Big Data Science

13 Dec 2024 16:59

🥲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.

Big Data Science

09 Dec 2024 16:59

🧐Data and its markup in 2024: emerging trends and future requirements

Caught an interesting bakingai/data-labeling-in-2023-emerging-trends-and-future-demands-for-impactful-results-337c130c5c02">article about data markup. Here are a few key points:

🤔 Current trends:

✅ Increasing complexity of datasets
✅ The move to real-time partitioning
✅ Large-scale development of automated tools to complement manual labor

🤔Market forecasts:

✅Expected to grow to $8.22 billion by 2028 at a CAGR of 26.6%
✅The requirements for quality and speed of markup are increasing and will grow exponentially

🤔Technological trends:

✅Adaptive AI.
✅Metauniverse
✅Industry cloud platforms
✅ Improvements in wireless technologies

Thus, the author indicates that the data partitioning industry will grow rapidly due to the increasing demand for accurate and reliable data for AI and machine learning. Automation, adaptive AI, and new technological solutions will improve the quality and speed of data partitioning.

Big Data Science

30 Nov 2024 16:59

🌎TOP DS-events all over the world in December

Dec 2-5 - TIES 2024 - Adelaide, Australia - https://www.isi-next.org/conferences/ties2024/
Dec 3 - Generation AI - Paris, France - https://dev.events/conferences/generation-ai-c4odjomu
Dec 5 - The International AI Summit 2024 - Brussels, Belgium - https://global-aiconference.com/
Dec 2-6 - Data Science Week 2024 - Fort Wayne, USA - https://sites.google.com/view/data-science-week-2024
Dec 2-6 - AWS re:Invent - LAS VEGAS, USA - https://reinvent.awsevents.com/
Dec 9-10 - ICMSCS 2024: 18 - London, United Kingdom - https://waset.org/mathematics-statistics-and-computational-sciences-conference-in-december-2024-in-london
Dec 10 - Global Big Data Conference - Online - https://www.globalbigdataconference.com/
Dec 10 - Prompt Engineering Bulgaria 2024 - Sofia, Bulgaria - https://www.eventbrite.nl/e/prompt-engineering-bulgaria-2024-tickets-796563251127?aff=oddtdtcreator
Dec 11 - AI Heroes - Torino, Italy - https://dev.events/conferences/ai-heroes-xxrqdxu9
Dec 11-12 - The AI Summit New York - New York, USA - https://newyork.theaisummit.com/
Dec 12-13 - AI: 2057 - Dubai, UAE - https://www.globalaishow.com/
Dec 15-18 - IEEE International Conference on Big Data 2024 - Washington, D.C., USA - https://www3.cs.stonybrook.edu/~ieeebigdata2024/
Dec 19 - Normandie.ai 2024 - Rouen, France - https://dev.events/conferences/normandie-ai-2024-e15asbe6

Big Data Science

27 Nov 2024 16:59

🤖Deus in Machina: Jesus-AI has been installed in a Swiss church

St. Peter's Chapel in Lucerne has launched an AI Jesus project that communicates in 100 languages. The AI is installed in the confessional where visitors can ask questions and receive answers in real time.

Trained on theological texts, Jesus-AI engaged more than 1,000 people in two months, two-thirds of whom described the experience as “spiritual.” However, the experiment has drawn criticism for the superficiality of the answers and the inability to have meaningful conversations with the machine.


🖥Read more here

Big Data Science

22 Nov 2024 16:59

😎💡AlphaQubit from Google: a new standard for accuracy in quantum computing.

Google DeepMind and Google Quantum AI have unveiled AlphaQubit, a decoder that dramatically improves error correction accuracy in quantum computing. Based on a neural network trained on synthetic and real data from the Sycamore processor, AlphaQubit uses the Transformers architecture to analyze errors.

Tests have shown that AlphaQubit reduces errors by 6% compared to tensor networks and 30% with correlation matching. However, despite the high level of accuracy, real-world speed and scalability issues remain.

