Parq

parq

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Iceberg Lakehouse Engineering Video Playlist In the first post of this series, we introduced the Apache Parquet file format and touched upon one of its key features—columnar storage. Now, we’ll take a deeper dive into what this columnar storage model is, how it works, and why it’s so efficient for big data analytics. Understanding Parquet's columnar architecture is key to leveraging its full potential in optimizing data storage and query performance. What is Columnar Storage? Columnar storage means that instead of storing rows of data together, the data for each column is stored separately. This might seem counterintuitive at first, but it has major benefits for certain types of workloads, particularly those where you’re analyzing or aggregating specific columns rather than accessing entire rows. In a row-based format like CSV or JSON, data is written and read one row at a time. Each row stores all fields together in sequence. On the other hand, in a columnar format like Parquet, all values for a single column are stored together. For instance, if you have a dataset with columns for Name, Age, and Salary, all the values for the Name column are stored in one block, all the values for the Age column are stored in another, and so on. Why is Columnar Storage Efficient? The efficiency of columnar storage becomes clear when we consider the type of operations typically performed on large datasets in analytics. Let’s break down the advantages. 1. Faster Query Performance Columnar storage shines when your queries focus on a subset of columns. For example, if you want to calculate the average salary of employees in a large dataset, Parquet allows you to scan just the Salary column without reading the entire dataset. In a row-based format, even though you're only interested in one column, the system has to read all the data in every row to retrieve the values for that column. This results in a lot of unnecessary I/O operations, slowing down query performance. With Parquet, only the columns you need are read, making queries significantly faster. 2. Better Compression Parquet's columnar structure also improves compression. Since similar data types are stored together, compression algorithms can be applied more effectively. For example, if a column contains repeated values or data that follows a consistent pattern (such as dates or integers), it can be compressed more efficiently. By grouping similar values together, columnar formats enable algorithms like dictionary encoding or run-length encoding to achieve high compression ratios. This leads to smaller file sizes, which means reduced storage costs and faster data transfers. 3. Efficient Aggregation Columnar storage is ideal for aggregation queries, such as calculating sums, averages, or counts. These types of operations often focus on specific columns. With Parquet, only the relevant columns need to be read into memory, which not only improves query speed but also reduces the overall resource usage. 4. Batch Processing and Parallelization Another benefit of Parquet’s columnar model is that it enables better parallel processing. Since columns are stored independently, data processing engines like Apache Spark can read different columns in parallel, further speeding up query execution. This makes Parquet a great fit for distributed computing environments, where parallelism is key to achieving high performance. How Parquet Organizes Data Understanding how Parquet organizes data internally can help you fine-tune how you store and query your datasets. Columns and Row Groups: Parquet organizes data into row groups, which contain chunks of column data. A row group contains all the data for a subset of rows, but the data for each column is stored separately. This allows for efficient I/O when reading subsets of rows or columns.
Pages: Within each column chunk, data is further divided into pages. Parquet uses pages to store column data more granularly, which helps optimize compression and read performance. Each page is typically a few megabytes in size, and Parquet stores statistics about the data in each page, making it easier to skip irrelevant pages during query execution. Use Cases for Columnar Storage Columnar storage formats like Parquet are most effective in the following scenarios: Analytics-Heavy Workloads: If your workload involves a lot of analytical queries (e.g., calculating averages, filtering by certain columns), columnar formats will provide significant performance gains.
Big Data Environments: Parquet is commonly used in distributed data environments where large datasets are stored in cloud data lakes (e.g., AWS S3, Google Cloud Storage). It works seamlessly with frameworks like Apache Spark and Presto, which are built to process data at scale.
Data Warehousing: When designing data warehouses, storing data in Parquet allows you to run complex analytical queries efficiently while reducing storage costs due to Parquet’s high compression. When Not to Use Columnar Storage While columnar storage offers significant advantages for read-heavy, analytical workloads, it may not be the best option for all use cases. For example, transactional systems that involve frequent, small updates to data (like an online store's transaction log) may perform better with row-based formats, which are optimized for write-heavy operations. In such cases, the overhead of reading and writing data in columnar format may outweigh its benefits. Conclusion Parquet’s columnar storage model is what makes it a powerful tool for big data analytics. By organizing data by columns, Parquet allows for faster query performance, better compression, and more efficient aggregation. It’s designed to excel in environments where read-heavy workloads dominate and when your queries often target specific columns rather than entire datasets. In the next blog post, we’ll dive deeper into the file structure of Parquet, exploring how data is organized into row groups, pages, and columns to optimize both storage and retrieval. Stay tuned for part 3: Parquet File Structure: Pages, Row Groups, and Columns. Free Copy of Apache Iceberg the Definitive Guide Free Apache Iceberg Crash Course Iceberg Lakehouse Engineering Video Playlist Free Copy of Apache Iceberg the Definitive Guide Free Copy of Apache Iceberg the Definitive Guide Free Apache Iceberg Crash Course Free Apache Iceberg Crash Course Iceberg Lakehouse Engineering Video Playlist Iceberg Lakehouse Engineering Video Playlist In the first post of this series, we introduced the Apache Parquet file format and touched upon one of its key features—columnar storage. Now, we’ll take a deeper dive into what this columnar storage model is, how it works, and why it’s so efficient for big data analytics. Understanding Parquet's columnar architecture is key to leveraging its full potential in optimizing data storage and query performance. What is Columnar Storage? Columnar storage means that instead of storing rows of data together, the data for each column is stored separately. This might seem counterintuitive at first, but it has major benefits for certain types of workloads, particularly those where you’re analyzing or aggregating specific columns rather than accessing entire rows. In a row-based format like CSV or JSON, data is written and read one row at a time. Each row stores all fields together in sequence. On the other hand, in a columnar format like Parquet, all values for a single column are stored together. For instance, if you have a dataset with columns for Name , Age , and Salary , all the values for the Name column are stored in one block, all the values for the Age column are stored in another, and so on. Name Age Salary Name Age Why is Columnar Storage Efficient? The efficiency of columnar storage becomes clear when we consider the type of operations typically performed on large datasets in analytics. Let’s break down the advantages. 1. Faster Query Performance Faster Query Performance Columnar storage shines when your queries focus on a subset of columns. For example, if you want to calculate the average salary of employees in a large dataset, Parquet allows you to scan just the Salary column without reading the entire dataset. Salary In a row-based format, even though you're only interested in one column, the system has to read all the data in every row to retrieve the values for that column. This results in a lot of unnecessary I/O operations, slowing down query performance. With Parquet, only the columns you need are read, making queries significantly faster. 2. Better Compression Better Compression Parquet's columnar structure also improves compression. Since similar data types are stored together, compression algorithms can be applied more effectively. For example, if a column contains repeated values or data that follows a consistent pattern (such as dates or integers), it can be compressed more efficiently. By grouping similar values together, columnar formats enable algorithms like dictionary encoding or run-length encoding to achieve high compression ratios. This leads to smaller file sizes, which means reduced storage costs and faster data transfers. dictionary encoding run-length encoding 3. Efficient Aggregation Efficient Aggregation Columnar storage is ideal for aggregation queries, such as calculating sums, averages, or counts. These types of operations often focus on specific columns. With Parquet, only the relevant columns need to be read into memory, which not only improves query speed but also reduces the overall resource usage. 4. Batch Processing and Parallelization Batch Processing and Parallelization Another benefit of Parquet’s columnar model is that it enables better parallel processing. Since columns are stored independently, data processing engines like Apache Spark can read different columns in parallel, further speeding up query execution. This makes Parquet a great fit for distributed computing environments, where parallelism is key to achieving high performance. How Parquet Organizes Data Understanding how Parquet organizes data internally can help you fine-tune how you store and query your datasets. Columns and Row Groups: Parquet organizes data into row groups, which contain chunks of column data. A row group contains all the data for a subset of rows, but the data for each column is stored separately. This allows for efficient I/O when reading subsets of rows or columns. Pages: Within each column chunk, data is further divided into pages. Parquet uses pages to store column data more granularly, which helps optimize compression and read performance. Each page is typically a few megabytes in size, and Parquet stores statistics about the data in each page, making it easier to skip irrelevant pages during query execution. Columns and Row Groups : Parquet organizes data into row groups , which contain chunks of column data. A row group contains all the data for a subset of rows, but the data for each column is stored separately. This allows for efficient I/O when reading subsets of rows or columns. Columns and Row Groups row groups Pages : Within each column chunk, data is further divided into pages . Parquet uses pages to store column data more granularly, which helps optimize compression and read performance. Each page is typically a few megabytes in size, and Parquet stores statistics about the data in each page, making it easier to skip irrelevant pages during query execution. Pages pages Use Cases for Columnar Storage Columnar storage formats like Parquet are most effective in the following scenarios: Analytics-Heavy Workloads: If your workload involves a lot of analytical queries (e.g., calculating averages, filtering by certain columns), columnar formats will provide significant performance gains. Big Data Environments: Parquet is commonly used in distributed data environments where large datasets are stored in cloud data lakes (e.g., AWS S3, Google Cloud Storage). It works seamlessly with frameworks like Apache Spark and Presto, which are built to process data at scale. Data Warehousing: When designing data warehouses, storing data in Parquet allows you to run complex analytical queries efficiently while reducing storage costs due to Parquet’s high compression. Analytics-Heavy Workloads : If your workload involves a lot of analytical queries (e.g., calculating averages, filtering by certain columns), columnar formats will provide significant performance gains. Analytics-Heavy Workloads Big Data Environments : Parquet is commonly used in distributed data environments where large datasets are stored in cloud data lakes (e.g., AWS S3, Google Cloud Storage). It works seamlessly with frameworks like Apache Spark and Presto, which are built to process data at scale. Big Data Environments Data Warehousing : When designing data warehouses, storing data in Parquet allows you to run complex analytical queries efficiently while reducing storage costs due to Parquet’s high compression. Data Warehousing When Not to Use Columnar Storage While columnar storage offers significant advantages for read-heavy, analytical workloads, it may not be the best option for all use cases. For example, transactional systems that involve frequent, small updates to data (like an online store's transaction log) may perform better with row-based formats, which are optimized for write-heavy operations. In such cases, the overhead of reading and writing data in columnar format may outweigh its benefits. transactional systems Conclusion Parquet’s columnar storage model is what makes it a powerful tool for big data analytics. By organizing data by columns, Parquet allows for faster query performance, better compression, and more efficient aggregation. It’s designed to excel in environments where read-heavy workloads dominate and when your queries often target specific columns rather than entire datasets. In the next blog post, we’ll dive deeper into the file structure of Parquet, exploring how data is organized into row groups, pages, and columns to optimize both storage and retrieval. file structure Stay tuned for part 3: Parquet File Structure: Pages, Row Groups, and Columns . Parquet File Structure: Pages, Row Groups, and Columns

