High-Tech

Data Science Dojo is offering Memphis broker for FREE on Azure Marketplace preconfigured with Memphis, a platform that provides a P2P architecture, scalability, storage tiering, fault-tolerance, and security to provide real-time processing for modern applications suitable for large volumes of data. 

Introduction

It is a cumbersome and tiring process to install Docker first and then install Memphis. Then look after the integration and dependency issues. Are you already feeling tired? It is somehow confusing to resolve the installation errors. Not to worry as Data Science Dojo’s Memphis instance fixes all of that. But before we delve further into it, let us get to know some basics.  

What is Memphis? 

Memphis is an open-source modern replacement for traditional messaging systems. It is a cloud-based messaging system with a comprehensive set of tools that makes it easy and affordable to develop queue-based applications. It is reliable, can handle large volumes of data, and supports modern protocols. It requires minimal operational maintenance and allows for rapid development, resulting in significant cost savings and reduced development time for data-focused developers and engineers. 

Challenges for individuals

Traditional messaging brokers, such as Apache Kafka, RabbitMQ, and ActiveMQ, have been widely used to enable communication between applications and services. However, there are several challenges with these traditional messaging brokers: 

1. Scalability: Traditional messaging brokers often have limitations on their scalability, particularly when it comes to handling large volumes of data. This can lead to performance issues and message loss. 
2. Complexity: Setting up and managing a traditional messaging broker can be complex, particularly when it comes to configuring and tuning it for optimal performance.
3. Single Point of Failure: Traditional messaging brokers can become a single point of failure in a distributed system. If the messaging broker fails, it can cause the entire system to go down. 
4. Cost: Traditional messaging brokers can be expensive to deploy and maintain, particularly for large-scale systems. 
5. Limited Protocol Support: Traditional messaging brokers often support only a limited set of protocols, which can make it challenging to integrate with other systems and technologies. 
6. Limited Availability: Traditional messaging brokers can be limited in terms of the platforms and environments they support, which can make it challenging to use them in certain scenarios, such as cloud-based systems.

Overall, these challenges have led to the development of new messaging technologies, such as event streaming platforms, that aim to address these issues and provide a more flexible, scalable, and reliable solution for modern distributed systems.  

Memphis as a solution

Why Memphis? 

“It took me three minutes to build in Memphis what took me a week and a half in Kafka.” Memphis and traditional messaging brokers are both software systems that facilitate communication between different components or systems in a distributed architecture. However, there are some key differences between the two: 

1. Architecture: It uses a peer-to-peer (P2P) architecture, while traditional messaging brokers use a client-server architecture. In a P2P architecture, each node in the network can act as both a client and a server, while in a client-server architecture, clients send messages to a central server which distributes them to the appropriate recipients. 
2. Scalability: It is designed to be highly scalable and can handle large volumes of messages without introducing significant latency, while traditional messaging brokers may struggle to scale to handle high loads. This is because Memphis uses a distributed hash table (DHT) to route messages directly to their intended recipients, rather than relying on a centralized message broker. 
3. Fault tolerance: It is highly fault-tolerant, with messages automatically routed around failed nodes, while traditional messaging brokers may experience downtime if the central broker fails. This is because it uses a distributed consensus algorithm to ensure that all nodes in the network agree on the state of the system, even in the presence of failures. 
4. Security: Memphis provides end-to-end encryption by default, while traditional messaging brokers may require additional configuration to ensure secure communication between nodes. This is because it is designed to be used in decentralized applications, where trust between parties cannot be assumed. 

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Overall, while both Memphis and traditional messaging brokers facilitate communication between different components or systems, they have different strengths and weaknesses and are suited to different use cases. It is ideal for highly scalable and fault-tolerant applications that require end-to-end encryption, while traditional messaging brokers may be more appropriate for simpler applications that do not require the same level of scalability and fault tolerance. 

What struggles does Memphis solve? 

Handling too many data sources can become overwhelming, especially with complex schemas. Analyzing and transforming streamed data from each source is difficult, and it requires using multiple applications like Apache Kafka, Flink, and NiFi, which can delay real-time processing.

Additionally, there is a risk of message loss due to crashes, lack of retransmits, and poor monitoring. Debugging and troubleshooting can also be challenging. Deploying, managing, securing, updating, onboarding, and tuning message queue systems like Kafka, RabbitMQ, and NATS is a complicated and time-consuming task. Transforming batch processes into real-time can also pose significant challenges.