✅Link to blog

Big Data Science

15 Nov 2024 16:59

🧐Anthropic CEO Dario Amodei interviews Lex Fridman

😎Highlights:

✅Dario expressed optimism about the imminent emergence of AI capable of reaching human levels. He noted that development and training costs will increase in the coming years, and by 2027, clusters will likely be built worth around $100 billion - significantly larger than the current largest supercomputers, which cost around $1 billion.

✅Amodei believes that models will continue to scale, despite the lack of a theoretical explanation for this process - there is, according to him, some "magic" in it.

✅AI models are currently improving at an astonishing rate, especially in areas such as programming, physics, and mathematics. On the SWE-bench test, their success at the beginning of the year was only 2-3%, and now reaches about 50%. The main concern in these conditions is the possible monopoly on AI, when control over it ends up in a small number of large companies, which could threaten

🖥You can watch the interview here

Big Data Science

08 Nov 2024 16:59

😂A Radical Solution from AI

Every day, thousands of programmers can breathe a sigh of relief when AI performs tasks for them like queries, data formatting, or other routine tasks😁

🖥ChatGPT was asked to write SQL queries for a store database. The answer just killed

😎Sometimes AI's views on solving a particular problem are slightly different from human ones

Big Data Science

04 Nov 2024 16:59

💡A small selection of useful things for working with Big Data

postgres-backup-local is a Docker tool for creating backups of PostgreSQL databases, storing them in the local file system with the ability to flexibly manage copies. With its help, you can back up multiple databases from one server by specifying their names through the POSTGRES_DB environment variable (separated by a comma or space).
The tool supports webhooks before and after backup, automatically manages the rotation and deletion of old copies, and is also available for Linux architectures, including amd64, arm64, arm/v7, s390x, and ppc64le.

EfCore.SchemaCompare is a tool for comparing database schemas in Entity Framework Core (EF Core), allowing you to find and analyze differences between the current database and migrations. It provides a convenient way to track changes in data structures, which helps prevent errors caused by schema mismatches during application development.
Suitable for database versioning, especially useful when developing and upgrading EF Core-based applications.

Greenmask is an open-source tool for PostgreSQL designed for masking, obfuscation, and logical backup of data. It allows you to anonymize sensitive information in database dumps, making it useful for preparing data for use in non-production environments such as development and testing. Greenmask support helps protect data by meeting privacy requirements and reducing the risk of leaks during development.

Big Data Science

25 Oct 2024 17:59

😎Nvidia have published a new dataset for training faintune models

HelpSteer2 is an English-language dataset developed by NVIDIA and hosted on the Hugging Face platform. It includes 21,362 rows and is designed to train reward models that help improve the utility, factual accuracy, and coherence of answers generated by large language models (LLMs).

Each row in the dataset contains a query, a response, and five human annotated response attributes:
✅Utility (usefulness)
✅ Correctness
✅ Coherence
✅ Complexity
✅ Verbosity

The dataset can be used to fine-tune LLMs to generate more relevant and better responses to user queries.

Big Data Science

21 Oct 2024 17:59

🔥A small selection of data annotation tools with all the details

CVAT (Computer Vision Annotation Tool) is one of the most popular and sought-after image annotation tools used to create datasets in the field of computer vision.

Advantages of CVAT:
✅Customization: CVAT, as an open-source solution, gives users complete freedom to customize the platform to their needs. This makes the tool flexible and adaptable, allowing it to be integrated into various workflows. The CVAT documentation provides detailed instructions on customization, making the setup process more accessible even for beginners.

✅Detailed documentation: CVAT documentation includes detailed descriptions of functionality, use cases, life hacks, and images. Regular documentation updates ensure that users are always aware of the latest changes and improvements.

Disadvantages of CVAT:
✅High resource requirements: One of the main disadvantages of CVAT is its high server resource requirements, which can be a problem for some teams.

Supervisely is a multi-functional platform for working with computer vision projects, offering solutions for the entire lifecycle of AI projects, from data labeling to model training and deployment.

Advantages:
✅A rich ecosystem of applications: Supervisely Apps already offers many ready-made widgets that allow you to extend the functionality of any part of the platform. Each of them is open source and available on GitHub, which makes it possible not only to modify existing applications but also to create new ones.

