🥲TOP fails with different DBMSs: pain, tears

✅PostgreSQL and the vacuum of surprise
Everyone loves PostgreSQL until they encounter the autovacuum. If you forget to configure it correctly, the database starts to slow down so much that it's easier to migrate data to Excel.

✅Cassandra: master of sharding and chaos
Oh, this magical world of distributed data! As long as everything is running smoothly, Cassandra is cool. But when one node fails, clusters become a mystery with a surprise: what part of the data survived? And cross-DC replication in large networks is a lottery.

✅Firebase Realtime Database
Sounds cool: data synchronized in real time! But when you have tens of thousands of active users, everything becomes hell, because every little query costs a ton of money. And unmonitored updates affect all clients at once.

✅Redis as the main database
Easy, fast, everything in memory. Sounds cool until you realize that they forgot about the data recovery mechanism. Oops, the server crashed - data flew to nowhere.

😎🔥A small collection of useful datasets:

Synthia-v1.5-I – a dataset that includes over 20,000 technical questions and answers. It uses system prompts in the Orca style to generate diverse responses, making it a valuable resource for training and testing LLMs on complex technical data.

HelpSteer2 – an English-language dataset designed for training reward models that improve the utility, accuracy, and coherence of responses generated by other LLMs.

LAION-DISCO-12M – includes 12 million links to publicly available YouTube tracks with metadata. The dataset is created to support research in machine learning, sound processing model development, musical data analysis, audio data processing, and training recommender systems and applications.

Universe – a large-scale collection containing astronomical data of various types: images, spectra, and light curves. It is intended for research in astronomy and astrophysics.

😎📊Data Trends That Will Transform Business in 2025

The article The Most Powerful Data Trends That Will Transform Business In 2025 highlights key trends shaping the future of data usage.

🤔Here are some of them:

✅ Confidential Computing: Blockchain and homomorphic encryption will enable data analysis without exposing its content. This is a crucial step for secure collaborative analytics between companies.

✅ Growth of Data Marketplaces: Businesses will start monetizing their datasets, creating new revenue streams. Specialized platforms for trading data will emerge.

✅ Expansion of Edge Computing: Processing data at the network edge will reduce latency and enhance security. Technologies like tinyML will transform industries where real-time data processing is critical.

✅ Behavioral Data as a New Asset: Emotional and behavioral data analysis will underpin personalized solutions and decision-making.

😎A Small Selection of Useful Big Data Repositories

Complete-Advanced-SQL-Series – a repository that provides everything you need to enhance your SQL skills, including over 100 exercises and examples.

ds-cheatsheet– a GitHub repository offering a variety of useful Data Science cheatsheets.

postgres_for_everything – a collection of examples showcasing how PostgreSQL can be used for tasks such as message queues, analytics, access control, GIS, time-series data handling, search, caching, and more.

GenAI Showcase – demonstrates the use of MongoDB in generative AI, featuring examples of integration with Retrieval-Augmented Generation (RAG) and various AI models.

Data-and-ML-Projects – a repository containing over 50 projects across areas like Data Analytics, Data Science, Data Engineering, MLOps, and Machine Learning.

🧐Multithreading: PostgreSQL vs. MSSQL Server – Pros and Cons

Both PostgreSQL and MSSQL Server are popular databases for web application infrastructure. Here’s a quick comparison of their multithreading models:

PostgreSQL

👍 Pros:
✅ Process-based model ensures isolation and minimizes interference.
✅ Stability and security reduce deadlock risks.
✅ Flexible scaling for individual tasks.

❌ Cons:
✅ High memory usage per process.
✅ Limited performance with many connections.
✅ Challenges with horizontal scaling.

MSSQL Server

👍 Pros:
✅ Thread-based model efficiently utilizes CPU and memory.
✅ High scalability for numerous parallel connections.
✅ Optimized for Windows servers.
✅ Fast thread switching boosts performance in competitive systems.

❌ Cons:
✅ Troubleshooting is harder due to parallel execution.
✅ Higher risk of deadlocks.
✅ Requires advanced administrative effort for thread management.

🤔Which to Choose?
PostgreSQL: For moderate connections, stable loads, and reliability.
MSSQL Server: For high-load systems needing peak scalability and performance.