Integrations: 

Memphis Broker provides several integration options for connecting to diverse types of systems and applications. Here are some of the integrations available in Memphis Broker: 

• JMS (Java Message Service) Integration 
• .NET Integration 
• REST API Integration 
• MQTT Integration 
• AMQP Integration 
• Apache Camel, Apache ActiveMQ, and IBM WebSphere MQ. 

Key features: 

• Fully optimized message broker in under 3 minutes 
• Easy-to-use UI, CLI, and SDKs 
• Dead-letter station (DLQ) 
• Data-level observability 
• Runs on your Docker or Kubernetes
• Real-time event tracing 
• SDKs: Python, Go, Node.js, Typescript, Nest.JS, Kotlin, .NET, Java 
• Embedded schema management using Protobuf, JSON Schema, GraphQL, Avro 
• Slack integration

What Data Science Dojo has for you: 

Azure Virtual Machine is preconfigured with plug-and-play functionality, so you do not have to worry about setting up the environment. Features include a zero-setup Memphis platform that offers you to: 

• Build a dead-letter queue 
• Create observability 
• Build a scalable environment 
• Create client wrappers 
• Handle back pressure. Client or queue side 
• Create a retry mechanism 
• Configure monitoring and real-time alerts 

Moreover, Memphis is a next-generation alternative to traditional message brokers. A simple, robust, and durable cloud-native message broker wrapped with an entire ecosystem that enables cost-effective, fast, and reliable development of modern queue-based use cases.

Wrapping up  

Memphis comes pre-configured with Ubuntu 20.04, so users do not have to set up anything featuring a plug n play environment. It on the cloud guarantees high availability as data can be distributed across multiple data centers and availability zones on the go. In this way, Azure increases the fault tolerance of data pipelines.

The power of Azure ensures maximum performance and high throughput for the server to deliver content at low latency and faster speeds. It is designed to provide a robust messaging system for modern applications, along with high scalability and fault tolerance.

The flexibility, performance, and scalability provided by Azure virtual machine to Memphis make it possible to offer a production-ready message broker in under 3 minutes. They provide durability and stability and efficient performing systems. 

When coupled with Microsoft Azure services and processing speed, it outperforms the traditional counterparts because data-intensive computations are not performed locally, but in the cloud. You can collaborate and share notebooks with various stakeholders within and outside the company while monitoring the status of each  

At Data Science Dojo, we deliver data science education, consulting, and technical services to increase the power of data. We are therefore adding a free Memphis instance dedicated specifically for highly scalable and fault-tolerant applications that require end-to-end encryption on Azure Market Place. Do not wait to install this offer by Data Science Dojo, your ideal companion in your journey to learn data science!

 

 

 

 

In last week’s post, DS-Dojo introduced our readers to this blog-series’ three focus areas, namely: 1) software development, 2) project-management, and 3) data science. This week, we continue that metaphorical (learning) journey with a fun fact. Better yet, a riddle. What do ALL jobs have in common?

 

One can (correctly) argue that essentially, all jobs require the worker in question to accomplish one basic or vital goal: solve (a) problem(s). And indeed, one can earnestly argue that the three interdisciplinary fields of this series (software-development, project-management, and data science) are iconic vis-a-vis their problem-solving characteristics. 

 

Advanced problem-solving tools for a (post-) modern world

One of the paradoxes of our (post-)modern era is this fact: our lives have become so much easier, safer, and much more enjoyable, thanks to digital technology. And yet simultaneously, our lives have gotten so complicated, with an overwhelming glut of technological tools at our disposal. 

 

And I suppose one can view this as a “rich person-problem,” akin to a kid in a candy store, indeed. In any case, here is the good news: “as luck would have it,” we can utilize a simple (set of) tool(s), with which we can both solve problems expansively, and/or simplify our lives as needed. 

 

To the rescue (!): Google, checklists, algorithms and data structures, and project-management

Incidentally, a Google search using search terms related to the topic at hand suggests a consensus vis-a-vis best practices for solving problems, and/or simplifying our lives.

Ultimately, we can use two or three vital tools: 1) [either] a simple checklist, 2) [or,] the interdisciplinary field of project-management, and 3) algorithms and data structures.

 

Here’s a fun question for you, dear reader: can you think of a tool that can simplify both simple and complex tasks such as i) grocery shopping, ii) surgery, and iii) safely flying an airplane? If you answered, “a checklist,” you’re correct. 

