📝A little about ClickHouse: advantages and disadvantages
ClickHouse is an open source columnar database designed for processing analytical queries with large volumes of data.
Advantages of ClickHouse:
1. High performance: ClickHouse is optimized for running analytical queries on large volumes of data. It provides high query speed due to its columnar data structure and other optimizations.
2. Scalability: ClickHouse easily scales horizontally, allowing you to add new cluster nodes to process a growing volume of data.
3. Efficient use of resources: Thanks to columnar layout and data compression, ClickHouse can efficiently use storage resources, which reduces disk space consumption.
4. Low read overhead: Thanks to its data structure and optimizations, ClickHouse provides high read performance.
Disadvantages of ClickHouse:
1. Limited transaction support: ClickHouse is focused on analytical queries and does not have full transaction support, which can be a disadvantage for applications that require strong data consistency.
2. Limited write support: ClickHouse is designed primarily for reading data, and write operations may be less efficient than other database management systems for large change volumes.
3. Insufficient indexing support: ClickHouse has limited indexing support compared to some other DBMSs, which can affect the performance of search operations.
4. Difficult to maintain and set up: Setting up ClickHouse may require some skill and understanding of its architecture, which may make it less attractive to less experienced administrators.
Overall, the choice of ClickHouse depends on the specific needs of the project. If your tasks involve analytics and processing large volumes of data, ClickHouse may be an excellent option. However, if highly consistent transactions and writes are required, other solutions may be worth considering.

😎🔎Selection of useful OLAP services for processing Big Data
Apache Druid is a real-time OLAP engine. It is focused on time series data, but can be used for any data. It uses its own columnar format that can highly compress data, and it has many built-in optimizations such as inverted indexes, text encoding, automatic data folding, and more.
Apache Pinot - Offers lower latency thanks to the Startree index, which does partial precomputation, so it can be used for user-facing applications (it was used to fetch LinkedIn feeds). This uses a sorted index instead of an inverted one, which is faster.
Apache Tajo - Designed to perform ad hoc queries with low latency and scalability, online aggregation and ETL for large data sets stored in HDFS and other data sources. It supports integration with Hive Metastore to access shared schemas.
Solr is a very fast open source enterprise search platform built on Apache Lucene. Solr is robust, scalable, and fault-tolerant, providing distributed indexing, replication and load-balanced queries, automatic failover and recovery, centralized configuration, and more.
Presto is an open source platform from Facebook. It is a distributed SQL query engine for running interactive analytical queries against data sources of any size. Presto lets you query data where it lives, including Hive, Cassandra, relational databases, and file systems. It can query large data sets in seconds. Presto is independent of Hadoop, but integrates with most of its tools, especially Hive, to run SQL queries.

🌎TOP DS-events all over the world in December
Dec 4-5 - ICDSTA 2023: 17 - Tokyo, Japan - https://waset.org/data-science-technologies-and-applications-conference-in-december-2023-in-tokyo
Dec 6-7 - The AI Summit New York - New York, USA - https://newyork.theaisummit.com/
Dec 6 - DSS NYC: Applying AI & ML to Finance & Technology - New York, USA - https://www.datascience.salon/newyork/
Dec 7-8 - ADSN 2023 Conference - University of Adelaide, Australia - https://www.australiandatascience.net/event/2023-adsn-conference/
Dec 8-10 - CDICS 2023 - Online - https://www.cdics.org/
Dec 11-15 - DSWS-2023 - Tokyo, Japan - https://ds.rois.ac.jp/article/dsws_2023
Dec 25-26 - ICVDA 2023: 17. International Conference on Vehicle Data Analytics - France, Paris - https://waset.org/vehicle-data-analytics-conference-in-december-2023-in-paris

