 20% by fine-tuning the Gemini model.

By connecting over 170 data sources with BigQuery, Gordon Food Service established a scalable, modern, AI-ready data architecture. This improved real-time response to critical business demands, enabled complete analytics, boosted client usage of their ordering systems, and offered staff rapid insights while cutting costs and boosting market share.

J.B. Hunt is revolutionising logistics for shippers and carriers by integrating Databricks into BigQuery.

General Mills saves over $100 million using BigQuery and Vertex AI to give workers secure access to LLMs for structured and unstructured data searches.

Google Cloud is unveiling many new features with its autonomous data to AI platform powered by BigQuery and Looker, a unified, trustworthy, and conversational BI platform:

New assistive and agentic experiences based on your trusted data and available through BigQuery and Looker will make data scientists, data engineers, analysts, and business users' jobs simpler and faster.

Advanced analytics and data science acceleration: Along with seamless integration with real-time and open-source technologies, BigQuery AI-assisted notebooks improve data science workflows and BigQuery AI Query Engine provides fresh insights.

Autonomous data foundation: BigQuery can collect, manage, and orchestrate any data with its new autonomous features, which include native support for unstructured data processing and open data formats like Iceberg.

Look at each change in detail.

User-specific agents

It believes everyone should have AI. BigQuery and Looker made AI-powered helpful experiences generally available, but Google Cloud now offers specialised agents for all data chores, such as:

Data engineering agents integrated with BigQuery pipelines help create data pipelines, convert and enhance data, discover anomalies, and automate metadata development. These agents provide trustworthy data and replace time-consuming and repetitive tasks, enhancing data team productivity. Data engineers traditionally spend hours cleaning, processing, and confirming data.

The data science agent in Google's Colab notebook enables model development at every step. Scalable training, intelligent model selection, automated feature engineering, and faster iteration are possible. This agent lets data science teams focus on complex methods rather than data and infrastructure.

Looker conversational analytics lets everyone utilise natural language with data. Expanded capabilities provided with DeepMind let all users understand the agent's actions and easily resolve misconceptions by undertaking advanced analysis and explaining its logic. Looker's semantic layer boosts accuracy by two-thirds. The agent understands business language like “revenue” and “segments” and can compute metrics in real time, ensuring trustworthy, accurate, and relevant results. An API for conversational analytics is also being introduced to help developers integrate it into processes and apps.

In the BigQuery autonomous data to AI platform, Google Cloud introduced the BigQuery knowledge engine to power assistive and agentic experiences. It models data associations, suggests business vocabulary words, and creates metadata instantaneously using Gemini's table descriptions, query histories, and schema connections. This knowledge engine grounds AI and agents in business context, enabling semantic search across BigQuery and AI-powered data insights.

All customers may access Gemini-powered agentic and assistive experiences in BigQuery and Looker without add-ons in the existing price model tiers!

Accelerating data science and advanced analytics

BigQuery autonomous data to AI platform is revolutionising data science and analytics by enabling new AI-driven data science experiences and engines to manage complex data and provide real-time analytics.

First, AI improves BigQuery notebooks. It adds intelligent SQL cells to your notebook that can merge data sources, comprehend data context, and make code-writing suggestions. It also uses native exploratory analysis and visualisation capabilities for data exploration and peer collaboration. Data scientists can also schedule analyses and update insights. Google Cloud also lets you construct laptop-driven, dynamic, user-friendly, interactive data apps to share insights across the organisation.

This enhanced notebook experience is complemented by the BigQuery AI query engine for AI-driven analytics. This engine lets data scientists easily manage organised and unstructured data and add real-world context—not simply retrieve it. BigQuery AI co-processes SQL and Gemini, adding runtime verbal comprehension, reasoning skills, and real-world knowledge. Their new engine processes unstructured photographs and matches them to your product catalogue. This engine supports several use cases, including model enhancement, sophisticated segmentation, and new insights.

Additionally, it provides users with the most cloud-optimized open-source environment. Google Cloud for Apache Kafka enables real-time data pipelines for event sourcing, model scoring, communications, and analytics in BigQuery for serverless Apache Spark execution. Customers have almost doubled their serverless Spark use in the last year, and Google Cloud has upgraded this engine to handle data 2.7 times faster.

BigQuery lets data scientists utilise SQL, Spark, or foundation models on Google's serverless and scalable architecture to innovate faster without the challenges of traditional infrastructure.

An independent data foundation throughout data lifetime

An independent data foundation created for modern data complexity supports its advanced analytics engines and specialised agents. BigQuery is transforming the environment by making unstructured data first-class citizens. New platform features, such as orchestration for a variety of data workloads, autonomous and invisible governance, and open formats for flexibility, ensure that your data is always ready for data science or artificial intelligence issues. It does this while giving the best cost and decreasing operational overhead.