 

But for more complicated problems, the interdisciplinary field of project management might be useful–i.e., via the 12 (project-management) elements introduced in last week’s post. To recap, those twelve elements (e.g. as defined by Belinda Goodrich, 2021) are: 

• Project life cycle, 
• Integration, 
• Scope, 
• Schedule, 
• Cost, 
• Quality, 
• Resources, 
• Communications, 
• Risk, 
• Procurement, 
• Stakeholders, and 
• Professional responsibility / ethics. 

 

In addition to the mindful use of the above twelve elements, our Google-search might reveal that various authors suggest some vital algorithms for data science. For instance, in the table below, we juxtapose four authors’ professional opinions with DS-Dojo’s curriculum.

 

What problem-solving tools next digital age has to offer 

Thanks to Moore’s law (e.g., as described via the relevant Wikipedia article about Moore’s law and other factors, the digital age will keep producing hardware and software tools that are both wondrous, and/or overwhelming (e.g., IoT, Web 3.0, metaverse, quantum-computing, etc.).

In this blog post, DS-Dojo provides a potential remedy to our readers vis-a-vis finding easier solutions to our world’s problems, and the avoidance of that “spoilt for choice” dilemma.

By using checklists and tools derived from the three interdisciplinary fields of this blog series, we can solve our world’s ever-growing/evolving problems, and/or simplify our lives as needed.

 

Sample Overview of Data-Science Dojo’s Curriculum: Weeks 1 to 3: Introduction to Quantitative Data-Analysis Weeks 4 to 8: Classification Week 9: Applications of Classification Week 10: Special Topic: Text Analysis Fundamentals Week 11: Unsupervised Learning Weeks 12 and 13: Regression Weeks 14 to 16: More Applications of Previously-Learned Concepts
VS.
Tech-Vidvan’s  “Top 10”: Linear Regression Logistic Regression Decision Trees Naive Bayes K-Nearest Neighbors Support Vector Machine K-Means Clustering Principal Component Analysis Neural Networks Random Forests	P. Zheng’s “Guide to Data Structures and Algorithms” Parts 1 and Part 2 1) Big O Notation 2) Search 3) Sort 3)–i)–Quicksort 3)–ii–Mergesort 4) Stack 5) Queue  6) Array 7) Hash Table 8) Graph 9) Tree (e.g., Decision Tree) 10) Breadth-First Search 11) Depth-First Search 12) Dijkstra’s Algorithm	Disha Ganguli’s Top 10 Linear Regression   Logistic Regression   Decision Trees   ID3 Algorithm   Cart Algorithm   Naïve Bayes   K-nearest neighbors (KNN)  Support vector machine (SVM)  K-means clustering  PCA Algorithm	Data-Quest’s Top 10: 5 Supervised Learning Techniques:  1) Linear Regression  2) Logistic Regression 3) CART  4) Naïve Bayes  5) KNN 3 Unsupervised Learning Techniques 6) Apriori 7) K-means  8) PCA 2 Ensembling Techniques 9) Bagging with Random Forests  10) Boosting with XGBoost.

In this blog, we asked ChatGPT to come up with some interesting and fun facts of the core areas related to data science, Artificial Intelligence, machine learning, and Statistics. The new AI tool surely knows the struggles of professionals and how to put it in a fun way. 

 

Some fun facts for Mathematicians by ChatGPT:   

 

Here are some fun facts for all statisticians to make you laugh by ChatGPT:

• Statisticians have a sense of humor too! The probability of someone laughing at a joke you tell is directly proportional to the number of statistical jokes you know.  
• A statistician’s favorite thing to do is to take a sample. 
• A statistician’s favorite drink is a “mean” martini. 
• A statistician’s favorite time of day is “mean time.” 
• Statisticians have a tendency to “mean” well. 
• Statisticians are always looking for a “standard deviation” from the norm. 
• Statisticians are always looking for a “mean” to get ahead. 
• Statisticians are never “mean” to each other – they are always “normally distributed.”
• Mathematicians are always on the lookout for new “equations” to solve.  
• A mathematician’s favorite thing is to “prove” a theorem.  
• Mathematicians are known to have a “matrix” of connections in the industry. 
• Mathematicians have been known to “calculate” the most unlikely scenarios. 
• A mathematician’s favorite type of math is “abstract” math – it keeps things interesting! 
• Mathematicians are always looking for ways to “optimize” their equations. 
• Mathematicians have a saying: “The more complex the problem, the better!” 
• Mathematicians are known to be “precise” – they never leave room for error in their calculations.  