🤔Grouparoo Review: Advantages and Disadvantages
Grouparoo is a data management tool that provides an automated process for collecting, processing and synchronizing data across different applications and data sources.
Benefits of Grouparoo:
1. Automate data synchronization processes: Grouparoo provides the ability to create rules for automatic data synchronization between different sources. This reduces manual labor and keeps data up to date in real time.
2. Flexibility and Customizability: The tool allows the user to customize synchronization rules to suit an organization's unique needs and data structure. Flexible customization makes Grouparoo a powerful tool for various business scenarios.
3. Improved data accuracy: An automated data synchronization process helps prevent errors associated with manual data entry and ensures greater data accuracy across multiple systems.
4. Integration with various data sources: Grouparoo provides support for integration with various applications and data sources, which allows you to manage data from various sources in a single format.
Disadvantages of Grouparoo:
1. Setup Difficulty: Grouparoo's setup process can sometimes be difficult, especially for users without technical experience. This may require time and effort to fully implement the tool.
2. Technical understanding required: Full use of Grouparoo requires an understanding of the technical aspects of data synchronization and rules configuration, which can be a challenge for users without relevant experience.
3. Dependency on Third Party Data Sources: Grouparoo depends on the availability and structure of data in third party applications. Problems with these sources can affect the performance of the tool.
Overall, Grouparoo is a powerful data management tool that can greatly simplify your data synchronization and processing processes. However, before use, it is important to carefully weigh the advantages and disadvantages, taking into account the specifics and needs of a particular organization.

💥💯💡A new open source library for working with data has appeared on the Internet
Cleanlab is a library that helps clean data and labels by automatically detecting problems in a machine learning dataset. To make machine learning easier on messy data, this data-centric II package uses additional models to evaluate problems in data sets that can be corrected to train even better models.
As a result, the AI library performs the following functions:
1. Detection of data problems (mislabeling, omissions, duplicates, drift)
2. Setting up and testing the training model.
3. Conduct active training of models

💥📝📊An archive of 32 datasets that you can use to practice your skills
Data Science Dojo has created an archive of 32 data sets that you can use to practice and improve your data science skills.
The repository provides a wide range of topics, complexity levels, dimensions, and attributes. The datasets are categorized according to different difficulty levels to suit different skill levels.
Datasets offer the opportunity to gain practical knowledge to improve your skills in areas such as exploratory data analysis, data visualization, data science, deep learning, and more.

📝🔎Kappa Big Data architecture: advantages and disadvantages
Kappa architecture is a coherent data processing model where all data is considered as a sequential stream of events.
K-architecture finds its application in scenarios where:
1. It is necessary to manage the queue of events and requests in a distributed file system
2. High availability and resilience are critical, since data processing occurs on every node in the system.
For example, Apache Kafka, as an efficient message broker, meets these requirements by providing a high-performance, reliable and scalable platform for data collection and aggregation. Thus, Kappa architecture built on top of Kafka is ideal for projects like LinkedIn, where large amounts of information need to be efficiently processed and stored to serve many simultaneous requests.
Advantages of Kappa architecture in Big Data:
1. Scalability: the architecture is easily scaled horizontally, which allows you to process large volumes of data. This is especially important with the increasing volume of information that many businesses face.
2. Low latency: Systems built on the Kappa architecture are capable of low latency in data processing. This is important for tasks that require a quick response to changes in data.
3. Easy updates: Since the data is processed in real time, making changes to the data processing becomes easier. This makes it easier to deploy new versions and system updates.
4. Support complex analytical tasks: Kappa architecture is suitable for complex analytical tasks such as real-time machine learning, anomaly analysis and others. It provides the ability to quickly respond to changes in data.
Disadvantages of Kappa architecture in Big Data:
1. Data duplication: One of the major disadvantages is data duplication. Because data first enters raw data storage and then goes through processing, this can lead to storage overuse.
2. Difficulty in managing data schemas: Since data enters the system in a raw format and is then transformed, managing data schemas can be a challenge, especially when there are changes in the data structure.
3. Resource Requirements: Real-time data processing can require significant computing resources. This can be a challenge for organizations with limited budgets.
Thus, the Kappa architecture makes a significant contribution to the development of the Big Data field by providing efficient data processing in real time. However, like any architecture, it has its advantages and disadvantages, which should be taken into account when choosing the appropriate solution for a particular project.

