#DataStreams

[salesforces-stuff]

Data Streams in Salesforce Data Cloud: Enhancing Real-Time Data Integration

​Salesforce Data Cloud's Data Streams feature enhances real-time data integration by allowing organizations to seamlessly connect and unify data from various sources. This capability enables businesses to create comprehensive customer profiles, leading to more personalized and effective customer interactions. By integrating disparate data streams, companies can gain deeper insights into customer behaviors and preferences, facilitating informed decision-making and improved customer engagement strategies. The implementation of Data Streams within Salesforce Data Cloud ensures that data remains current and accessible, supporting agile responses to market changes and customer needs. This integration ultimately drives operational efficiency and fosters stronger customer relationships.​ View More

[govindhtech]

Making Flink Apache Available Across Your Enterprise Data

Making Flink Apache consumable in every aspect of your company: Apache Flink for all.

In this age of fast technological development, adaptability is essential. Event-driven enterprises in every industry need real-time data to respond to events as they happen. By satisfying consumers, these adaptable companies identify requirements, meet them, and take the lead in the market.

What is Apache Flink?

Here’s where Flink Apache really shines, providing a strong way to fully utilize the processing and computational power of an event-driven business architecture. This is made feasible in large part by Flink tasks, which are built to process continuous data streams.

How Apache Flink improves enterprises that are event-driven in real time

Envision a retail business that has the ability to rapidly modify its inventory by utilizing real-time sales data pipelines. In order to take advantage of new opportunities, they can quickly adjust to shifting demands. Alternatively, think about a FinTech company that can identify and stop fraudulent transactions right away. Threats are neutralized, saving the company money and averting unhappy customers. Any business hoping to be a market leader in 2018 must have these real-time capabilities, they are no longer optional.

By processing raw events, Flink Apache increases their relevance within a larger business context. When events are joined, aggregated, and enriched during event processing, deeper insights are obtained and a wide range of use cases are made possible, including:

By tracking user behavior, financial transactions, or data from Internet of Things devices, data analytics: Assists in performing analytics on data processing on streams.

From continuously streaming data streams, pattern detection makes it possible to recognize and extract complicated event patterns.

Anomaly detection: Rapidly locates anomalous activities by identifying odd patterns or outliers in streaming data.

Data aggregation makes ensuring that continuous data flows are efficiently summarized and processed so that timely insights and decisions may be made.

Stream joins: These techniques combine information from several data sources and streaming platforms to enhance event correlation and analysis.

Data filtering: This process takes streaming data and applies certain conditions to extract pertinent data.

Data manipulation: Uses data mapping, filtering, and aggregation to transform and modify data streams.

Apache Flink’s distinct benefits

In order to help organizations respond to events more effectively in real time, Flink Apache enhances event streaming solutions such as Apache Kafka. Both Flink and Kafka are strong tools, however Flink has a few more special benefits:

Data stream processing uses efficient computing to provide stately, time-based processing of data streams for use cases including predictive maintenance, transaction analysis, and client customization.

Integration: Has little trouble integrating with other platforms and data systems, such as Apache Kafka, Spark, Hadoop, and different databases.

Scalability: Manages big datasets among dispersed computers, guaranteeing performance even in the most taxing Flink tasks.

Fault tolerance ensures dependability by recovering from faults without losing data.

IBM gives users more power and enhances Apache Kafka and Flink

The de-facto standard for real-time event streaming is Apache Kafka, which should come as no surprise. But that’s only the start. A single raw stream is insufficient for most applications, and many programs can utilize the same stream in different ways.

Events can be distilled using Flink Apache, allowing them to do even more for your company. Each event stream’s value can increase dramatically when combined in this way. Leverage advanced ETL procedures, improve your event analytics, and react faster and more effectively to growing business demands. With your fingertips, you can harness the power to provide real-time automation and insights.

IBM is leading the way in stream processing and event streaming, enhancing Apache Flink’s functionality. They want to address these significant industry challenges by offering an open and modular solution for event streaming and streaming applications. Any Kafka topic can be used with Flink Apache, making it accessible to everyone.

By enhancing what clients already have, IBM technology avoids vendor lock-in. Regardless of their role, users may exploit events to supplement their data streams with real-time context, even if they lack extensive knowledge of SQL, Java, or Python, thanks to its user-friendly and no-code style. Users can increase the number of projects that can be delivered by decreasing their reliance on highly qualified technicians and freeing up developers’ time. Enabling them to concentrate on business logic, create incredibly responsive Flink apps, and reduce application workloads are the objectives.