 All About Parquet Part 02 - Parquet's Columnar Storage Model

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Iceberg Lakehouse Engineering Video Playlist Managing and processing large datasets efficiently is crucial for many organizations. One of the key factors in data efficiency is the format in which data is stored and retrieved. Among the numerous file formats available, Apache Parquet has emerged as a popular choice, particularly in big data and cloud-based environments. But what exactly is the Parquet file format, and why is it so widely adopted? In this post, we’ll introduce you to the key concepts behind Parquet, its structure, and why it has become a go-to solution for data engineers and analysts alike. What is Parquet? Parquet is an open-source, columnar storage file format designed for efficient data storage and retrieval. Unlike row-based formats (like CSV or JSON), Parquet organizes data by columns rather than rows, making it highly efficient for analytical workloads. However, Parquet is used with various processing engines such as Apache Spark, Dremio, and Presto, and it works seamlessly with cloud platforms like AWS S3, Google Cloud Storage, and Azure. Why Use Parquet? The design of Parquet provides several key benefits that make it ideal for large-scale data processing: Efficient CompressionParquet’s columnar format allows for highly efficient compression. Since data is stored by column, similar values are grouped together, making compression algorithms far more effective compared to row-based formats. This can significantly reduce the storage footprint of your datasets.

Faster QueriesColumnar storage enables faster query execution for analytical workloads. When executing a query, Parquet allows data processing engines to scan only the columns relevant to the query, rather than reading the entire dataset. This reduces the amount of data that needs to be read, resulting in faster query times.

Schema EvolutionParquet supports schema evolution, which means you can modify the structure of your data (e.g., adding or removing columns) without breaking existing applications. This flexibility is particularly useful in dynamic environments where data structures evolve over time.