  

  

Some fun facts for Data Scientists by ChatGPT  

 

Here are a few funny facts about data scientists: 

• Data scientists have been known to “mine” for data in the most unlikely places. 
• A data scientist’s favorite type of data is “big data” – the bigger, the better! 
• A data scientist’s favorite tool is the “data hammer” – they can pound any data into submission. 
• Data scientists have a saying: “The data never lies, but it can be misleading.” 
• Data scientists have been known to “data dunk” their colleagues – throwing them into a pool of data and seeing if they can swim. 
• Data scientists are always “data mining” for new insights and discovering “data gold.” 
• Data scientists are known to have “data-phoria” – a state of excitement or euphoria when they uncover a particularly interesting or valuable piece of data. 
• Data scientists have been known to “data mash” – combining different datasets to create something new and interesting. 

 

 Enroll in our Data Science Bootcamp course to become a Data Scientist today

 

Some fun facts for Machine Learning professionals by ChatGPT 

 

Here are some fun facts about machine learning professionals   

• Machine learning professionals are always on the lookout for new “learning opportunities.” 
• A machine learning professional’s favorite thing is to “train” their algorithms. 
• Machine learning professionals are known to have a “neural network” of friends in the industry. 
• Machine learning professionals have been known to “deep learn” on the job – immersing themselves in their work and picking up new skills along the way. 
• A machine learning professional’s favorite type of data is “clean” data – it makes their job much easier! 
• Machine learning professionals are always looking for ways to “optimize” their algorithms. 
• Machine learning professionals have a saying: “The more data, the merrier!” 
• Machine learning professionals are known to be “adaptive” – they can quickly adjust to new technologies and techniques. 

    

Some fun facts for AI experts by ChatGPT 

 

Here are a few funny facts about artificial intelligence experts:   

• AI experts are always on the lookout for new “intelligent” ideas. 
• AI experts have been known to “teach” their algorithms to do new tasks. 
• AI experts are known to have a “neural network” of connections in the industry. 
• AI experts have been known to “deep learn” on the job – immersing themselves in their work and picking up new skills along the way. 
• AI experts are always looking for ways to “optimize” their algorithms. 
• AI experts have a saying: “The more data, the smarter the AI!” 
• AI experts are known to be “adaptive” – they can quickly adjust to new technologies and techniques. 
• AI experts are always looking for ways to make their algorithms more “human-like.”  
• The term “artificial intelligence” was first coined in 1956 by computer scientist John McCarthy. 
• The first recorded instance of artificial intelligence was in the early 1800s when mathematician Charles Babbage designed a machine that could perform basic mathematical calculations. 
• One of the earliest demonstrations of artificial intelligence was the “Turing Test,” developed by Alan Turing in 1950. The test is a measure of a machine’s ability to exhibit intelligent behavior equivalent to, or indistinguishable from, that of a human. 
• The first self-driving car was developed in the 1980s by researchers at Carnegie Mellon University. 
• In 1997, a computer program called Deep Blue defeated world chess champion Garry Kasparov, marking the first time a computer had beaten a human at chess. 
• In 2011, a machine translation system developed by Google called Google Translate was able to translate entire documents from Chinese to English with near-human accuracy. 
• In 2016, a machine learning algorithm developed by Google DeepMind called AlphaGo defeated the world champion at the ancient Chinese board game Go, which is considered to be much more complex than chess. 
• Artificial intelligence has the potential to revolutionize a wide range of industries, including healthcare, finance, and transportation.  

  

Some fun facts for Data Engineers by ChatGPT 

 

Here are a few funny facts about data engineers by ChatGPT: 

• Data engineers are always on the lookout for new “pipelines” to build. 
• A data engineer’s favorite thing is to “ingest” large amounts of data. 
• Data engineers are known to have a “data infrastructure” of connections in the industry. 
• Data engineers have been known to “scrape” the internet for new data sources. 
• A data engineer’s favorite type of data is “structured” data – it makes their job much easier! 
• Data engineers are always looking for ways to “optimize” their data pipelines. 
• Data engineers have a saying: “The more data, the merrier!” 
• Data engineers are known to be “adaptive” – they can quickly adjust to new technologies and techniques. 

  

 

Do you have a more interesting answer by ChatGPT?

People across the world are generating interesting responses using ChatGPT. The new AI tool has an immense contribution to the knowledge of professionals associated with different industries. Not only does it produce witty responses but also share information that is not known before by many. Share with us your use of this amazing AI tool as a Data Scientist.

In this blog, we will learn in detail about the reverse image search functionality and how it helps users to find relevant images instantly.

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