Disadvantages:
✅High cost: Despite its extensive capabilities, Supervisely may be a less profitable choice financially compared to other tools.

Label Studio is a powerful and flexible open-source tool for data annotation in various machine learning tasks, including computer vision, text, and audio processing. It is used to label data for subsequent training of models.

Advantages:
✅Flexibility: Users can create labels themselves using code, which opens up new possibilities for customization.
✅Extensibility: The modular structure allows for easy addition of new features and integration of additional label types.

Disadvantages:
✅High resource requirements: Label Studio may require a significant amount of resources to fully use, which makes it less convenient for users with disabilities.
✅Limitations in Bounding Boxes labeling: While, for example, CVAT offers a more convenient and fast tool for Bounding Boxes labeling, Label Studio is better suited for labeling audio data.

Big Data Science

14 Oct 2024 17:59

😎 Optimizing Analytics with Oracle

Oracle posted an article on their blog where they talk about how to connect to a BDS cluster using Hive and Spark connections from Oracle Analytics Cloud (OAC).

Oracle Big Data Service clusters contain a Hadoop Distributed File System (HDFS) and a Hive database that load and transform data from different sources and in different formats (structured, semi-structured, and unstructured).

Learn how to connect Oracle Analytics Cloud to Oracle Big Data Service using Hive and Spark to improve data analytics. Combining powerful tools can help you efficiently process and visualize large amounts of data.

Big Data Science

09 Oct 2024 17:59

⚡️HTTP SQLite StarbaseDB

StarbaseDB is a powerful and scalable open source database that is based on SQLite and runs over the HTTP protocol. This database is built to run in a cloud environment (e.g. on Cloudflare), allowing it to scale efficiently down to zero based on load. Key benefits of StarbaseDB include:

✅Ease of use: Provides the ability to work through HTTP requests, making it easy to integrate with various systems and services.
✅Scalability: Automatically adjusts to load volume with the ability to scale both ways.
✅Support for SQLite: Utilize the time-tested and lightweight SQLite database for data storage.
✅Open Source: Open source, allowing developers to customize and improve the system to suit their needs.

It is suitable for developers who are looking for a simple and reliable way to organize databases with minimal customization and high availability in cloud platforms such as Cloudflare.

Big Data Science

02 Oct 2024 17:59

💡😎3 unpopular but very necessary visualization libraries

Supertree is a Python library designed for interactive and convenient visualization of decision trees in Jupyter Notebooks, Jupyter Lab, Google Colab and other notebooks that support HTML rendering. With this tool, you can not only visualize decision trees, but also interact with them directly in the notebook.

Mycelium is a library for creating graphical visualizations of machine learning models or any other directed acyclic graphs. It also provides the ability to use the Talaria graph viewer to visualize and optimize models

TensorHue is a Python library designed to visualize tensors directly in the console, making it easier to analyze and debug them, making the process of working with tensors more visual and understandable.

Big Data Science

27 Sep 2024 17:59

😎💡🔥A selection of unpopular but very useful Python libraries for working with data

Bottleneck is a library that speeds up NumPy methods up to 25 times, especially when processing arrays containing NaN values. It optimizes calculations such as finding minima, maxima, medians, and other aggregate functions. By using specialized algorithms and handling missing data, Bottleneck significantly speeds up work with large data sets, making it more efficient than standard NumPy methods.

Nbcommands is a tool that simplifies code search in Jupyter notebooks, eliminating the need for users to search manually. It allows you to find and manage code by keywords, functions, or other elements, which significantly speeds up working with large projects in Jupyter and helps users navigate their notes and code blocks more efficiently.

SciencePlots is a style library for matplotlib that allows you to create professional graphs for presentations, research papers, and other scientific publications. It offers a set of predefined styles that meet the requirements for data visualization in scientific papers, making graphs more readable and aesthetically pleasing. SciencePlots makes it easy to create high-quality graphs that meet the standards of academic publications and presentations.