For many companies, unstructured data is their biggest untapped potential. Even while structured data provides analytical avenues, unique ideas in text, audio, video, and photographs are often underutilised and discovered in siloed systems. BigQuery instantly tackles this issue by making unstructured data a first-class citizen using multimodal tables (preview), which integrate structured data with rich, complex data types for unified querying and storage.

Google Cloud's expanded BigQuery governance enables data stewards and professionals a single perspective to manage discovery, classification, curation, quality, usage, and sharing, including automatic cataloguing and metadata production, to efficiently manage this large data estate. BigQuery continuous queries use SQL to analyse and act on streaming data regardless of format, ensuring timely insights from all your data streams.

Customers utilise Google's AI models in BigQuery for multimodal analysis 16 times more than last year, driven by advanced support for structured and unstructured multimodal data. BigQuery with Vertex AI are 8–16 times cheaper than independent data warehouse and AI solutions.

Google Cloud maintains open ecology. BigQuery tables for Apache Iceberg combine BigQuery's performance and integrated capabilities with the flexibility of an open data lakehouse to link Iceberg data to SQL, Spark, AI, and third-party engines in an open and interoperable fashion. This service provides adaptive and autonomous table management, high-performance streaming, auto-AI-generated insights, practically infinite serverless scalability, and improved governance. Cloud storage enables fail-safe features and centralised fine-grained access control management in their managed solution.

Finaly, AI platform autonomous data optimises. Scaling resources, managing workloads, and ensuring cost-effectiveness are its competencies. The new BigQuery spend commit unifies spending throughout BigQuery platform and allows flexibility in shifting spend across streaming, governance, data processing engines, and more, making purchase easier.

Start your data and AI adventure with BigQuery data migration. Google Cloud wants to know how you innovate with data.

[craigbrownphd]

How I Built a Real-Time Weather Data Pipeline Using AWS—Entirely Serverless

#tech #Technology #DataAnalytics https://towardsdatascience.com/how-i-built-a-real-time-weather-data-pipeline-using-aws-entirely-serverless-12ddbca19289?source=rss----7f60cf5620c9--data_engineering&utm_source=dlvr.it&utm_medium=tumblr

[shantitechnology]

How Small and Mid-Sized Engineering Firms Can Benefit from ERP

In today’s competitive business landscape, manufacturers and engineering companies in India are under constant pressure to improve efficiency, reduce costs, and enhance productivity.  The adoption of ERP for manufacturing companies in India has become more than just a trend—it is a necessity for survival and growth.  Manufacturing ERP software in India is specifically designed to address the unique challenges faced by the industry, offering seamless integration, automation, and data-driven decision-making capabilities.

If you are an engineering or manufacturing business looking to streamline your operations, this blog will help you understand why ERP software for engineering companies in India is essential and how choosing the best ERP for the engineering industry can revolutionize your operations.

Why ERP is Essential for Manufacturing and Engineering Companies

1.  Streamlining Operations and Enhancing Efficiency

One of the biggest challenges faced by manufacturing and engineering companies is managing various processes such as inventory, procurement, production, and distribution.  Manufacturing ERP software in India centralizes data, enabling real-time monitoring and control over every aspect of the business.  This eliminates redundant tasks, reduces manual errors, and improves efficiency.

2.  Improved Supply Chain Management

A well-integrated ERP system ensures smooth coordination with suppliers, vendors, and distributors.  With ERP for manufacturing companies in India, businesses can track raw materials, monitor supplier performance, and optimize procurement processes, reducing delays and ensuring a seamless supply chain.

3.  Enhanced Data-Driven Decision Making

With access to real-time data analytics and comprehensive reporting, ERP software for engineering companies in India empowers businesses to make informed decisions.  Managers can analyze production trends, forecast demand, and identify areas for improvement, leading to better business outcomes.

4.  Cost Reduction and Higher Profitability

Automation of processes helps in minimizing waste, reducing operational costs, and increasing profitability.  The best ERP for the engineering industry ensures resource optimization by tracking inventory levels, reducing excess stock, and eliminating inefficiencies in production planning.

5.  Compliance and Quality Control

Manufacturers must adhere to strict industry standards and regulatory requirements.  Manufacturing ERP software in India helps in maintaining compliance by providing documentation, audit trails, and quality control measures, ensuring that all products meet industry regulations.

Key Features of the Best ERP for Engineering Industry

Choosing the right ERP solution is crucial for achieving maximum benefits.  Here are some key features to look for in an ERP software for engineering companies in India:

Comprehensive Production Planning & Control – Ensures seamless coordination between different production units.

Inventory & Material Management – Tracks stock levels, raw materials, and procurement processes efficiently.

Financial Management – Integrates accounting, payroll, and financial reporting for better fiscal control.