Proceed to the next action

Companies can take the lead in their endeavors no matter where they are in their journey thanks to IBM Event Automation, an entirely modular event-driven solution. Unlocking the value of events requires an event-driven architecture, which is made possible by the event streams, event processing capabilities, and event endpoint management. In order to promote smooth integration and control, you can also manage your events similarly to APIs.

With Flink Apache and IBM Event Automation, you can move closer to a competitive, responsive, and agile IT ecosystem.

Read more on govindhtech.com

[doodlelesbians]

Datastream defender mini comic!! :D

[iheartfurrympreg]

a contrapuntal poem of martyn and ren throughout the seasons (and the lack thereof)

[lythecreatorart]

Consequences of Abandon part 2

This part of the comic brought to you by “Die with a Smile” by Bruno Mars and Lady Gaga-

[itlw-mailbox]

[it was so awesome to have a communicator again. It was strange to see the case so clean though, very different from his original one which had been dirty and covered in stickers. He just hangs a charm off the new one for now, it’ll get more decorated over time.]

[he looks over it a bit, making sure it still has everything his old one had. All his messages had transferred over thankfully, meaning all his conversations were her- oh that’s martyn’s contact. Oh he was supposed to message the other once he was out of exile-]

RENDOG: martyn

RENDOG: marty

RENDOG: dude :D

RENDOG: new comm!!! and no more exile!!

RENDOG: you still fine with maybe hanging out soon now that im out??

@rendogs-mailbox

[His communicator buzzed- Ren? Okay, this was- yes! Martyn was both ecstatic and incredibly apprehensive. If Ren was back, that likely meant Doc was back too, which in turn meant communication, and likely a few more of his visions before they got it all worked out, basically, it was Martyn’s hell.]

[On the other hand, he got to see his not quite boyfriend, which would be nice. His communicator buzzed a few more times, and maybe he was hopeful thinking it’d just be Ren again.]

Doc(tor): you’re about to go back

Doc(tor): make an effort to stay in my sight this time

Doc(tor): and stop trusting people so freely

Doc(tor): doubt your ren is a C.H.E.S.T agent, he’s had plenty of chances to kill you, but doc? Watch your back

Doc(tor): everyone is either an npc or a C.H.E.S.T agent, don’t be fooled.

Doc(tor): I’ll be in touch

[Out of spite, Martyn didn’t even offer a reply to the man, he could shove his npcs and agents where the-]

[Reply to Ren, got it.]

InTheLittleWood: glad to have ya back bud

InTheLittleWood: I’ll be right there

[Without a moment more of hesitation, he tore back through the datastream, and into the hermitcraft server. Convenient. He began his search for Ren, slipping momentarily into code when any other hermit came into sight, not up for any conversations yet.]

[the-fandom-queenxox]

The ratchanting divorce isn't gonna happen because, Ren is overly friendly/trusting & doesn't know what the word "no" means and Martyn doesn't ever communicate his feelings/thoughts about anything that bothers him(because gods forbid we have healthy communications in minecraft smps/j)

It's gonna happen because Martyn was never even supposed to come back to the rats world in the first place and even so he still has a mission given to him to complete and at the end of every mission he has to leave the world he completed it in. So even if it wasn't rats but another world where everything played out exactly the same, the ultimate outcome of the situation would have been the same, that's just how it's supposed to be. No matter how long he has stayed there, no matter how attached he gets to everyone he meets, he still has to leave it all behind if he wants to make it back to his own world/reality(<<<idk???)

So in short, it was always gonna happen but it is only now hitting us all cause it's happening right now and it's as sad & angsty as we could have predicted

18/02/25 edit/update: Post canceled...

23/02/25 edit: the previous cancellation is no longer viable

[that-fall-guy]

"The Search" - a webweave for Room 3 of @mcytblrescape !!

Wild Life sources: clock, "Ohne Titel (Geldig)" by Kurt Schwitters, "Aucassin Seeks for Nicolette" by Maxfield Parrish, "Tamarisk Trees in Early Sunlight" by Guy Rose, window, blue flower, green flower, pink flower, pink flowers, camera, "Starting Fires" by Bears in Trees, "Like Real People Do" by Hozier, "Puppet Loosely Strung" by the Correspondents

Pirates & Rats 2 sources: clock, "Merzzeichnung in Merzzeichnung" by Kurt Schwitters, stamps, books, trinket tin, crystal skull, "14 Verses" by Declan Bennett, "Farewell Wanderlust" by the Amazing Devil, "Gods & Monsters" by Lana del Rey

New Life sources: clock, "20 Ore mit Koranseiten" by Kurt Schwitters, "Snow-Covered Landscape" by Guillaume Vogels, tamagotchi, camera, hat, backpack, "I Could Never Be" from Steven Universe, "Tread on Me" by Matt Maeson