Cross-Platform CompatibilityParquet is compatible with multiple languages and tools, including Python, Java, C++, and many data processing frameworks. This makes it an excellent choice for multi-tool environments where data needs to be processed by different systems. The Difference Between Row-Based and Columnar Formats To fully understand the benefits of Parquet, it's essential to grasp the distinction between row-based and columnar file formats. Row-based formats store all the fields of a record together in sequence. Formats like CSV or JSON are row-based. These are suitable for transactional systems where entire rows need to be read and written frequently.
Columnar formats, like Parquet, store each column of a dataset together. This approach is advantageous for analytical workloads, where operations like aggregations or filters are performed on individual columns. For example, in a dataset with millions of rows and many columns, if you only need to perform analysis on one or two columns, Parquet allows you to read just those columns, avoiding the need to scan the entire dataset. Key Features of Parquet Parquet is packed with features that make it well-suited for a wide range of data use cases: Columnar Storage: As mentioned, the format stores data column-wise, making it ideal for read-heavy, analytical queries.
Efficient Compression: Parquet supports multiple compression algorithms (Snappy, Gzip, Brotli) that significantly reduce data size.
Splittable Files: Parquet files are splittable, meaning large files can be divided into smaller chunks for parallel processing.
Rich Data Types: Parquet supports complex nested data types, such as arrays, structs, and maps, allowing for flexible schema designs. When to Use Parquet Parquet is an excellent choice for scenarios where: You have large datasets that need to be processed for analytics.
Your queries often target specific columns in a dataset rather than entire rows.
You need efficient compression to reduce storage costs.
You're working in a distributed data environment, such as Hadoop, Spark, or cloud-based data lakes. However, Parquet may not be ideal for small, frequent updates or transactional systems where row-based formats are more suitable. Conclusion The Apache Parquet file format is a powerful tool for efficiently storing and querying large datasets. With its columnar storage design, Parquet provides superior compression, faster query execution, and flexibility through schema evolution. These advantages make it a preferred choice for big data processing and cloud environments. In the upcoming parts of this blog series, we’ll dive deeper into Parquet’s architecture, how it handles compression, encoding, and how you can work with Parquet in various tools like Python, Spark, and Dremio. Stay tuned for the next post in this series: Parquet's Columnar Storage Model. Free Copy of Apache Iceberg the Definitive Guide Free Apache Iceberg Crash Course Iceberg Lakehouse Engineering Video Playlist Free Copy of Apache Iceberg the Definitive Guide Free Copy of Apache Iceberg the Definitive Guide Free Copy of Apache Iceberg the Definitive Guide Free Apache Iceberg Crash Course Free Apache Iceberg Crash Course Free Apache Iceberg Crash Course Iceberg Lakehouse Engineering Video Playlist Iceberg Lakehouse Engineering Video Playlist Iceberg Lakehouse Engineering Video Playlist Managing and processing large datasets efficiently is crucial for many organizations. One of the key factors in data efficiency is the format in which data is stored and retrieved. Among the numerous file formats available, Apache Parquet has emerged as a popular choice, particularly in big data and cloud-based environments. But what exactly is the Parquet file format, and why is it so widely adopted? In this post, we’ll introduce you to the key concepts behind Parquet, its structure, and why it has become a go-to solution for data engineers and analysts alike. Apache Parquet What is Parquet? Parquet is an open-source, columnar storage file format designed for efficient data storage and retrieval. Unlike row-based formats (like CSV or JSON), Parquet organizes data by columns rather than rows, making it highly efficient for analytical workloads. However, Parquet is used with various processing engines such as Apache Spark, Dremio, and Presto, and it works seamlessly with cloud platforms like AWS S3, Google Cloud Storage, and Azure. open-source, columnar storage file format Why Use Parquet? The design of Parquet provides several key benefits that make it ideal for large-scale data processing: Efficient CompressionParquet’s columnar format allows for highly efficient compression. Since data is stored by column, similar values are grouped together, making compression algorithms far more effective compared to row-based formats. This can significantly reduce the storage footprint of your datasets. Faster QueriesColumnar storage enables faster query execution for analytical workloads. When executing a query, Parquet allows data processing engines to scan only the columns relevant to the query, rather than reading the entire dataset. This reduces the amount of data that needs to be read, resulting in faster query times. Schema EvolutionParquet supports schema evolution, which means you can modify the structure of your data (e.g., adding or removing columns) without breaking existing applications. This flexibility is particularly useful in dynamic environments where data structures evolve over time. Cross-Platform CompatibilityParquet is compatible with multiple languages and tools, including Python, Java, C++, and many data processing frameworks. This makes it an excellent choice for multi-tool environments where data needs to be processed by different systems. Efficient CompressionParquet’s columnar format allows for highly efficient compression. Since data is stored by column, similar values are grouped together, making compression algorithms far more effective compared to row-based formats. This can significantly reduce the storage footprint of your datasets. Efficient Compression Parquet’s columnar format allows for highly efficient compression. Since data is stored by column, similar values are grouped together, making compression algorithms far more effective compared to row-based formats. This can significantly reduce the storage footprint of your datasets. Efficient Compression Faster QueriesColumnar storage enables faster query execution for analytical workloads. When executing a query, Parquet allows data processing engines to scan only the columns relevant to the query, rather than reading the entire dataset. This reduces the amount of data that needs to be read, resulting in faster query times. Faster Queries Columnar storage enables faster query execution for analytical workloads. When executing a query, Parquet allows data processing engines to scan only the columns relevant to the query, rather than reading the entire dataset. This reduces the amount of data that needs to be read, resulting in faster query times. Faster Queries Schema EvolutionParquet supports schema evolution, which means you can modify the structure of your data (e.g., adding or removing columns) without breaking existing applications. This flexibility is particularly useful in dynamic environments where data structures evolve over time. Schema Evolution Parquet supports schema evolution, which means you can modify the structure of your data (e.g., adding or removing columns) without breaking existing applications. This flexibility is particularly useful in dynamic environments where data structures evolve over time. Schema Evolution Cross-Platform CompatibilityParquet is compatible with multiple languages and tools, including Python, Java, C++, and many data processing frameworks. This makes it an excellent choice for multi-tool environments where data needs to be processed by different systems. Cross-Platform Compatibility Parquet is compatible with multiple languages and tools, including Python, Java, C++, and many data processing frameworks. This makes it an excellent choice for multi-tool environments where data needs to be processed by different systems. Cross-Platform Compatibility The Difference Between Row-Based and Columnar Formats To fully understand the benefits of Parquet, it's essential to grasp the distinction between row-based and columnar file formats. Row-based formats store all the fields of a record together in sequence. Formats like CSV or JSON are row-based. These are suitable for transactional systems where entire rows need to be read and written frequently. Columnar formats, like Parquet, store each column of a dataset together. This approach is advantageous for analytical workloads, where operations like aggregations or filters are performed on individual columns. Row-based formats store all the fields of a record together in sequence. Formats like CSV or JSON are row-based. These are suitable for transactional systems where entire rows need to be read and written frequently. Row-based formats Columnar formats , like Parquet, store each column of a dataset together. This approach is advantageous for analytical workloads, where operations like aggregations or filters are performed on individual columns. Columnar formats For example, in a dataset with millions of rows and many columns, if you only need to perform analysis on one or two columns, Parquet allows you to read just those columns, avoiding the need to scan the entire dataset. Key Features of Parquet Parquet is packed with features that make it well-suited for a wide range of data use cases: Columnar Storage: As mentioned, the format stores data column-wise, making it ideal for read-heavy, analytical queries. Efficient Compression: Parquet supports multiple compression algorithms (Snappy, Gzip, Brotli) that significantly reduce data size. Splittable Files: Parquet files are splittable, meaning large files can be divided into smaller chunks for parallel processing. Rich Data Types: Parquet supports complex nested data types, such as arrays, structs, and maps, allowing for flexible schema designs. Columnar Storage : As mentioned, the format stores data column-wise, making it ideal for read-heavy, analytical queries. Columnar Storage Efficient Compression : Parquet supports multiple compression algorithms (Snappy, Gzip, Brotli) that significantly reduce data size. Efficient Compression Splittable Files : Parquet files are splittable, meaning large files can be divided into smaller chunks for parallel processing. Splittable Files Rich Data Types : Parquet supports complex nested data types, such as arrays, structs, and maps, allowing for flexible schema designs. Rich Data Types When to Use Parquet Parquet is an excellent choice for scenarios where: You have large datasets that need to be processed for analytics. Your queries often target specific columns in a dataset rather than entire rows. You need efficient compression to reduce storage costs. You're working in a distributed data environment, such as Hadoop, Spark, or cloud-based data lakes. You have large datasets that need to be processed for analytics. Your queries often target specific columns in a dataset rather than entire rows. You need efficient compression to reduce storage costs. You're working in a distributed data environment, such as Hadoop, Spark, or cloud-based data lakes. However, Parquet may not be ideal for small, frequent updates or transactional systems where row-based formats are more suitable. Conclusion The Apache Parquet file format is a powerful tool for efficiently storing and querying large datasets. With its columnar storage design, Parquet provides superior compression, faster query execution, and flexibility through schema evolution. These advantages make it a preferred choice for big data processing and cloud environments. In the upcoming parts of this blog series, we’ll dive deeper into Parquet’s architecture, how it handles compression, encoding, and how you can work with Parquet in various tools like Python, Spark, and Dremio. Stay tuned for the next post in this series: Parquet's Columnar Storage Model . Parquet's Columnar Storage Model