😎💡FineMath: A New Math Dataset by Hugging Face

Hugging Face has released FineMath, a comprehensive dataset for training models on mathematical content. It was constructed using CommonCrawl, a classifier trained on LLama-3.1-70B-Instruct annotations, and a thorough data filtering process.

Compared to OpenWebMath and InfiMM, FineMath shows more consistent accuracy improvements as the dataset size increases, thanks to its high quality and diverse content.

A project utilizing FineMath for training LLMs in math assistance is already live — explore the GitHub repository.

🌎TOP DS-events all over the world in January 2025
Jan 7-8 - HPC Monthly Workshop: Machine Learning and BIG DATA - https://www.psc.edu/resources/training/hpc-workshop-big-data-january-7-8-2025/
Jan 9 - Innovative Practices in Science & Technology - Taipei, Taiwan - https://phdcluster.confx.org/wcipst-9jan-taipei/
Jan 9-10 – ICUSGBD - Seville, Spain - https://conferenceineurope.net/eventdetail/2640428
Jan 10-12 - ACIE 2025 - Phuket, Thailand - https://acie.org/
Jan 10-12 - ICSIM 2025 - Singapore, Singapore - https://www.icsim.org/
Jan 15-17 - IT / Digital Transformation (DX) show - INTEX OSAKA, Japan - https://www.japan-it.jp/osaka/en-gb.html
Jan 21 - ElasticON Tour - La Salle Wagram, Paris - https://dev.events/conferences/elastic-on-6dyjbty
Jan 23-24 - 5th Annual Excellence in Master Data Management & Data Governance Summit - Amsterdam, The Netherlands - https://tbmgroup.eu/etn/5th-annual-excellence-in-master-data-management-data-governance-summit-cross-industry/
Jan 25 – CBIoTML - Atlanta,USA - https://bigdataresearchforum.com/Conference/267/ICBIoTML/
Jan 31-Feb 2 - Artificial Intelligence & Innovation in Healthcare - Dubai, UAE - https://maiconferences.com/artificial-intelligence-in-healthcare/

🤔What is the difference between Smart Data and Big Data?

In the article What’s Smart data and how it’s different from Big data? the author examines the features of "Smart Data". Below we will give our vision of this concept (it may differ, or it may coincide🥸).

So, Smart Data is a concept focused on processing, analyzing and using data taking into account its relevance, quality and usefulness for decision-making. Unlike Big Data, where the emphasis is on volume, Smart Data focuses on extracting valuable information from a huge array of data.

🤔Smart Data Features:
✅Data Quality: Selection of only relevant, accurate and structured data
✅Contextuality: Data is processed taking into account its significance for a specific task
✅Real-time analytics: Smart Data is used to enable quick decision-making

🤔Benefits:
✅Efficiency: Saving resources by working only with the necessary data
✅Personalization: Ability to tailor services to specific needs
✅Fewer Errors: Focus on high data quality reduces the risk of obtaining incorrect results

🥸However, not everything is so rosy, there are also disadvantages:
✅Ethical and legal issues: Working with personal data carries risks of privacy violation and misuse of information. This can lead to fines and loss of trust
✅High dependence on data quality: If the source data is incomplete, inaccurate or outdated, the results of the analysis can be misleading and impair decision making
✅High implementation costs: Requires investment in technology, time and qualified personnel
✅Problems with interpreting results: Even with high-quality data, analytics can be difficult for non-experts to understand, which requires additional training costs for employees
✅Technical failures: The infrastructure for processing data can be vulnerable to failures, which is especially critical when working with real-world processes such as financial or medical management

🧐Thus, Smart Data is about the meaningful use of data to achieve specific goals. This concept allows companies not only to cope with information noise, but also to gain competitive advantages. However, implementation requires a well-thought-out strategy and resources, otherwise there is a risk of incurring huge losses

😎💡Top Collection of Useful Data Tools

✅ gitingest — A utility created for automating data analysis from Git repositories. It allows collecting information about commits, branches, and authors, then transforming it into convenient formats for integration with language models (LLM). This tool is perfect for analyzing change histories, building models based on code, and automating work with repositories.

✅ datasketch — A Python library for optimizing work with large data. It provides probabilistic data structures, including MinHash for Jaccard similarity estimation and HyperLogLog for counting unique items. These tools allow quick tasks such as finding similar items and cardinality analysis with minimal memory and time consumption.