Ultimate Survival SMP sources: clock, "C 50 Last Birds and Flowers" by Kurt Schwitters, "Ceylonese Jungle" by Hermann von Konigsbrunn, bear, beetle, moth, crown, gloves, vined hand, "King" by Lauren Aquilina, "I Just Don't Care That Much" by Matt Maeson

Limited & Secret Life sources: clock, "Spitzbergen Merzzeichnung" by Kurt Schwitters, "She came to the blue sea-ocean" by Ivan Bilibin, bird, letter, fish, cassette, "Queen of Nothing" by the Crane Wives, "What's a Devil to Do" by Harley Poe, "14 Verses" by Declan Bennett, "Bullet" by Saint Motel

Rats sources: clock, "Zeichnung I 9 Hebel 2" by Kurt Schwitters, "Candles" by Gerhard Richter, "Sunflower Seeds" by Ai Weiwei, band-aid tin, bazooka gum, amethyst geode, amethyst crystal, socks, tag, knife, "Puppet Loosely Strung" by the Correspondents, "What's a Devil to Do" by Harley Poe

Double Life sources: clock, "Mz x 21 Street" by Kurt Schwitters, "Loup Scar, Wharfdale" by Richard Jack, coin, coffin, cat in moon, bottle cap, receipt, "Honeybee" by Steam Powered Giraffe, "the broken hearts club" by gnash

Last Life sources: clock, "Merz 30, 42" by Kurt Schwitters, "Trees and Church Tower" by Raymond McIntyre, mask, locket, clover, scarecrow and rabbit, "Whispering Grass" by the Ink Spots, "How to Rest" by the Crane Wives

3rd Life sources: clock, "Sans Titre" by Kurt Schwitters, "Forest and Dove" by Max Ernst, window, heart, pomegranate, stamp, fox, "14 Verses" by Declan Bennett, "Honeybee" by Steam Powered Giraffe

Evo sources: clock, "Ohne Titel" by Kurt Schwitters, "The man with the cart" by Ivan Grohar, pearls, stars, window, feather, dog, "Rule #9 - Child of the Stars" by Fish in a Birdcage, "Dancing After Death" by Matt Maeson

Finale-unique sources: tv, warning window, video player, error tabs, handheld game console, progress bar, axe, "The Circle Maker" by Sparkbird, "The Mask" by Matt Maeson

All skins from namemc; all stereo pngs from this post. As I'm sure you can tell, this is a hell of a source list, so I apologize if I linked anything incorrectly or managed to forget something!

[andr3w-the-d0dgeb4ll]

The two different Jimmy aus I have plus their best friends and love interests lol

[wyrmgrass]

[agenericfae]

what two years can do to a man 😔😔😔

blud does NOT know that he's gonna turn into a rat. twice 💀💀‼️‼️

[govindhtech]

Vector Databases Tutorial: Data Access for Advanced AI Apps

Vector Database Tutorial

Vector databases are revolutionising artificial intelligence and  machine learning. These databases are revolutionising data storage and access and advancing  AI and machine learning applications. This essay will analyse vector databases’ unique abilities and how they are changing numerous sectors.

What is Vector Database?

Knowing the Fundamentals

Specialised database systems called vector databases are made with the purpose of effectively storing, managing, and retrieving high-dimensional vectors. Vectors are numerical representations of data items, such as words, photos, or human behaviours, that encapsulate their key characteristics in a multidimensional space in the context of artificial intelligence (AI) and machine learning. Since vector databases can manage complicated data types, they are perfect for tasks involving similarity search, clustering, and classification, in contrast to standard databases that store scalar values (such as integers and texts).

AI Vector database

Important Vector Database Features

These are particularly good at handling high-dimensional data, which frequently has hundreds or thousands of dimensions.

Effective Similarity Search

These databases carry out quick and precise similarity searches a necessary function for applications such as picture recognition and recommendation systems by utilising sophisticated indexing algorithms.

Scalability

These can manage massive data volumes without sacrificing performance because of its horizontal scalability design.

Integration with  AI Workflows

They easily interface with pipelines and models for machine learning, making it easier to train, implement, and infer  AI models.

Reasons Vector Databases Are Unbelievably Excellent

Improved Features for Search

The capacity of vector databases to conduct quick and precise similarity searches is one of their best qualities. High-dimensional data presents challenges for traditional databases, which frequently result in sluggish and inaccurate search results. On the other hand, this use methods like locality-sensitive hashing (LSH) and approximate nearest neighbour (ANN) search to swiftly identify the most similar vectors. This feature is especially useful for the following applications:

Recommendation Systems

Vector databases are remarkably accurate in providing personalised suggestions based on analysis of user behaviour and preferences.