 All About Parquet Part 01 - An Introduction

Our team is proud to be part of transforming the way parking spaces work across the whole of Norway and soon the rest of the world, thanks to Parqio and the sharing economy. As the founder of the software development company , I'm always drawn to projects that challenge industries. Parqio, a mobile app revolutionizing parking space sharing, is one such project. Taking inspiration from the likes of and , Parqio's innovative mobile app is changing the way people rent out parking spaces and garages. Voypost Airbnb Uber Today, I invite you behind-the-scenes as I share insights into our collaboration with an environmentally friendly, cheap, and very convenient solution Parqio. Parqio Background When started on their mission to revolutionize parking space management, they faced a few challenges: launch their app fast to validate the idea on the market, implement third-party integrations, test and enhance UX/UI design. It was a daunting task that required a strategic development partner with relevant expertise to help them navigate the mentioned complexities of API, web & mobile app development. Here’s where Parqio started searching for a reliable development team and came across a Berlin-based company, Voypost. Parqio https://www.youtube.com/watch?v=nySwCRD3Lo4&embedable=true Voypost enters the game In November 2020 our team entered the game. There was literally only the idea Parqio C-level had. Ketil Hoigaard (Co-Founder, CEO), Henrik Printz Aamlid (Co-Founder, COO) and Nicholas Kristoffersen, Parqio's ex-CTO shared the vision of how to simplify access to parking spaces, as well as provide an opportunity to rent or lend them. But to make it most convenient for users, automation was needed. For us, the main goal was to turn the vision into reality. We committed to launch an MVP within less than 6 months and deliver high-quality, scalable, easily supported, standardized codebase. With the collaboration initially beginning with just 1 dedicated developer, the Parqio team quickly expanded to include 4 developers from Voypost after a successful MVP launch and a great product demand in the Norwegian market. Currently, we continue working closely  with CTO Benjamin Ochoa and Henrik Printz Aamlidto to drive quality-of-life improvements such as voice interfaces and mobile-friendly guest access tools with 3 developers and 1 project manager engaged with the product. Tech Magic: The Nuts and Bolts of Functionality The product ecosystem consists of 4 main technical aspects. Parqio API is bridging all parts of the Parqio ecosystem. It’s written in and which is shared for all apps & devices listed below. Node.js PostgreSQL Web app called Parqio Admin App to configure the functionality of gates and control access (add/remove residents, grant different access levels to a garage), add gates. This admin panel is built using React.js. Cross-platform Parqio App is used for residents and visitors to open gates, share spaces, find services in the neighborhood, and add automatic gate opening. It uses React Native to be accessible both for iOS/Android users. ParqioBox is a customized IoT device that connects to the garage door controller, somewhat like a router, which connects physical garage door management with Parqio Admin  and the Parqio  App. It is based on Linux/Bash, its firmware uses С/С++. The device logic is written using Node.js and Python integrated into the final firmware version. The journey began with the creation of an MVP, offering a simple design and essential features. Later on, we helped Parqio implement advanced functionality such as: Siri Intent Integration to open doors with voice commands Geofences and mobile push notifications for location-aware prompts to open gates Mapbox integration and live location display Integrating with several 3rd party services such as Twilio and Intercom Custom firmware for Raspberry Pi-based hardware with 4G LTE compatibility Further developing Parqio API for internal and external use, including custom integrations with several Parqio partners iBeacon and Ultra Wide-Band POCs Currently, Parqio cooperates with several partners and allows even more potential suppliers to integrate easily into their system. https://www.youtube.com/watch?v=2THVwIHIoVQ&embedable=true Results in Numbers Henrik Printz Aamlid says: "I’m glad to share my feedback on the partnership with Voypost to make their team shine in this article. The app developed by Voypost has reached around 30.000 users since its launch. The team is very communicative, and they work hard to deliver quality products on time. We are very happy with our collaboration and how they continuously find new ways to add value.” Parqio's performance speaks for itself. Within a year after launch, they acquired more than and garnered over Their partnership with Norway's largest property developer, and the investment from OBOS and among others, totaling over , further validate their success. The company now has its sights set on expanding within the Nordic region and beyond. 60 locations 5,000 users. OBOS , Construct Venture $3.5 million investment Anna, Parqio’s dedicated Project Manager, shared her thoughts on the collaboration: “It's an incredible experience to work on Parqio and witness a huge transformation: from the early days when it was just an idea, to nowadays, with over launched. Our teamwork and agile methodologies of Parqio management, based on product and people-first values, help us achieve all the expected goals and milestones. I'm excited to continue our cooperation and observe the next level of product development because there are a lot of plans for the future.” 200 locations Sum up 4+ people dedicated to the project 5,000+ users in the first year 30,000 total users now by October 2023 200 locations launched $3.5 million in investment raised 3+ years of cooperation is a story of resilience, innovation, and the unwavering belief that great things can be achieved. With Voypost's support, they have achieved remarkable results and are poised for further growth. As Parqio expands its footprint across Europe, it remains a shining example of how technology can revolutionize traditional industries and create new opportunities in the sharing economy. Parqio's journey