Supply Chain Management – Enhances supplier relationships and improves procurement efficiency.

Customer Relationship Management (CRM) – Manages customer interactions, sales pipelines, and service requests.

Business Intelligence & Reporting – Provides real-time insights for strategic decision-making.

Scalability & Customization – Adapts to the growing needs of your business with modular functionalities.

Top ERP Software Providers in India

India is home to some of the top ERP software providers, offering advanced solutions for engineering and manufacturing businesses.  Companies like Shantitechnology (STERP) have emerged as leaders in providing industry-specific ERP solutions that cater to the unique requirements of manufacturing and engineering firms.

Why Choose STERP?

STERP is one of the top ERP software providers in India, offering customized ERP solutions specifically designed for the engineering and manufacturing industries.  Here is why STERP stands out:

Industry-Specific Solutions – Tailored to meet the challenges of the manufacturing and engineering sectors.

Cloud & On-Premise Options – Flexible deployment models to suit different business needs.

User-Friendly Interface – Easy to use, with intuitive dashboards and real-time analytics.

Excellent Customer Support – Dedicated support teams for implementation and ongoing assistance.

Scalable Solutions – Designed to grow with your business, ensuring long-term usability and return on investment.

How to Implement ERP for Maximum Success

Step 1:  Assess Business Needs

Understand your business requirements and identify key areas that need improvement.  Choose a solution that aligns with your industry needs.

Step 2:  Choose the Right ERP Software

Selecting the best ERP for the engineering industry involves comparing features, scalability, pricing, and vendor support.

Step 3:  Customization & Integration

Ensure that the ERP system integrates seamlessly with your existing tools and is customizable to fit your unique business processes.

Step 4:  Training & Support

Invest in training programs to ensure that your team is comfortable using the new system.  Opt for a provider that offers continuous support and upgrades.

Step 5:  Monitor & Optimize

Post-implementation, continuously monitor the system’s performance, gather feedback, and make necessary optimizations to enhance efficiency.

Future Trends in ERP for Manufacturing and Engineering

The ERP landscape is evolving rapidly, with emerging trends shaping the future of ERP for manufacturing companies in India.  Some key trends to watch include:

AI & Machine Learning Integration – Automating predictive maintenance and process optimization.

Cloud-Based ERP Solutions – Offering flexibility, remote accessibility, and cost savings.

IoT-Enabled ERP – Enhancing real-time tracking of production and inventory.

Mobile ERP – Allowing on-the-go access for better decision-making.

Blockchain for Supply Chain Management – Ensuring transparency and security in transactions.

Conclusion

Investing in ERP software for engineering companies in India is no longer an option—it is a necessity for businesses looking to stay ahead in the competitive market.  Whether you are a small manufacturer or a large-scale engineering firm, having the best ERP for the engineering industry can drive efficiency, improve decision-making, and enhance overall profitability.

With industry leaders like Shantitechnology (STERP) offering cutting-edge solutions, businesses can achieve digital transformation effortlessly.  As one of the top ERP software providers in India, STERP continues to empower manufacturing and engineering companies with tailored ERP solutions.

Are you ready to revolutionize your business with ERP?  Contact STERP today and take the first step towards seamless automation and unmatched efficiency!

[cyberanalyst023]

Exploring the Azure Technology Stack: A Solution Architect’s Journey

Kavin

As a solution architect, my career revolves around solving complex problems and designing systems that are scalable, secure, and efficient. The rise of cloud computing has transformed the way we think about technology, and Microsoft Azure has been at the forefront of this evolution. With its diverse and powerful technology stack, Azure offers endless possibilities for businesses and developers alike. My journey with Azure began with Microsoft Azure training online, which not only deepened my understanding of cloud concepts but also helped me unlock the potential of Azure’s ecosystem.

In this blog, I will share my experience working with a specific Azure technology stack that has proven to be transformative in various projects. This stack primarily focuses on serverless computing, container orchestration, DevOps integration, and globally distributed data management. Let’s dive into how these components come together to create robust solutions for modern business challenges.

Understanding the Azure Ecosystem

Azure’s ecosystem is vast, encompassing services that cater to infrastructure, application development, analytics, machine learning, and more. For this blog, I will focus on a specific stack that includes:

Azure Functions for serverless computing.

Azure Kubernetes Service (AKS) for container orchestration.

Azure DevOps for streamlined development and deployment.

Azure Cosmos DB for globally distributed, scalable data storage.

Each of these services has unique strengths, and when used together, they form a powerful foundation for building modern, cloud-native applications.

1. Azure Functions: Embracing Serverless Architecture

Serverless computing has redefined how we build and deploy applications. With Azure Functions, developers can focus on writing code without worrying about managing infrastructure. Azure Functions supports multiple programming languages and offers seamless integration with other Azure services.