Image and Video Search

They change the way media libraries are managed by providing quick access to comparable images or movies based on visual content.

Natural Language Processing, or NLP

Natural Language Processing NLP uses these to make semantic search more effective and precise by retrieving information based on word meanings rather than exact matches.

Processing Data in Real Time

Real-time data processing and analysis is essential in the big data era. Because vector databases can manage real-time data streams, they are perfect for applications that need quick decisions and responses. This comprises:

Fraud Detection

By using vector databases to track transactions in real-time, financial institutions can accurately detect and stop fraudulent activity.

Predictive Maintenance

Real-time sensor data analysis by vector databases in manufacturing enables the prediction of equipment faults and the proactive scheduling of maintenance.

Personalised Marketing

Based on user behaviour, marketers can send personalised adverts and promotions by using these to analyse user interactions in real-time.

Better Processes for Machine Learning

Machine learning procedures and vector databases work together seamlessly to improve the efficacy and efficiency of  AI models. These databases’ ability to store the vectors generated by models allows for:

Model Training

Large volumes of training data can be quickly accessed and stored in vector databases, facilitating both model training and retraining.

Inference

Vector databases are able to quickly obtain pertinent vectors during inference, guaranteeing AI applications minimal latency replies.

Continuous Learning

They help maintain the accuracy and currentness of  AI systems by enabling models to be updated in real-time with fresh data points.

Vector Database Applications in Industry

Retail and E-Commerce

Vector databases are revolutionising customer interactions in the retail and e-commerce industries. Using vector databases, businesses can:

Improved Product Suggestions

Provide extremely tailored product suggestions according to user preferences and behaviour.

Optimise Search Results

By offering more precise and pertinent search results based on vector similarity, you can enhance search functionality.

Analyse Customer Sentiment

To better understand consumer sentiment and develop goods and services, examine customer reviews and feedback.

Medical Care

The potential of vector databases is also advantageous to the healthcare sector. Examples of applications are:

Medical Image Analysis

By quickly storing and retrieving medical images, vector databases can help in illness diagnosis and treatment.

Genomics

Research and personalised therapy are made easier by their ability to analyse high-dimensional genomic data.

Patient Monitoring

The ability to process data in real-time facilitates ongoing patient health monitoring and prompt intervention.

Money

Vector databases are improving the speed and accuracy of a number of applications in the finance sector, including:

Risk management

Evaluate and effectively manage risks by analysing high-dimensional financial data.

Algorithmic Trading

Use real-time data processing to quickly and intelligently decide which trades to make.

Customer Insights

Acquire a deeper understanding of consumer behaviour and preferences in order to customise financial services and goods.

Vector Databases’ Future

Vector databases will become even more crucial as  AI and machine learning become more widely used. Potential future advancements in this field could be:

Improved Integration with  AI Platforms

More thorough integration with AI frameworks and platforms, which facilitates the deployment and management of AI applications even more.

Improvements in Indexing Methods

Indexing methods have been refined to increase similarity search speed and accuracy.

Use Case Expansion

As new and creative use cases are found in a variety of industries, vector databases are being adopted more widely.

In summary

Without a doubt, vector databases are changing the field of artificial intelligence and  machine learning. They are absurdly strong at handling high-dimensional data, processing real-time data, conducting effective similarity searches, and improving machine learning operations. Vector databases will be essential in opening doors and spurring innovation as industries investigate and use this technology.

Read more on Govindhtech.com

[doodlelesbians]

HAPPY BIRTHDAY BOSS!!!

[lythecreatorart]

Consequences of Abandon part 1

[the-fandom-queenxox]

Honestly the biggest question I have for rats 2 is, if Martyn is going to be in it... like in the main cast again or just in the server in general

Cause spoilers for those who don't know, he LEFT the world of the rats. Like literally. He in a easy way to explain "world hopped" out of there

Guess we'll wait and see till we get some news about it...

[ilexdiapason]

may I ask for either Pi-Rats or majorwood?

now on ao3

"What's funny?" The captain snickers. "Your little face! It's adorable!" "Alright, fair," Mratyn admits. "I just missed you, Lieutenant. Let me give you a smooch." He scampers over, past the conversation Ratman and Ratchela and Ros are having, and plants one right on Mratyn's lips. It sets him all aflutter, admittedly - he honest-to-goodness swoons, so overcome is he by affection. "Oh, lovestruck! I'm knocked off my feet." It's wild. He's never felt like this about the other characters he's played alongside; this genuine crushing investment, like he's got way more than three months' connection with Ren - Jaque - the captain - whatever.

VOTE TREEBARK