Aquarel is a library that adds additional styles to visualizations in matplotlib. It allows you to improve the appearance of graphs, making them more attractive and professional. Aquarel simplifies the creation of custom styles, helping users create graphs with more interesting designs without having to manually configure all the visualization parameters.

Modelstore is a library for managing and tracking machine learning models. It helps organize, save, and version models, as well as track their lifecycle. With Modelstore, users can easily save models to various storages (S3, GCP, Azure, and others), manage their updates and restore. This makes it easier to deploy and monitor models in production environments, making working with models more convenient and controllable.

CleverCSV is a library that improves the process of parsing CSV files and helps avoid errors when reading them with Pandas. It automatically detects the correct delimiters and format of CSV files, which is especially useful when working with files that have non-standard or heterogeneous structures. CleverCSV simplifies working with data by eliminating errors associated with incorrect recognition of delimiters and other file format parameters.

Big Data Science

23 Sep 2024 17:59

📊Quick Tips for Handling Large Datasets in Google's Pandas

Pandas is a great tool for working with small datasets, typically between two and three gigabytes in size.

For datasets larger than this threshold, using Pandas is not recommended. This is because if the dataset size exceeds the available RAM, Pandas loads the entire dataset into memory before processing. Memory issues can arise even with smaller datasets, as preprocessing and rewriting create duplicate DataFrames.

⚠️Here are some tips for efficient data processing in Pandas:

✅ Use efficient data types: Use more memory-efficient data types (e.g. int32 instead of int64, float32 instead of float64) to reduce memory usage.
✅ Load less data: Use the use-cols parameter to load only the columns you need, reducing memory consumption.pd.read_csv()
✅ Chunking: Use the chunksize parameter in to read the dataset in smaller chunks, processing each chunk iteratively.pd.read_csv()
✅ Optimize Pandas dtypes: Use the astype method to convert columns to more memory-efficient types after loading the data, if appropriate.
✅ Parallelize Pandas with Dask: Use Dask, a parallel computing library, to scale Pandas workflows to larger-than-memory datasets by leveraging parallel processing.

🖥Learn more here

Big Data Science

11 Dec 2024 16:58

😎Google unveiled Willow - a quantum chip with exponential scaling

Google has released Willow, the world's first quantum chip capable of exponential error reduction with increasing number of qubits. This is made possible by the efficient implementation of logical qubits that operate below the boundary of Quantum Error Correction, a method of protecting data through its distribution across qubits.

Willow features:

✅Record number of qubits: 105, far exceeding previous quantum computers.

✅Calculation speed: a septillion times faster than classical chips. Willow solves problems in 300 seconds that a conventional chip would take 10 quintillion years to complete.

✅ Error minimization: as the number of qubits increases, errors decrease exponentially, solving a major problem in quantum computing over the past 30 years.

While tasks like cracking bitcoin will require 300-400 million qubits, Willow is already setting a new bar in quantum technology.

🔎 Learn more here

Big Data Science

06 Dec 2024 16:59

😎🔥A selection of tools for Big Data processing

Timeplus Proton is a ClickHouse-based SQL engine designed to process, route, and analyze streaming data from sources such as Apache Kafka and Redpanda, with the ability to transfer aggregated data to other systems.

qsv is a command-line utility designed for quickly indexing, processing, analyzing, filtering, sorting, and merging CSV files. It offers convenient and understandable commands for performing these operations.

WrenAI is an open-source tool that prepares an existing database for working with RAG (Retrieval-Augmented Generation). It allows you to transform text queries into SQL, explore data from the database without writing SQL code, and perform other tasks.

Groll is an open-source CLI utility for managing schema migrations in PostgreSQL. It provides safe and reversible changes, supporting multiple schema versions at the same time. Groll supports complex migrations, ensuring that client applications do not stop working while updating the database schema.

Valkey is a high-performance open-source data warehouse that supports caching, message queues, and can be used as a primary database. It operates as a standalone background service or as part of a cluster, providing replication and high availability.

DataEase is an open-source BI tool for creating interactive visualizations and analyzing business metrics. It simplifies access to analytics with an intuitive drag-and-drop interface, making working with data convenient and understandable.