⚡️💡Free tool for visualizing user journey data
MyTracker is a multi-platform analytics and attribution system for mobile applications and websites. This service is also a tool for collecting and processing data on marketing activity and user actions in the application and on the website. MyTracker works for free, without restrictions on the volume and period of data storage. Main components of MyTracker:
1. SDK - software library for tracking mobile applications.
2. Web counter for tracking data on websites.
3. Web interface for creating a working environment, viewing and downloading analytical reports.

😎⚡️💥Top little-known but quite useful Python libraries for Big Data analysis
Pattern - designed for data extraction on the Internet, natural language processing, machine learning and social network analysis. Tools include a search engine, APIs for Google, Twitter and Wikipedia, and text analysis algorithms that can be executed in a few lines of code.
SciencePlots is a library that provides styles for the Matplotlib library to produce professional plots for presentations, research papers, etc.
Pgeocode is a Python geocoding module that is designed to process geographic data and helps to combine and correlate different data. Using the pgeocode module, you can obtain and provide information related to a region or area using postal code information. Distances between two postal codes are also supported.
pynimate - module for animating line graphs of statistical data

⚡️📝💡Platforms for marking data for computer vision tasks
VoTT is a free, open-source image annotation tool developed by Microsoft. It provides comprehensive support for creating datasets and validating video and image-based object detection models.
LabeIimg is a graphical image annotation tool for labeling objects using Bounding Boxes. It is written in Python. Labeled data is exported as XML files in PASCAL VOC format.
Labelme is an online data annotation tool created by MIT's Computer Science and Artificial Intelligence Laboratory. Labelme supports six different types of annotations: polygons, rectangles, circles, lines, dots and linear stripes.
DataLoop is a universal cloud-based annotation platform with built-in tools and automation for creating high-quality training datasets.
Supervise.ly is a web platform for annotating images and videos with your community. Researchers and large groups can annotate and experiment with datasets and neural networks.

📝💡🔎Selection of datasets for autopilots
Berkeley DeepDrive BDD100k - One of the largest datasets for autopilots. Includes more than 100 thousand videos with more than a thousand hours of driving recordings at different times of day and in different weather conditions
Baidu Apolloscapes - a dataset for recognizing 26 semantically different objects such as cars, buildings, pedestrians, bicycles, street lights, etc.
Comma.ai. - more than 7 hours of driving on the highway. The dataset contains information about car speed, GPS coordinates, acceleration, steering angle
Oxford’s Robotic Car - more than a hundred repetitions of one route around Oxford, filmed over the course of a year. The dataset contains different combinations of traffic, pedestrians, weather conditions, as well as road works
Cityscape Dataset - recordings of one hundred street scenes in fifty cities

🌎TOP DS-events all over the world in2024
Jan 9-12 - CES 2024 - LAS VEGAS, USA - https://www.ces.tech/
Jan 11-12 - ICSDS 2024: 18. International Conference on Statistics and Data Science - Zurich, Switzerland - https://waset.org/statistics-and-data-science-conference-in-january-2024-in-zurich?utm_source=conferenceindex&utm_medium=referral&utm_campaign=listing
Jan 15-16 - ICCDS 2024: 18. International Conference on Computational and Data Sciences - Montevideo, Uruguay - https://waset.org/computational-and-data-sciences-conference-in-january-2024-in-montevideo?utm_source=conferenceindex&utm_medium=referral&utm_campaign=listing
Jan 15-16 - ICCIDS 2024: 18. International Conference on Communication Informatics and Data Science - Rome, Italy - https://waset.org/communication-informatics-and-data-science-conference-in-january-2024-in-rome?utm_source=conferenceindex&utm_medium=referral&utm_campaign=listing
Jan 24 - Data Science Salon Seattle: Retail & ecommerce - Seattle, USA - https://www.datascience.salon/seattle/
Jan 25 - AI, Machine Learning & Data Science Meetup - Online - https://www.meetup.com/london-ai-machine-learning-data-science/events/297485409/
Jan 24-25 - The Festival of Genomics & Biodata - London, UK - https://festivalofgenomics.com/
Jan 29-Feb 2 - SUPERWEEK 2024 - https://superweek.hu/
Jan 31 - National Data Science PhD Meetup - Nyborg, Denmark - https://ddsa.dk/phd-meetup-2-0/
Feb 2-5 - ICBDM 2024 - Shenzhen, China - https://www.icbdm.org/
Feb 8-10 - World Artificial Intelligence Cannes Festival - Cannes, France - https://www.worldaicannes.com/en
April 24-25 - Data Innovation Summit - Stockholm, Sweden -https://datainnovationsummit.com/
May 23-24 - The Data Science Conference - Chicago, USA - https://www.thedatascienceconference.com/
June 17-19 - World Conference on Data Science & Statistics - Amsterdam, Netherlands - https://datascience.thepeopleevents.com/
July 9-11 - DATA 2024 – Conference - Dijon, France - https://data.scitevents.org/
31 July-1 Aug - Gartner Data Analytics Summit - Sydney, Australia - https://www.gartner.com/en/conferences/apac/data-analytics-australia