✅Polars — A high-performance library for working with tabular data, developed in Rust with Python support. The library integrates with NumPy, Pandas, PyArrow, Matplotlib, Plotly, Scikit-learn, and TensorFlow. Polars supports filtering, sorting, merging, joining, and grouping data, providing high speed and efficiency for analytics and handling large volumes of data.

✅ SQLAlchemy — A library for working with databases, supporting interaction with PostgreSQL, MySQL, SQLite, Oracle, MS SQL, and other DBMS. It provides tools for object-relational mapping (ORM), simplifying data management by allowing developers to work with Python objects instead of writing SQL queries, while also supporting flexible work with raw SQL for complex scenarios.

✅ SymPy — A library for symbolic mathematics in Python. It allows performing operations on expressions, equations, functions, matrices, vectors, polynomials, and other objects. With SymPy, you can solve equations, simplify expressions, calculate derivatives, integrals, approximations, substitutions, factorizations, and work with logarithms, trigonometry, algebra, and geometry.

✅ DeepChecks — A Python library for automated model and data validation in machine learning. It identifies issues with model performance, data integrity, distribution mismatches, and other aspects. DeepChecks allows for easy creation of custom checks, with results visualized in convenient tables and graphs, simplifying analysis and interpretation.

✅ Scrubadub — A Python library designed to detect and remove personally identifiable information (PII) from text. It can identify and redact data such as names, phone numbers, addresses, credit card numbers, and more. The tool supports rule customization and can be integrated into various applications for processing sensitive data.

⚔️ Kafka 🆚 RabbitMQ: Head-to-Head Clash

In the article RabbitMQ vs Kafka: Head-to-head confrontation in 8 major dimensions, the author compares two well-known tools: Apache Kafka and RabbitMQ.

Here are two primary differences between them:

✅ RabbitMQ is a message broker that handles routing and queue management.
✅ Kafka is a distributed streaming platform that focuses on data storage and message replay.

🤔 Key Characteristics:

✅ Message Order: Kafka ensures order within a single topic, while RabbitMQ provides only basic guarantees.
✅ Routing: RabbitMQ supports complex routing rules, whereas Kafka requires additional processing for message filtering.
✅ Message Retention: Kafka stores messages regardless of their consumption status, while RabbitMQ deletes messages after they are processed.
✅ Scalability: Kafka delivers higher performance and scales more efficiently.

🤔 Error Handling:

✅ RabbitMQ: Offers built-in tools for handling failed messages, such as Dead Letter Exchanges.
✅ Kafka: Error handling requires implementing additional mechanisms at the application level.
In summary, RabbitMQ is well-suited for tasks requiring flexible routing, time-based message management, and advanced error handling, while Kafka excels in scenarios with strict ordering requirements, long-term message storage, and high scalability.

💡 The article also emphasizes that both platforms can be used together to address different needs in complex systems.

🧐 Distributed Computing: Hit or Miss

In the article Optimizing Parallel Computing Architectures for Big Data Analytics, the author explains how to efficiently distribute workloads when processing Big Data using Apache Spark.

🤔 However, the author doesn't address the key advantages and disadvantages of distributed computing, which we inevitably have to navigate.

💡 Advantages:

✅ Scalability: Easily expand computational capacity by adding new nodes.
✅ Fault tolerance: The system remains operational even if individual nodes fail, thanks to replication and redundancy.
✅ High performance: Concurrent data processing across nodes accelerates task execution.

⚠️ Now for the disadvantages:

✅ Management complexity: Coordinating nodes and ensuring synchronized operation requires a sophisticated architecture.
✅ Security: Distributing data makes protecting it from breaches and attacks more challenging.
✅ Data redundancy: Ensuring fault tolerance often requires data replication, increasing storage overhead.
✅ Consistency issues: Maintaining real-time data consistency across numerous nodes is difficult (as per the CAP theorem).
✅ Update challenges: Making changes to a distributed system, such as software updates, can be lengthy and risky.
✅ Limited network bandwidth: High data transfer volumes between nodes can overload the network, slowing down operations.