SurrealDB is a modern multi-model database that combines SQL, NoSQL, and graph databases. It supports relational, document, graph, temporal, and key-value data models, providing a unified solution for managing data without the need for different platforms.

LibSQL is a fork of SQLite, extended with features such as HTTP and gRPC query processing, and transparent replication support. It allows you to create distributed databases with writes on the primary server and reads from replicas. LibSQL provides secure data transfer via TLS and provides a Docker image for easy deployment.

Redash is an open-source data analytics tool designed to simplify connecting, querying, and visualizing data from a variety of sources. It allows you to create SQL and NoSQL queries, visualize results in the form of graphs and charts, and share dashboards with teams.

Big Data Science

29 Nov 2024 16:59

💡 SmolTalk: a synthetic English-language dataset for LLM education

SmolTalk is a synthetic dataset from HuggingFace designed for teacher-led LLM learning. It consists of 2 million rows and was used to develop SmolLM2-Instruct models.

🔥Dataset includes both new and existing datasets

😎New datasets:

✅Smol-Magpie-Ultra (400k rows).
✅Smol-constraints (36,000 rows)
✅Smol-rewrite (50 thousand lines)
✅Smol-summarize (101 thousand lines)

⚡️Older datasets:

✅OpenHermes2.5 (100 thousand lines)
✅MetaMathQA (50 thousand lines)
✅NuminaMath-CoT (1120 thousand lines)
✅Self-Oss-Starcoder2-Instruct (1120 thousand lines)
✅SystemChats2.0 (30 thou. lines)
✅LongAlign (less than 16 thousand tokens)
✅Everyday-conversations (50 thousand lines)
✅APIGen-Function-Calling (80k lines)
✅Explore-Instruct-Rewriting (30k lines)

📚Training results:
SmolTalk showed significant improvements in model performance, especially in the tasks of math, programming, and following system prompts. SmolTalk training gave better results on IFEval, BBH, GS8Mk and MATH labels, including when training Mistral-7B.

Big Data Science

25 Nov 2024 16:59

🤔CUPED: advantages and disadvantages

CUPED (Controlled Pre-Experiment Data) is a data preprocessing technique used to improve the accuracy of A/B test evaluation. CUPED reduces the variance of metrics by utilizing data collected before the experiment, allowing statistically significant differences to be identified more quickly.

Benefits of CUPED:

✅Reduces variance of metrics: Improves test sensitivity by accounting for prior data.
Resource savings: Reduces the sample size required to achieve statistical significance.
✅Faster interpretation of results: Reducing noise allows real effects to be found more quickly.
✅Accounting for seasonality: Using data before the experiment helps account for trends and external factors.

Disadvantages of CUPED:

✅Implementation complexity: Requires knowledge of statistics and proper choice of covariates.
✅Dependence on data quality: Pre-experimental data must be reliable and representative.
✅Necessity of covariates: A significant correlation between metric and predictor is required, otherwise the effect will be minimized.
✅Risk of overestimation: If not properly adjusted, may lead to overestimation of the effect.

Thus, CUPED is particularly useful when it is important to maximize the efficiency of experiments but requires careful data preparation and analysis.

Big Data Science

20 Nov 2024 16:59

🔎 Optimizing search in MongoDB

MongoDB is a non-relational database that differs from SQL databases such as PostgreSQL or MySQL in its structure. Instead of tables with columns and rows, MongoDB uses collections.

Searching for text in MongoDB involves using special query operators to work with text data. It allows you to search for text phrases in collections and return documents containing the specified words. This is often used for complex operations where data is grouped by common attributes such as price, authors, or age.

In this article, the author also shares his experience with MongoDB, including the challenges in creating optimal search queries to make them easier to understand for beginners.

The article also mentions Mongoose, a popular ORM (object-relational mapping) tool that simplifies the interaction between MongoDB and programming languages such as Node.js/JavaScript. It provides functions for data modeling, schema development, model authentication, and data management.