How Parqio Hacks Parking Space Management in Norway 

নরওয়েতে পারকিও কীভাবে পার্কিং স্পেস ম্যানেজমেন্ট হ্যাক করে

Wie Parqio das Parkplatzmanagement in Norwegen hackt

Cómo Parqio piratea la gestión de espacios de aparcamiento en Noruega

Comment Parqio pirate la gestion des espaces de stationnement en Norvège

कैसे Parqio नॉर्वे में पार्किंग स्पेस प्रबंधन को हैक करता है

Parqio がノルウェーの駐車スペース管理をハッキングする方法

Parqio가 노르웨이에서 주차 공간 관리를 해킹하는 방법

Como Parqio hackeia o gerenciamento de vagas de estacionamento na Noruega

Как Parqio взламывает управление парковочными местами в Норвегии

Parqio, Norveç'teki Park Alanı Yönetimini Nasıl Hackliyor?

Parqio hack quản lý không gian đỗ xe ở Na Uy như thế nào

Parqio 如何破解挪威的停车位管理

Hi, my name is Andrew! I oversee a large-scale online service where our queues (Kafka or similar) process millions of events each day. We have to spot anomalies quickly, track key metrics, and notify the team about critical issues—all in real time. Because data streams in from various systems, it’s never as simple as using one language or tool. Some engineers favor Go for performance and concurrency, others prefer Python for machine learning, and Node.js often wins for handling external integrations (Slack, webhooks, etc.). On top of that, our DevOps folks rely heavily on classic CLI tools like tar and gzip for archiving logs. tar gzip At first, everything was scattered across different scripts, containers, and services. It worked, but it got messy—too many handoffs, too many config files, and a constant risk of something breaking when one team decided to update their part of the puzzle. So we decided to unify the entire process in one computational graph. The solution we came up with is a local-first FaaS approach where each “node” can be written in whatever language or tool we want, but everything is orchestrated under a single workflow. Below is how we designed each node in our real-time pipeline, why we chose each language or tool, and how data flows between them via simple JSON adapters. one Node 1 (Rust): High-Performance Ingestion Why Rust? We needed maximum throughput and memory safety. Our ingestion service handles hundreds of thousands of events per minute from sources like IoT devices and microservices in our cluster. Rust’s ownership model helps avoid memory leaks and race conditions, which is crucial when your service runs 24/7. Example Rust code (node_rust_ingest.rs): Example Rust code ( node_rust_ingest.rs use std::fs;
use serde::{Deserialize, Serialize};

#[derive(Deserialize)]
struct IngestConfig {
    pub source_path: String,
}

fn main() -> Result<(), Box<dyn std::error::Error>> {
    // Read config from JSON
    let config_data = fs::read_to_string("config.json")?;
    let config: IngestConfig = serde_json::from_str(&config_data)?;

    // Imagine reading from a high-volume stream here:
    let raw_events = vec![
        "temperature:24.5 sensor_id=alpha",
        "temperature:25.1 sensor_id=beta",
    ];

    // Convert the raw events into JSON and save
    let json_str = serde_json::to_string_pretty(&raw_events)?;
    fs::write("raw_events.json", json_str)?;

    Ok(())
} use std::fs;
use serde::{Deserialize, Serialize};

#[derive(Deserialize)]
struct IngestConfig {
    pub source_path: String,
}

fn main() -> Result<(), Box<dyn std::error::Error>> {
    // Read config from JSON
    let config_data = fs::read_to_string("config.json")?;
    let config: IngestConfig = serde_json::from_str(&config_data)?;

    // Imagine reading from a high-volume stream here:
    let raw_events = vec![
        "temperature:24.5 sensor_id=alpha",
        "temperature:25.1 sensor_id=beta",
    ];

    // Convert the raw events into JSON and save
    let json_str = serde_json::to_string_pretty(&raw_events)?;
    fs::write("raw_events.json", json_str)?;

    Ok(())
} Input: A JSON file named config.json (handled by an adapter, e.g., adapter_input_json("config.json")).
Output: raw_events.json with the raw events. Input: A JSON file named config.json (handled by an adapter, e.g., adapter_input_json("config.json")). Input config.json adapter_input_json("config.json") Output: raw_events.json with the raw events. Output raw_events.json In practice, this Rust code might pull real messages from Kafka or a custom TCP socket, but the idea is the same: read something from a stream, then write it out as JSON. Node 2 (Go): Filtering and Normalization Why Go? It’s great for concurrency (goroutines and channels) and compiles into a single binary we can deploy anywhere. This node focuses on quick filtering of raw data, dropping empty or invalid records, and transforming some fields before sending them off for deeper analysis. Example Go code (node_go_filter.go): Example Go code ( node_go_filter.go package main

import (
    "encoding/json"
    "fmt"
    "io/ioutil"
    "os"
    "strings"
)

type FilteredEvent struct {
    Valid bool   `json:\"valid\"`
    Data  string `json:\"data\"`
}

func main() {
    // Read raw events JSON from file (adapter does the path resolution)
    rawData, err := ioutil.ReadFile(\"raw_events.json\")
    if err != nil {
        fmt.Println(\"Error reading file:\", err)
        os.Exit(1)
    }

    var events []string
    if err := json.Unmarshal(rawData, &events); err != nil {
        fmt.Println(\"Error unmarshalling JSON:\", err)
        os.Exit(1)
    }