🥸 Conclusion:
Distributed computing offers immense opportunities for scaling, accelerating computations, and ensuring fault tolerance. However, its implementation comes with a host of technical, organizational, and financial challenges, including managing complex architectures, ensuring data security and consistency, and meeting demanding network infrastructure requirements.

📚 A small selection of books on Data Science and Big Data

Software Engineering for Data Scientists - This book explains the mechanisms and practices of software development in Data Science. It also includes numerous implementation examples in Python.

Graph Algorithms for Data Science - The book covers key algorithms and methods for working with graphs in data science, providing specific recommendations for implementation and application. No prior experience with graphs is required. The algorithms are explained in simple terms, avoiding unnecessary jargon, and include visual illustrations to make them easy to apply in your projects.

Big Data Management and Analytics - This book covers all aspects of working with big data, from the basics to detailed practical examples. Readers will learn about selecting data models, extracting and integrating data for big data tasks, modeling data using machine learning methods, scalable Spark technologies, transforming big data tasks into graph databases, and performing analytical operations on graphs. It also explores various tools and methods for big data processing and their applications, including in healthcare and finance.

Advanced Data Analytics Using Python - This book explores architectural patterns in data analytics, text and image classification, optimization methods, natural language processing, and computer vision in cloud environments.

Minimalist Data Wrangling with Python - This book provides both an overview and a detailed discussion of key concepts. It covers methods for cleaning data collected from various sources, transforming it, selecting and extracting features, conducting exploratory data analysis, reducing dimensionality, identifying natural clusters, modeling patterns, comparing data between groups, and presenting results

💡 A Quick Selection of GitHub Repositories for Beginners and Beyond

SQL Roadmap for Data Science & Data Analytics - a step-by-step program for learning SQL. This GitHub repository is supplemented with links to learning materials, making it a great resource for mastering SQL

kh-sql-projects - collection of source codes for popular SQL projects catering to developers of all levels, from beginners to advanced. The repository includes PostgreSQL-based projects for systems like library management, student records, hospitals, booking, and inventory. Perfect for hands-on SQL practice!

ds-cheatsheet - repository packed with handy cheat sheets for learning and working in the Data Science field. An excellent resource for quick reference and study

GenAI Showcase - repository showcasing the use of MongoDB in generative artificial intelligence. It includes examples of integrating MongoDB with Retrieval-Augmented Generation (RAG) techniques and various AI models

💡😎 A Small Selection of Big, Fascinating, and Useful Datasets

Sky-T1-data-17k — diverse dataset designed to train the Sky-T1-32B model, which powers the reasoning capabilities of MiniMax-Text-01. This model consistently outperforms GPT-4o and Gemini-2 in benchmarks involving long-context tasks

XMIDI Dataset — large-scale music dataset with precise emotion and genre labels. It contains 108,023 MIDI files, making it the largest known dataset of its kind—ideal for research in music and emotion recognition

AceMath-Data - family of datasets used by NVIDIA to train their flagship model, AceMath-72B-Instruct. This model significantly outperforms GPT-4o and Claude-3.5 Sonnet in solving mathematical problems

🤔💡 How Spotify Built a Scalable Annotation Platform: Insights and Results

Spotify recently shared their case study, How We Generated Millions of Content Annotations, detailing how they scaled their annotation process to support ML and GenAI model development. These improvements enabled the processing of millions of tracks and podcasts, accelerating model creation and updates.

Key Steps:
1️⃣ Scaling Human Expertise:
✅ Core teams: annotators (primary reviewers), quality analysts (resolve complex cases), project managers (team training and liaison with engineers).
✅ Automation: Introduced an LLM-based system to assist annotators, significantly reducing costs and effort.

2️⃣ New Annotation Tools:
✅ Designed interfaces for complex tasks (e.g., annotating audio/video segments or texts).
✅ Developed metrics to monitor progress: task completion, data volume, and annotator productivity.
✅ Implemented a "consistency" metric to automatically flag contentious cases for expert review.

3️⃣ Integration with ML Infrastructure:
✅ Built a flexible architecture to accommodate various tools.
✅ Added CLI and UI for rapid project deployment.
✅ Integrated annotations directly into production ML pipelines.

😎 Results:
✅ Annotation volume increased 10x.
✅ Annotator productivity improved 3x.
✅ Reduced time-to-market for new models.