Big Data Science

13 Nov 2024 16:59

😎The Power of Data: Analyzing Quarterly Revenue Growth for Business Success

💡I recently came across an article in which the author talks about analyzing quarterly revenue growth. He argues that focusing only on annual data can hide trends and slow down decision making. Quarterly analysis allows you to better understand the current performance of the business and identify potential problems, such as a decrease in revenue in a certain period. This granularity helps you identify causes (such as seasonal fluctuations or marketing shortcomings) faster and take action faster than when analyzing only annual data. Quarterly data creates a foundation for optimizing growth strategies, moving from reactive to more effective data-driven management.

The author also highlights key metrics for analyzing quarterly revenue growth:

✅Customer Acquisition Cost (CAC): It is important to understand the cost of acquiring new customers to optimize marketing and sales efforts, which helps increase ROI and revenue growth.
✅Customer Lifetime Value (CLTV): This metric shows the total revenue a customer brings in over their entire relationship with the company, helping to identify high-yield segments for targeting and retention.
✅Sales Conversion: Analyzing conversion at each stage of the funnel helps identify bottlenecks and improve overall sales efficiency, which contributes to revenue growth.

🖥ccdallas/the-power-of-data-analyzing-quarterly-revenue-growth-for-business-success-173fc7dcc2ab">Link to the article

Big Data Science

06 Nov 2024 16:59

😎How Spotify accelerated data markup for ML by 10x

Spotify shared how it accelerated data markup for machine learning models using large language models (LLMs) in conjunction with the work of annotators. Automated initial LLM partitioning significantly reduced processing time by allowing annotators to focus on complex or ambiguous cases. This combined solution tripled process throughput and reduced costs. This scalable solution is especially relevant for a rapidly growing platform and is used to monitor compliance with service rules and policies.

💡 Spotify's data partitioning strategy is based on three core principles:

✅Scaling human expertise: annotators validate and refine results to improve data accuracy.

✅Annotation tools: creating efficient tools that simplify the work of annotators and allow models to be integrated more quickly into the process.

✅Fundamental infrastructure and integration: the platform is designed to handle large amounts of data in parallel and run dozens of projects simultaneously.

This approach has allowed Spotify to run multiple projects simultaneously, reduce costs, and maintain high accuracy.
More information about Spotify's solution can be found in their whitepaper.

Big Data Science

31 Oct 2024 16:59

🌎TOP DS-events all over the world in November
Nov 4-8 - PASS Data Community Summit 2024 - Seattle, USA - https://passdatacommunitysummit.com/
Nov 6 - Enterprise AI & Big Data - London, UK - https://whitehallmedia.co.uk/bdanov2024/
Nov 6-8 - PyData NYC, New York, USA - https://pydata.org/nyc2024
Nov 7 - Data Science Day 2024 - https://events.altair.com/data-science-day-2024/
Nov 7 - Data & Analytics Congres 2024 - Liemes, Utrecht - https://datainsightsnetwork.nl/events/dac-2024/
Nov 14 - IMPACT: The Data Observability Summit - Online - https://impactdatasummit.com/
Nov 18-19 - Machine Learning Week Europe - Munich, Germany - https://machinelearningweek.eu/
Nov 18-22 - LEADING GLOBAL AI EVENT - Belgrade, Serbia - https://datasciconference.com/
Nov 18-22 - QCon - San Francisco, USA - https://qconsf.com/
Nov 20 - Tech & AI LIVE 2024 - New York, USA - https://live.technologymagazine.com/tech-ai-newyork-2024/
Nov 20-23 - FMLDS - Sydney, Australia - https://www.fmlds.org/
Nov 20-21 - Data & Analytics Insight Summit - San Diego, USA - https://gdsgroup.com/events/physical-summit/data-analytics-na-nov-24/
Nov 21 - Data Science Summit - Warsaw, Polland - https://dssconf.pl/
Nov 28-29 - AI ML, Data Science & Robotics Conferences 2024 - Porto, Portugal - https://aiml.events/events/ai-ml-data-science-robotics-conferences-2024

Big Data Science

23 Oct 2024 17:06

💡🔥Working with geographic data efficiently

GeoPy is a Python library that allows you to work with geographic data and provides tools for performing tasks such as geocoding (converting addresses to coordinates), reverse geocoding (converting coordinates to addresses), and calculating distances between geographic points.