📉📊The world of data with Tableau: advantages and disadvantages
Tableau is an innovative data visualization software that has become an integral part of modern data analysis.
Advantages of Tableau:
Intuitive Interface: One of the key benefits of Tableau is its intuitive and easy to understand interface. Users can create complex visualizations without extensive programming knowledge.
Rich Visualization Options: Tableau provides a variety of options for data visualization, ranging from standard graphs to complex dashboards. This allows users to present data in the most visual form.
Integration with various data sources: Tableau supports a wide range of data sources, including databases, Excel files, cloud and many more. This provides convenience in working with data from various sources.
Dynamic Dashboards and Reports: With Tableau, users can create dynamic dashboards and reports that allow them to instantly track changes and analyze data in real time.
Extensive Community and Support: Tableau has an active user community, providing access to extensive resources, training, and forums for problem solving and sharing experiences.
Disadvantages of Tableau:
Need for Data Preparation: In some cases, pre-processing of data is required before it can be visualized in Tableau. This may require time and additional effort.
Limited analytics capabilities: Compared to some other data analytics tools, Tableau may be less capable of complex analytical calculations.
Limited real-time capabilities: In some scenarios, Tableau may face limitations in processing data in real-time, which may be an issue for certain business scenarios.
Overall, Tableau remains a powerful and popular data visualization tool, providing rich functionality for analyzing data and making informed decisions. The decision to use it depends on the specific needs and capabilities of the business.

📚A selection of books for immersion in the world of time series analysis
Time series analysis and forecasting - considered time series indicators, main types of trends and methods for their recognition, methods for estimating fluctuation parameters, measuring the stability of series levels and dynamic trends, modeling and time series forecasting. Designed for persons with knowledge of the general theory of statistics.
Practical analysis of time series: forecasting with statistics and machine learning - modern technologies for analyzing time series data are described here and examples of their practical use in a variety of subject areas are given. It is designed to help solve the most common problems in the study and processing of time series using traditional statistical methods and the most popular machine learning models.
Elementary theory of analysis and statistical modeling of time series - the book contains the theoretical and probabilistic foundations of the analysis of the simplest time series, as well as methods and techniques for their statistical modeling (simulation ). The material on elementary probability theory and mathematical statistics is presented briefly using the analogy of probabilistic schemes and supplemented with results on the theory of series and criteria of randomness.
Statistical analysis of time series - monograph by a famous American specialist in mathematical statistics contains a detailed presentation of the theory of statistical inference for various probabilistic models. Methods for representing time series, estimating the parameters of corresponding probabilistic models, and testing hypotheses regarding their structure are outlined. The extensive material collected by the author, previously scattered across various sources, makes the book a valuable guide and reference book.
Time series. Data processing and theory () - the monograph is devoted to the study of times series found in various fields of physics, mechanics, astronomy, technology, economics, biology, medicine. The main orientation of the book is practical: methods of theoretical analysis are illustrated with detailed examples, and the results are clearly presented in numerous graphs.