    // Filter out any malformed or empty events
    var filtered []FilteredEvent
    for _, e := range events {
        if strings.TrimSpace(e) == \"\" {
            continue
        }
        filtered = append(filtered, FilteredEvent{
            Valid: true,
            Data:  e,
        })
    }

    // Write filtered events out to JSON
    output, err := json.MarshalIndent(filtered, \"\", \"  \")
    if err != nil {
        fmt.Println(\"Error marshalling JSON:\", err)
        os.Exit(1)
    }

    if err := ioutil.WriteFile(\"filtered_events.json\", output, 0644); err != nil {
        fmt.Println(\"Error writing file:\", err)
        os.Exit(1)
    }
} package main

import (
    "encoding/json"
    "fmt"
    "io/ioutil"
    "os"
    "strings"
)

type FilteredEvent struct {
    Valid bool   `json:\"valid\"`
    Data  string `json:\"data\"`
}

func main() {
    // Read raw events JSON from file (adapter does the path resolution)
    rawData, err := ioutil.ReadFile(\"raw_events.json\")
    if err != nil {
        fmt.Println(\"Error reading file:\", err)
        os.Exit(1)
    }

    var events []string
    if err := json.Unmarshal(rawData, &events); err != nil {
        fmt.Println(\"Error unmarshalling JSON:\", err)
        os.Exit(1)
    }

    // Filter out any malformed or empty events
    var filtered []FilteredEvent
    for _, e := range events {
        if strings.TrimSpace(e) == \"\" {
            continue
        }
        filtered = append(filtered, FilteredEvent{
            Valid: true,
            Data:  e,
        })
    }

    // Write filtered events out to JSON
    output, err := json.MarshalIndent(filtered, \"\", \"  \")
    if err != nil {
        fmt.Println(\"Error marshalling JSON:\", err)
        os.Exit(1)
    }

    if err := ioutil.WriteFile(\"filtered_events.json\", output, 0644); err != nil {
        fmt.Println(\"Error writing file:\", err)
        os.Exit(1)
    }
} Input: raw_events.json.
Output: filtered_events.json with an array of cleaned-up records. Input: raw_events.json. Input raw_events.json Output: filtered_events.json with an array of cleaned-up records. Output filtered_events.json Node 3 (Python): ML Classification Why Python? Most data science and ML teams rely on Python’s ecosystem (NumPy, scikit-learn, PyTorch, etc.). It’s easy to iterate quickly and load pretrained models. Example Python code (node_python_ml.py): Example Python code ( node_python_ml.py import json

def main():
    # Load filtered events from JSON
    with open('filtered_events.json', 'r') as f:
        filtered_data = json.load(f)

    # Imagine we have a pre-trained model loaded here
    # For illustration, let's just mock predictions
    predicted_events = []
    for item in filtered_data:
        data_str = item['data']
        # Some ML logic here...
        prediction = 'normal'
        if 'sensor_id=beta' in data_str:
            prediction = 'warning'  # as an example

        predicted_events.append({
            'data': data_str,
            'prediction': prediction
        })

    # Save predictions to JSON
    with open('predicted_events.json', 'w') as f:
        json.dump(predicted_events, f, indent=2)

if __name__ == '__main__':
    main() import json

def main():
    # Load filtered events from JSON
    with open('filtered_events.json', 'r') as f:
        filtered_data = json.load(f)

    # Imagine we have a pre-trained model loaded here
    # For illustration, let's just mock predictions
    predicted_events = []
    for item in filtered_data:
        data_str = item['data']
        # Some ML logic here...
        prediction = 'normal'
        if 'sensor_id=beta' in data_str:
            prediction = 'warning'  # as an example

        predicted_events.append({
            'data': data_str,
            'prediction': prediction
        })

    # Save predictions to JSON
    with open('predicted_events.json', 'w') as f:
        json.dump(predicted_events, f, indent=2)

if __name__ == '__main__':
    main() Input: filtered_events.json.
Output: predicted_events.json with a prediction field for each record. Input: filtered_events.json. Input filtered_events.json Output: predicted_events.json with a prediction field for each record. Output predicted_events.json prediction In a real scenario, you might load a Torch or TensorFlow model and do actual inference. The concept stays the same: read JSON, process, write JSON. Node 4 (Node.js): External Service Notifications Why Node.js? We use it for real-time hooks (Slack notifications, webhooks, etc.), thanks to its event-driven nature and wide range of packages on npm. Example JavaScript code (node_js_notify.js): Example JavaScript code ( node_js_notify.js const fs = require('fs');
const axios = require('axios'); // For sending data to a webhook, Slack, etc.

async function main() {
  // Read the predicted events JSON
  const rawData = fs.readFileSync('predicted_events.json', 'utf8');
  const predictedEvents = JSON.parse(rawData);

  // For each event, if it's a warning, send an alert
  for (const event of predictedEvents) {
    if (event.prediction === 'warning') {
      await sendAlert(event.data);
    }
  }
}

async function sendAlert(data) {
  // Example: post to Slack (replace with your actual webhook)
  const slackWebhook = 'https://hooks.slack.com/services/your/webhook/url';
  await axios.post(slackWebhook, {
    text: `Alert! Check sensor data: ${data}`
  });
  console.log('Alert sent to Slack for:', data);
}

main().catch(err => {
  console.error('Error sending alerts:', err);
  process.exit(1);
}); const fs = require('fs');
const axios = require('axios'); // For sending data to a webhook, Slack, etc.

async function main() {
  // Read the predicted events JSON
  const rawData = fs.readFileSync('predicted_events.json', 'utf8');
  const predictedEvents = JSON.parse(rawData);

  // For each event, if it's a warning, send an alert
  for (const event of predictedEvents) {
    if (event.prediction === 'warning') {
      await sendAlert(event.data);
    }
  }
}

async function sendAlert(data) {
  // Example: post to Slack (replace with your actual webhook)
  const slackWebhook = 'https://hooks.slack.com/services/your/webhook/url';
  await axios.post(slackWebhook, {
    text: `Alert! Check sensor data: ${data}`
  });
  console.log('Alert sent to Slack for:', data);
}

main().catch(err => {
  console.error('Error sending alerts:', err);
  process.exit(1);
}); Input: predicted_events.json.
Output: Not necessarily a JSON output (though you could log to a file). Typically, it just triggers external actions. Input: predicted_events.json. Input predicted_events.json Output: Not necessarily a JSON output (though you could log to a file). Typically, it just triggers external actions. Output Node 5 (Shell Script): Archiving Logs Why Shell/CLI? Classic tools like tar and gzip are reliable workhorses. There’s no need to rewrite them in some other language. They’ve been proven in production for decades. tar gzip Example Shell script (node_shell_archive.sh): Example Shell script ( node_shell_archive.sh #!/usr/bin/env bash

# We assume raw_events.json, filtered_events.json, predicted_events.json
# are present in the current directory. We'll archive them together.