😎Main features of working with geodata via GeoPy:

✅Geocoding: Converts addresses or places into geographic coordinates (latitude and longitude). This is useful when you need to, for example, visualize data on a map.

✅Reverse geocoding: Converts coordinates into a human-readable address. This can be useful for creating more understandable data or interfaces.

✅Reverse geocoding: Converts coordinates into a human-readable address. This can be useful for creating more understandable data or interfaces.

🖥You can learn more about geographic data analysis from sercanyesiloz98/handling-location-features-effectively-with-geopy-2194988834de">this article

Big Data Science

16 Oct 2024 17:59

😎Top Python libraries for optimizing work with data

✅Pony ORM is a convenient and powerful library for working with object-relational databases, which allows you to write SQL queries using Python syntax. It automatically converts Python code into SQL queries, which simplifies interaction with databases, making it more intuitive and concise. Pony ORM supports major DBMSs such as PostgreSQL, MySQL, SQLite and others, providing flexibility and convenience when creating queries and working with data models.

✅Pypika is a library for creating SQL queries programmatically in Python, which allows you to avoid errors in hand-writing SQL code and protects against SQL injections. It is especially useful for building dynamic and parameterized queries, making it an ideal tool for database applications. Pypika allows you to build queries with a high degree of detail and complexity, while maintaining the readability and security of your code.

✅EdgeDB is a modern database and client library for Python that simplifies managing data schemas and writing queries. It offers a more intuitive and convenient approach compared to traditional SQL databases, providing advanced capabilities for working with data. Key features of EdgeDB include automatic schema generation, working with relational data without the need to write complex SQL queries, as well as support for type safety and a more expressive syntax for manipulating data.

✅Tortoise ORM is a modern asynchronous ORM (Object-Relational Mapping) designed for working with databases in asynchronous Python applications. It supports various relational databases such as PostgreSQL, MySQL, SQLite, and is written with an emphasis on simplicity and ease of use. Tortoise ORM allows you to build complex SQL queries using Python code, automatically synchronizing data models with the database. Support for asynchrony makes it especially useful in high-load or web applications where it is important to efficiently manage resources and database queries.

✅Polars is a high-performance data processing and analysis library in Python and Rust, focused on working with large volumes of data. Thanks to multithreading and an optimized architecture, Polars provides significantly higher execution speeds compared to traditional tools such as Pandas. The library supports a wide range of operations on tabular data (dataframes), offering an intuitive interface for filtering, aggregating and transforming data. It is ideal for tasks that require high performance, especially when working with large data sets.

Big Data Science

11 Oct 2024 17:59

💡 News of the day: MongoDB creates AI partner ecosystem

MongoDB is actively adapting to the challenges of artificial intelligence development by introducing an improved version of its database (8.0) and launching the MongoDB AI Application Program (MAAP). This program aims to create a global partner ecosystem aimed at standardizing AI solutions. Key partners include major cloud and consulting players such as Microsoft Azure, Google Cloud Platform, Amazon Web Services, Accenture, and AI companies Anthropic and Fireworks AI.

Updates to MongoDB 8.0 promise notable performance improvements:

✅ A 32% increase in throughput.
✅Acceleration of batch writes by 56%.
✅ Increase parallel write speed by 20%.

This gives MongoDB the ability to better handle the high loads often encountered with big data and AI. Solutions have already been deployed for large companies, including one of France's leading automakers and a global home appliance manufacturer.

In this way, MongoDB, by building MAAP and improving its technology, aims to become a key player in the AI industry, supporting developers and companies in their quest for innovation.

🔎Read more here

Big Data Science

04 Oct 2024 17:59

😎⚡️A powerful dataset generated using Claude Opus.

Synthia-v1.5-I is a dataset of over 20,000 technical questions and answers designed to train large language models (LLM). It includes system prompts styled like Orca to encourage the generation of diverse answers. This dataset can be used to train models to answer technical questions more accurately and comprehensively, improving their performance on a variety of technical and engineering problems.