💡😎Databricks Lakehouse: advantages and disadvantages
Databricks Lakehouse is a concept that combines the functionality of a data lake and a data warehouse to provide more efficient data management.
Benefits of Databricks Lakehouse:
1. Single space for data storage: Lakehouse provides a single storage for data, combining the advantages of a data lake (flexibility, scalability) and a data warehouse (structured queries optimized for analytics).
2. Scalability: Databricks Lakehouse allows you to efficiently scale data storage and processing, supporting large volumes of information.
3. Support for structured and unstructured data: Lakehouse provides the ability to store and process both structured and unstructured data, making it versatile for various types of information.
4. Using Apache Spark: Databrix includes Apache Spark, which provides high performance and supports big data processing.
Disadvantages of Databricks Lakehouse:
1. Implementation Difficulty: Implementing and configuring Databricks Lakehouse can be challenging, especially for organizations that have not previously worked with similar technologies.
2. Dependency on cloud solutions: For many companies, using Databricks Lakehouse may imply dependence on cloud services, which may cause certain limitations.
3. Cost: Using Databricks Lakehouse, especially in the cloud, can come with additional costs, making it less affordable for smaller businesses.
4. Necessity of data preparation: Working effectively with Lakehouse often requires preliminary data preparation, which may require additional effort.
5. Data management complexity: Managing data in a single space can be a challenge, especially when dealing with large volumes of information and different types of data.

💡📉Dataset programming is no longer a problem
Snorkel - a framework for data programming. The approach of this framework is to use various heuristics and a priori knowledge to automatically label datasets. The project started at Stanford as a tool to help mark up datasets for the information extraction task, and now the developers are creating a platform for use by external customers.
Snorkel's arsenal includes three key tools:
-marking functions for creating a dataset;
-transforming functions for dataset augmentation;
-slicing functions that highlight subsets in the dataset that are critical for the performance of learning models.

💡📊Selection of libraries for data analysis
Lux is an add-on to the popular Pandas data analysis package. It allows you to quickly create visual representations of data sets and apply basic statistical analysis with a minimum amount of code.
Pandas-profiling - helps generate a profiling report. This report gives a detailed overview of the variables in your dataset. It provides insight into statistics for individual characteristics of the data, such as the distribution, as well as the mean, minimum and maximum values. The same report provides insight into correlations and interactions between variables.
Sweet-Viz - provides fast visualization and analysis of data. Sweet-Viz's main selling point is its extensive HTML dashboard with useful views and data summaries, which is generated by executing just one line of code.
D-Tale is a Python library that provides an interactive and user-friendly interface for visualizing and analyzing Pandas data structures. It uses Flask as the backend and React as the frontend, making it easy to view and explore Pandas data frames, Series objects, MultiIndex, DatetimeIndex and RangeIndex. It integrates easily with Jupyter, Python terminals and ipython.
AutoViz is a Python library that provides automatic data visualization capabilities, allowing users to visualize data sets of any size with just one line of code. The program automatically generates reports in various formats, including HTML and Bokeh, and allows users to interact with the generated HTML reports.
KLib is a Python library that provides automatic exploratory data analysis (EDA) and data profiling capabilities. It offers various features and visualizations to quickly explore and analyze data sets.
SpeedML is a Python library that aims to speed up the development process of a machine learning pipeline. It integrates commonly used ML packages such as Pandas, NumPy, Scikit-learn, XGBoost and Matplotlib. SpeedML also provides functionality for automated EDA

😎💡📊In search of the hidden: little-known Python libraries for data analysts
PyCaret - An automated machine learning library that simplifies the transition from data preparation to modeling. PyCaret includes features for automatic model comparison, data preprocessing, and integration with MLflow for easy experimentation.
Vaex - A library for lazy loading and efficient processing of very large data. Great for analyzing large datasets with limited computing resources. aex allows you to efficiently work with datasets containing billions of rows, minimizing memory usage and optimizing performance.
Streamlit - A tool for quickly creating interactive web applications for data analytics. Streamlit can be used to develop applications that demonstrate machine learning results, such as image classification or time series forecasting.
Dask - Designed for parallel computing and working with large datasets. Ideal for scaling analytical operations and processing large volumes of data. Dask provides compatibility with tools like Pandas and Numpy and allows you to perform complex calculations on clusters.
Dash by Plotly - Framework for creating analytical web applications. Ideal for creating interactive dashboards and complex data visualizations. Dash allows you to create rich web applications for data analysis, such as visualizing company financial performance or market data trends.