ARCHIVE_NAME="events_archive.tar.gz"

tar -czf "$ARCHIVE_NAME" raw_events.json filtered_events.json predicted_events.json

echo "Archive created: $ARCHIVE_NAME" #!/usr/bin/env bash

# We assume raw_events.json, filtered_events.json, predicted_events.json
# are present in the current directory. We'll archive them together.

ARCHIVE_NAME="events_archive.tar.gz"

tar -czf "$ARCHIVE_NAME" raw_events.json filtered_events.json predicted_events.json

echo "Archive created: $ARCHIVE_NAME" Input: The JSON files we produced in the previous steps.
Output: events_archive.tar.gz, ready to be stored or uploaded to a long-term storage solution. Input: The JSON files we produced in the previous steps. Input Output: events_archive.tar.gz, ready to be stored or uploaded to a long-term storage solution. Output events_archive.tar.gz The Role of Data Adapters In our setup, each node declares what type of data it expects to read and what it produces. But instead of tying every node to a single format like JSON, we rely on adapters to handle the actual loading and saving of data in the target language. An adapter is just a small function or library call that knows how to read or write a specific data type (files, streams, binary formats, you name it) within a particular runtime. adapters Example:

adapter_input_json("filtered_events.json") might parse a JSON file into native objects in Python or Go.
adapter_output_csv("results.csv") could serialize an array of events into a CSV string and save it. Example:

adapter_input_json("filtered_events.json") might parse a JSON file into native objects in Python or Go.
adapter_output_csv("results.csv") could serialize an array of events into a CSV string and save it. Example: adapter_input_json("filtered_events.json") might parse a JSON file into native objects in Python or Go.
adapter_output_csv("results.csv") could serialize an array of events into a CSV string and save it. adapter_input_json("filtered_events.json") might parse a JSON file into native objects in Python or Go. adapter_input_json("filtered_events.json") adapter_output_csv("results.csv") could serialize an array of events into a CSV string and save it. adapter_output_csv("results.csv") The beauty of this approach is that each node can seamlessly work with different data representations, as long as there’s an adapter for them. Whether you prefer JSON, CSV, binary blobs, or even specialized formats (like Parquet or Avro), adapters keep the underlying implementation details out of the node’s logic. That way, Rust stays focused on ingestion performance, Python remains laser-focused on ML tasks, and Node.js is free to handle whatever output format suits your external APIs—without forcing the entire pipeline to stick to one rigid standard. Rust stays focused on ingestion performance, Python remains laser-focused on ML tasks, and Node.js is free to handle whatever output format suits your external APIs—without forcing the entire pipeline to stick to one rigid standard. Final Thoughts By unifying everything into a single computational graph and adopting a local FaaS mindset, we drastically reduced the friction between teams. The Rust folks can keep optimizing throughput without breaking anything downstream, Go developers can refine their filtering logic, Python ML engineers can retrain or tweak models, and our Node.js layer can easily tie into Slack. Even our DevOps guys are happy to keep using battle-tested CLI tools for archives. Before we set this up, we spent way too much time managing glue code and random integration points. Now, each team can just focus on their own node, with minimal dependencies on everyone else. If you’re dealing with a multi-language environment—or foresee that one tool won’t suffice for your entire pipeline—this sort of local-first FaaS approach might be exactly what you need.

Building a Multi-Language Monster Pipeline? Here’s the Smart Way to Do It

The AI Engineer’s Playbook: Mastering Vector Search & Management (Part 2)