Real-World Application

In one of my projects, we needed to process real-time data from IoT devices deployed across multiple locations. Azure Functions was the perfect choice for this task. By integrating Azure Functions with Azure Event Hubs, we were able to create an event-driven architecture that processed millions of events daily. The serverless nature of Azure Functions allowed us to scale dynamically based on workload, ensuring cost-efficiency and high performance.

Key Benefits:

Auto-scaling: Automatically adjusts to handle workload variations.

Cost-effective: Pay only for the resources consumed during function execution.

Integration-ready: Easily connects with services like Logic Apps, Event Grid, and API Management.

2. Azure Kubernetes Service (AKS): The Power of Containers

Containers have become the backbone of modern application development, and Azure Kubernetes Service (AKS) simplifies container orchestration. AKS provides a managed Kubernetes environment, making it easier to deploy, manage, and scale containerized applications.

Real-World Application

In a project for a healthcare client, we built a microservices architecture using AKS. Each service—such as patient records, appointment scheduling, and billing—was containerized and deployed on AKS. This approach provided several advantages:

Isolation: Each service operated independently, improving fault tolerance.

Scalability: AKS scaled specific services based on demand, optimizing resource usage.

Observability: Using Azure Monitor, we gained deep insights into application performance and quickly resolved issues.

The integration of AKS with Azure DevOps further streamlined our CI/CD pipelines, enabling rapid deployment and updates without downtime.

Key Benefits:

Managed Kubernetes: Reduces operational overhead with automated updates and patching.

Multi-region support: Enables global application deployments.

Built-in security: Integrates with Azure Active Directory and offers role-based access control (RBAC).

3. Azure DevOps: Streamlining Development Workflows

Azure DevOps is an all-in-one platform for managing development workflows, from planning to deployment. It includes tools like Azure Repos, Azure Pipelines, and Azure Artifacts, which support collaboration and automation.

Real-World Application

For an e-commerce client, we used Azure DevOps to establish an efficient CI/CD pipeline. The project involved multiple teams working on front-end, back-end, and database components. Azure DevOps provided:

Version control: Using Azure Repos for centralized code management.

Automated pipelines: Azure Pipelines for building, testing, and deploying code.

Artifact management: Storing dependencies in Azure Artifacts for seamless integration.

The result? Deployment cycles that previously took weeks were reduced to just a few hours, enabling faster time-to-market and improved customer satisfaction.

Key Benefits:

End-to-end integration: Unifies tools for seamless development and deployment.

Scalability: Supports projects of all sizes, from startups to enterprises.

Collaboration: Facilitates team communication with built-in dashboards and tracking.

4. Azure Cosmos DB: Global Data at Scale

Azure Cosmos DB is a globally distributed, multi-model database service designed for mission-critical applications. It guarantees low latency, high availability, and scalability, making it ideal for applications requiring real-time data access across multiple regions.

Real-World Application

In a project for a financial services company, we used Azure Cosmos DB to manage transaction data across multiple continents. The database’s multi-region replication ensure data consistency and availability, even during regional outages. Additionally, Cosmos DB’s support for multiple APIs (SQL, MongoDB, Cassandra, etc.) allowed us to integrate seamlessly with existing systems.

Key Benefits:

Global distribution: Data is replicated across regions with minimal latency.

Flexibility: Supports various data models, including key-value, document, and graph.

SLAs: Offers industry-leading SLAs for availability, throughput, and latency.

Building a Cohesive Solution

Combining these Azure services creates a technology stack that is flexible, scalable, and efficient. Here’s how they work together in a hypothetical solution:

Data Ingestion: IoT devices send data to Azure Event Hubs.

Processing: Azure Functions processes the data in real-time.

Storage: Processed data is stored in Azure Cosmos DB for global access.

Application Logic: Containerized microservices run on AKS, providing APIs for accessing and manipulating data.

Deployment: Azure DevOps manages the CI/CD pipeline, ensuring seamless updates to the application.

This architecture demonstrates how Azure’s technology stack can address modern business challenges while maintaining high performance and reliability.

Final Thoughts

My journey with Azure has been both rewarding and transformative. The training I received at ACTE Institute provided me with a strong foundation to explore Azure’s capabilities and apply them effectively in real-world scenarios. For those new to cloud computing, I recommend starting with a solid training program that offers hands-on experience and practical insights.

As the demand for cloud professionals continues to grow, specializing in Azure’s technology stack can open doors to exciting opportunities. If you’re based in Hyderabad or prefer online learning, consider enrolling in Microsoft Azure training in Hyderabad to kickstart your journey.

Azure’s ecosystem is continuously evolving, offering new tools and features to address emerging challenges. By staying committed to learning and experimenting, we can harness the full potential of this powerful platform and drive innovation in every project we undertake.