😎💡Little-known but very useful DBMS
TimescaleDB takes PostgreSQL functionality and adds time series to it! Created as an extension to PostgreSQL, this database comes into its own when you deal with large-scale data that changes over time - such as data from IoT devices
FaunaDB is an online distributed transaction processing database with ACID properties. Due to this, high data processing speed and reliability are achieved. FaunaDB is based on technology pioneered by Twitter and was created as a startup by members of the social network's development team.
KeyDB is a Redis fork developed by a Canadian company and distributed under the free BSD license. There is support for multithreading
Riak (KV) is a distributed NoSQL key-value database. Riak CS is designed to provide simplicity, availability, distribution of cloud storage of any scale, and can be used to build cloud architectures - both public and private - or as infrastructure storage for highly loaded applications and services.
InfluxData is designed to monitor metrics and events in the infrastructure. The main focus is storing large amounts of time-stamped data (such as monitoring data, application metrics, and sensor readings) and processing them under high write load conditions.

🌎TOP DS-events all over the world in February
Feb 1-2 - Cloud Technology Townhall Tallinn 2024 - Tallinn, Estonia - https://cloudtechtallinn.com/
Feb 2 - Beyond Big Data: AI/Machine Learning Summit 2024 - Pittsburgh, USA - https://www.pghtech.org/events/BeyondBigData2024
Feb 2 - Nordic AI & Metaverse Summit - Copenhagen, Denmark - https://www.danskindustri.dk/arrangementer/soeg/arrangementer/salg-og-marketing/nordic-ai--metaverse-summit-2024/
Feb 2-3 - National Big Data Health Science Conference 2024 - Columbia, USA - https://www.sc-bdhs-conference.org/
Feb 2-5 - International Conference on Big Data Management 2024 - Zhuhai, China - https://www.icbdm.org/
Feb 6 - TINtech London Market 2024 - London, UK - https://www.the-insurance-network.co.uk/conferences/tintech-london-market
Feb 6 - Big Data III and Artificial Intelligence 2024 - London, UK - https://www.soci.org/events/fine-chemicals-group/2024/big-data-iii-and-artificial-intelligence
Feb 5-7 - IEEE International Conference On Semantic Computing 2024 - California, USA - https://www.ieee-icsc.org/
Feb 11-14 - Summit For Clinical Ops Executives 2024 - Orlando, USA - https://www.scopesummit.com/
Feb 22-23 - 9TH WORLD MACHINE LEARNING SUMMIT - Bangalore, India - https://1point21gws.com/machine-learning/bangalore/

🧐💡Firebird DBMS: advantages and disadvantages
Firebird is an open relational database with high performance and advanced capabilities.
Advantages:
1. Open Source: Firebird is distributed under an open source license (InterBase Public License). This allows users to freely use, modify and distribute the software without restrictions.
2. Multi-user support: Firebird provides efficient multi-user functionality, making it suitable for deployment in large enterprise environments.
3. Transactional security: Firebird supports ACID properties (atomicity, consistency, isolation, durability) to ensure transactional data integrity.
4. Multi-tier transaction architecture: Firebird uses a multi-tier transaction architecture, which allows multiple transactions to be executed simultaneously and prevents data locks.
5. SQL standard support: Firebird complies with SQL standards and has advanced features such as support for nested transactions and triggers.
Flaws:
1. Limited ecosystem and tools: Firebird may have a more limited ecosystem and tools compared to more common DBMSs such as MySQL, PostgreSQL or Microsoft SQL Server.
2. Limited GUI support: Firebird may not have as advanced database management tools as some competitors.
3. Limited Community: Compared to some other database management systems, Firebird may have a smaller community of users and developers, which may affect the availability of support and resources for developers.
In general, the choice of DBMS depends on the specific requirements of the project, and Firebird may be a good option for certain use cases, especially when openness and reliability are important.