DUBAI, UAE, March 22nd, 2025/Chainwire/--Bybit, the world’s second-largest cryptocurrency exchange by trading volume, has partnered with Tomorrowland Brasil as its exclusive payment provider for 2025 and 2026. Tomorrowland Brasil Tomorrowland Brasil This landmark collaboration grants Bybit Card holders privileged early access to tickets before they become available to the general public - a first in Tomorrowland’s history. A Historic First for Bybit and Tomorrowland Tomorrowland Brasil 2025 is set for October 10-12 at Parque Maeda in Itu, São Paulo. This year, Tomorrowland Brasil welcomes ‘LIFE’ as its mainstage theme, continuing the magical journey that began at Tomorrowland Belgium in 2024. Set in the mythical world of Silvyra, ‘LIFE’ transports the People of Tomorrow to an era of untamed natural beauty, just as a rare celestial event - the alignment of two moons - is about to unfold. Over three unforgettable days, attendees can escape the city and enjoy performances from more than 150 top electronic artists across six breathtaking stages. “By partnering with Tomorrowland Brasil, we are merging the energy of music with the innovation of blockchain. This collaboration reinforces Bybit’s commitment to integrating crypto seamlessly into everyday experiences, and our cardholders will enjoy unparalleled access to one of the world’s most iconic festivals,” said Joan Han, Head of the Payment Business Unit at Bybit. “By partnering with Tomorrowland Brasil, we are merging the energy of music with the innovation of blockchain. This collaboration reinforces Bybit’s commitment to integrating crypto seamlessly into everyday experiences, and our cardholders will enjoy unparalleled access to one of the world’s most iconic festivals,” said Joan Han, Head of the Payment Business Unit at Bybit. Bybit Card: More Than Just a Payment Solution The Bybit Card enhances users' festival experience by offering instant activation, seamless payments, and exclusive rewards. With compatibility across Apple Pay, Google Pay, and Samsung Pay, it ensures effortless transactions worldwide. Beyond convenience, Bybit Cards help users grow their wealth. With Auto-Savings, cardholders can earn up to 8% APR on their balance, ensuring passive income without extra effort. There are no annual fees or hidden charges, and users can withdraw up to $100 in cash for free every month, with a 2% fee thereafter. Plus, Bybit’s partnership with DHL ensures fast and secure worldwide delivery of the physical card. How to Secure Tomorrowland Brasil Tickets with Bybit Card The Bybit Card is the ultimate festival payment solution, giving holders exclusive access to a two-day presale before tickets go on sale to the general public. Exclusive Registration Period: Now-April 3, 2025
Presale Period: April 4, 10:00 BRT / 15:00 CEST - April 6, 10:00 BRT / 15:00 CEST
Presale Website: Bybit Card x Tomorrowland
General Public Sale: Begins after the presale period Exclusive Registration Period: Now-April 3, 2025 Presale Period: April 4, 10:00 BRT / 15:00 CEST - April 6, 10:00 BRT / 15:00 CEST Presale Website: Bybit Card x Tomorrowland Bybit Card x Tomorrowland Bybit Card x Tomorrowland General Public Sale: Begins after the presale period To participate in the exclusive presale, users must be Bybit Card holders and complete their registration before the presale date on the Bybit website. Only registered users will have access to the presale period. Bybit website Bybit website New users can still apply for a Bybit Virtual Card to join the presale. Bybit Virtual Card Bybit Virtual Card Priority Access: Bybit Card holders enter the first eight digits of their card (BIN code) to unlock ticket access.
Secure a Spot: Each user can purchase up to six tickets.
Exclusive Payment Method: Tickets must be purchased using a Bybit Card, as Bybit is the festival’s exclusive presale payment partner. Priority Access: Bybit Card holders enter the first eight digits of their card (BIN code) to unlock ticket access. Secure a Spot: Each user can purchase up to six tickets. Exclusive Payment Method: Tickets must be purchased using a Bybit Card, as Bybit is the festival’s exclusive presale payment partner. Bybit’s seamless integration with Tomorrowland Brasil’s official website and ticketing system ensures a hassle-free booking experience for cardholders. General Ticket Sales Open April 8 For users who miss out on the exclusive presale, tickets will still be available for general sale starting April 8. Purchases can be made using Bybit Card or Bybit Pay, with new users enjoying a 10% cashback on their festival purchases. #Bybit / #TheCryptoArk About Bybit Bybit is the world’s second-largest cryptocurrency exchange by trading volume, serving a global community of over 60 million users. Founded in 2018, Bybit is redefining openness in the decentralized world by creating a simpler, open and equal ecosystem for everyone. Bybit Bybit With a strong focus on Web3, Bybit partners strategically with leading blockchain protocols to provide robust infrastructure and drive on-chain innovation. Renowned for its secure custody, diverse marketplaces, intuitive user experience, and advanced blockchain tools, Bybit bridges the gap between TradFi and DeFi, empowering builders, creators, and enthusiasts to unlock the full potential of Web3. Discover the future of decentralized finance at Bybit.com. Bybit.com Bybit.com For more details about Bybit, please visit Bybit Press. For media inquiries, please contact: media@bybit.com Bybit Press. Bybit Press media@bybit.com media@bybit.com For updates, please follow: Bybit's Communities and Social Media Bybit's Communities and Social Media Bybit's Communities and Social Media Discord | Facebook | Instagram | LinkedIn | Reddit | Telegram | TikTok | X | Youtube Discord Discord Facebook Facebook Instagram Instagram LinkedIn LinkedIn Reddit Reddit Telegram Telegram TikTok TikTok X X Youtube Youtube About TOMORROWLAND BRASIL Tomorrowland Brasil 2025 will take place on October 10-12 in the beautiful festival area of Parque Maeda in Itu, a municipality of São Paulo. The festival will revolve around the mesmerizing ‘LIFE’ theme, set against the backdrop of Brazil’s enchanting natural beauty. Tomorrowland Brasil 2025 Tomorrowland Brasil 2025 A story set in the mythical realm of Silvyra, it’s a world unto its own, filled with creatures, plant life, and people living in harmony, each with their own stories, with the diversity of Silvyra’s lush nature represented in the spectacular ‘LIFE’ Mainstage. Offering the ultimate escape from the city during three days of bliss, guests will be treated to breathtaking performances by more than 150 of the world’s finest electronic artists across 6 mesmerizing stages. The first two festival editions of Tomorrowland Brasil took place in 2015 and 2016 in the beautiful festival area of Parque Maeda in Itu, São Paulo. After years of dreaming of a return, Tomorrowland finally headed back to Brazil in 2023, becoming a yearly highlight once again. Tomorrowland Brasil 2025 October 10-12, 2025 Parque Maeda, Itu brasil.tomorrowland.com brasil.tomorrowland.com brasil.tomorrowland.com Contact Head of PR Tony Au Bybit media@bybit.com This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here here here

Bybit Named Exclusive Payment Partner For Tomorrowland Brasil 2025-26, Launches Cardholder Presale

Bybit е обявен за ексклузивен партньор за плащане за Tomorrowland Brasil 2025-26, стартира предварителна продажба на картодържател

টুমরোল্যান্ড ব্রাজিল ২০২৫-২৬-এর জন্য বাইবিট এক্সক্লুসিভ পেমেন্ট পার্টনার হিসেবে মনোনীত, কার্ডহোল্ডার প্রিসেল চালু করেছে

Bybit, socio exclusivo de pagos para Tomorrowland Brasil 2025-26, lanza preventa para titulares de tarjetas.

Bybit, partenaire de paiement exclusif pour Tomorrowland Brasil 2025-26, lance la prévente pour les titulaires de carte

Bybit בשם שותף תשלומים בלעדי עבור Tomorrowland Brasil 2025-26, משיקה מכירה מוקדמת לבעלי כרטיס

Bybit Dinobatkan Sebagai Mitra Pembayaran Eksklusif Untuk Tomorrowland Brasil 2025-26, Luncurkan Prapenjualan Pemegang Kartu

Bybit、Tomorrowland Brasil 2025-26の独占決済パートナーに指名され、カード会員向け先行販売を開始

Bybit Named Exclusive Payment Partner for Tomorrowland Brasil 2025-26, spúšťa predpredaj pre držiteľov kariet

Bybit ได้รับการเสนอชื่อให้เป็นพันธมิตรด้านการชำระเงินเฉพาะสำหรับ Tomorrowland Brasil 2025-26 และเปิดตัวการขายล่วงหน้าสำหรับผู้ถือบัตร

“Ertirland Brasil 2025-26” üçin “Bybit Exclusive Payment Partner” diýlip atlandyrylýar, Kart eýesi Presale başlaýar

Bybit, Tomorrowland Brasil 2025-26 için Özel Ödeme Ortağı Olarak Adlandırıldı, Kart Sahibi Ön Satışını Başlattı

بائیبٹ کا نام خصوصی ادائیگی پارٹنر برائے ٹومورولینڈ برازیل 2025-26، کارڈ ہولڈر پری سیل کا آغاز

AI Just Took Over Ad Targeting—And It’s Smarter, Faster, and Less Creepy Than Ever

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