Spotify's scalable and efficient approach demonstrates how human expertise, automation, and robust infrastructure can transform annotation workflows for large-scale AI projects. 🚀

😱 Data Errors That Led to Global Disasters

✅ Demolishing the Wrong Houses – Due to inaccurate geoinformation system data, demolition crews were sent to incorrect addresses because of Google Maps errors. This led to homes being destroyed, causing tens of thousands of dollars in damages and legal battles for companies.

✅ Zoll Medical Defibrillators – Data quality issues during manufacturing caused Zoll Medical defibrillators to display error messages or completely fail during use. The company had to issue a Category 1 recall (the most severe, with a high risk of serious injury or death), costing $5.4 million in fines and damaging trust.

✅ UK Passport Agency Failures – Errors in data migration during system updates caused severe passport issuance delays, leading to public outcry and a backlog of applications. Fixing the issue and hiring extra staff once cost the agency £12.6 million.

✅ Mars Climate Orbiter Disaster – The $327.6 million NASA probe burned up in Mars' atmosphere due to a unit conversion error—one engineering team used metric measurements, while another used the imperial system.

✅ Knight Capital Stock Trading Error – A software bug caused Knight Capital to accidentally purchase 150 different stocks worth $7 billion in one hour. The firm lost $440 million and went bankrupt.

✅ AWS Outage at Amazon – A typo in a server management command accidentally deleted more servers than intended, causing a 4-hour outage. Companies relying on AWS suffered $150 million in losses due to downtime.

✅ Spanish Submarine "Isaac Peral" (S-81) – A decimal point miscalculation led to the submarine being 75–100 tons too heavy to float. A complete redesign caused significant delays and cost over €2 billion.

✅ Boeing 737 Max Crashes – In 2018 and 2019, two Boeing 737 Max crashes killed 349 people. The aircraft relied on data from a single angle-of-attack sensor, which triggered an automatic system that overrode pilot control. The disaster grounded the entire 737 Max fleet, costing Boeing $18 billion.

✅ Lehman Brothers Collapse – Poor data quality and weak risk analysis led Lehman Brothers to take on more risk than they could handle. The hidden true value of assets contributed to their $691 billion bankruptcy, triggering a global financial crisis.

💡Moral of the story: Data errors aren’t just small mistakes—they can cost billions, ruin companies, and even put lives at risk. Always verify, validate, and double-check!

🌎TOP DS-events all over the world in February

Feb 4-6 - AI Everything Global – Dubaï, UAE - https://aieverythingglobal.com/home
Feb 5 - Open Day at DSTI – Paris, France - https://dsti.school/open-day-at-dsti-5-february-2025/
Feb 5-6 - The AI & Big Data Expo – London, UK - https://www.ai-expo.net/global/
Feb 6-7 - International Conference on Data Analytics and Business – New York, USA - https://sciencenet.co/event/index.php?id=2703381&source=aca
Feb 11 - AI Summit West - San Jose, USA - https://ai-summit-west.re-work.co/
Feb 12-13 - CDAO UK – London, UK - https://cdao-uk.coriniumintelligence.com/
Feb 13-14 - 6th National Big Data Health Science Conference – Columbia, USA - https://www.sc-bdhs-conference.org/
Feb 13-15 - WAICF - WOrld AICAnnes Festival - Cannes, France - https://www.worldaicannes.com/
Feb 18 - adesso Data Day - Frankfurt / Main, Germany - https://www.adesso.de/de/news/veranstaltungen/adesso-data-day/programm.jsp
Feb 18-19 - Power BI Summit – Online - https://events.m365-summits.de/PowerBISummit2025-1819022025#/
Feb 18-20 - 4th IFC Workshop on Data Science in Central Banking – Rome, Italy - https://www.bis.org/ifc/events/250218_ifc.htm
Feb 19-20 - Data Science Day - Munich, Germany - https://wan-ifra.org/events/data-science-day-2025/
Feb 21 - ICBDIE 2025 – Suzhou, China - https://www.icbdie.org/submission
Feb 25 - Customerdata trends 2025 – Online - https://www.digitalkonferenz.net/
Feb 26-27 - ICET-25 - Chongqing, China - https://itar.in/conf/index.php?id=2703680