✅To load the dataset using Python:

from datasets import load_dataset
ds = load_dataset("migtissera/Synthia-v1.5-I")

Big Data Science

30 Sep 2024 17:59

🌎TOP DS-events all over the world in October
Oct 1-2 - AI and Big Data Expo Europe - Amsterdam, Netherlands - https://www.ai-expo.net/europe/
Oct 7-10 - Coalesce - Las Vegas, USA - https://coalesce.getdbt.com/
Oct 9-10 - World Summit AI - Amsterdam, Netherlands - https://worldsummit.ai/
Oct 9-10 - Big Data & AI World - Singapore, Singapore - https://www.bigdataworldasia.com/
Oct 10-11 - COLLIDE 2024: The South's largest data & AI conference - Atlanta, USA - https://datasciconnect.com/events/collide/
Oct 14-17 - Data, AI & Analytics Conference Europe 2024 - London, UK - https://irmuk.co.uk/data-ai-conference-europe-2024/
Oct 16-17 - Spatial Data Science Conference 2024 - New York, USA - https://spatial-data-science-conference.com/2024/newyork
Oct 19 - Oktoberfest - London, UK - https://datasciencefestival.com/event/oktoberfest-2024/
Oct 19 - INFORMS Workshop on Data Science 2024 - Seattle, Washington, USA - https://sites.google.com/view/data-science-2024
Oct 20-25 - TDWI Transform - Orlando, USA - https://tdwi.org/events/conferences/orlando/information/sell-your-boss.aspx
Oct 21-25 - SIAM Conference on Mathematics of Data Science (MDS24) - Atlanta, USA - https://www.siam.org/conferences-events/siam-conferences/mds24/
Oct 23-24 - NDSML Summit 2024 + AI2R Expo - Stockholm, Sweden - https://ndsmlsummit.com/
Oct 28-29 - Cyber Security Summit - San Paulo, Brazil - https://www.cybersecuritysummit.com.br/index.php
Oct 29-31 - ODSC West - California, United States - https://odsc.com/

Big Data Science

25 Sep 2024 17:59

🧐💡A Brief Introduction to MapReduce: Advantages and Disadvantages

MapReduce is a programming model and associated framework for processing large data sets in parallel on distributed computing systems. It includes two main phases: Map (projection) and Reduce (reduction).

Advantages of MapReduce:

✅Scalability: MapReduce easily scales to thousands of machines, allowing it to process huge amounts of data

✅Parallelism: MapReduce automatically distributes tasks across available nodes, executing them in parallel, reducing computational time

✅Fault tolerance: Built-in fault tolerance allows tasks to be restarted in the event of node failure, ensuring completion without data loss

Disadvantages of MapReduce:

✅High I/O Cost: One of the key disadvantages is that data is written and read from disk between the Map and Reduce stages, significantly reducing performance in tasks where fast data transfer is important

✅Lack of interactivity: MapReduce is designed for batch processing, making it inefficient for interactive queries or real-time analysis

✅Shuffle phase requirement: The shuffle phase is often resource intensive and time, making this process a bottleneck in MapReduce performance

✅Low performance for complex tasks: For complex algorithms that require many steps of communication between nodes (e.g. iterative tasks), MapReduce performance degrades

You can also learn more about MapReduce from here

Big Data Science

18 Sep 2024 17:59

💡Creating recommendations for applications with minimal complexity using vector databases

This data not only trains AI systems, but is also the final output that you continue to work with. That's why it's so important to use "good" data. No matter how powerful the model is, if the input is bad data, the output will be the same.

This article is about an example of using the Weaviate database in Streamlit format to simplify working with vector databases. The authors believe that this will allow you to create a powerful search and recommendation system taking into account technical and cost factors.

📚For information, it is worth noting that:
✅Weaviate is an open-source vector database that allows users to store data objects and vector data from machine learning models and easily scales to billions of data objects. .

✅Streamlit is a Python framework. It contains a set of software tools that allow you to transfer a machine learning model to a website. The written "smart" program with this framework can be quickly turned into web applications.