February 2022 Summaries

Memgraph 2.2 introduces several updates aimed at enhancing user accessibility and system functionality, including native support for Apple M1 chips and improved logging capabilities via WebSocket integration. This release simplifies the integration of Memgraph in different environments by offering a Docker container for Apple silicon and introducing new streaming configuration options for Kafka, which are particularly beneficial in enterprise settings. Improvements to query module management now allow for more dynamic and domain-specific logic, with new built-in procedures offering tighter control over module execution. Additionally, bug fixes address issues like Python interpreter crashes and transaction handling errors, while features such as enhanced temporal type precision and upcoming capabilities like Bolt over WebSocket and custom function definitions via C API promise further advancements. Users are encouraged to explore these updates, provide feedback on any issues, and engage with the community through the available documentation and Discord server.

Memgraph has successfully navigated the challenges posed by the COVID-19 pandemic by implementing a robust performance management system centered around continuous feedback and employee engagement surveys, which has helped ensure alignment and efficiency within the company. As the pandemic necessitated a shift to remote work, Memgraph adapted quickly by providing resources for home offices, optimizing processes for virtual collaboration, and ultimately deciding to make remote work a permanent fixture, which allowed them to tap into a broader talent pool across six countries. The company achieved significant milestones such as securing $6.7 million in funding and increasing its headcount by 25% in 2020, as well as maintaining an average annual growth rate of 26% in 2021. Memgraph also focused on employee-centric policies, revising compensation strategies to account for inflation and ensuring salaries are competitive within local markets, leading to a rise in average salaries by 30% since the pandemic began. The positive results of these initiatives are reflected in employee engagement surveys, which indicate that the continuous feedback approach is positively impacting work-life balance and contributing to the company's growth and success.

Nenad Malic announces the return of a free, 10-day email course designed to teach the basics of Cypher, the query language used by Memgraph, after a brief hiatus due to website and newsletter updates. Participants receive daily emails covering specific Cypher topics, complete with explanations, code examples, and tips, as well as the opportunity to explore a dataset of the European road network on the Memgraph Playground. The course includes lessons on topics such as filtering results, structuring data in a graph database, handling cases and duplicates, using lists and aggregating functions, modifying data, applying constraints and indexing, and writing and optimizing advanced queries. By the end of the course, participants will have gained the foundational knowledge needed to work with Cypher and graph databases, along with a downloadable guide summarizing all 10 lessons.

Streaming analytics, a technology essential for near-instantaneous data processing, requires databases that support continuous queries to handle real-time data. These databases are crucial for applications like fraud detection, real-time credit scoring, and customer relationship management by processing and enriching incoming data streams from sources such as IoT devices, mobile phones, and web clickstreams. Choosing the right streaming database involves considering factors like data type, volume, budget, and processing speed, as traditional batch processing systems cannot manage the high velocity and variety of streaming data. Some notable streaming analytics tools include Amazon Kinesis, which manages real-time data with low latencies; Memgraph, known for its rapid in-memory data processing; Apache Storm, which provides scalable and fault-tolerant stream processing; Apache Kafka, which securely captures event streams in real time; and StreamSQL, which simplifies and accelerates machine learning development. These tools offer varied features, from ease of use and scalability to support for machine learning and real-time insights, enabling organizations to efficiently handle massive data inflows and make informed decisions quickly.

Memgraph, an in-memory graph database, and MAGE, its open-source repository tool, facilitate the easy implementation of graph algorithms on data networks. Users can quickly download Memgraph, import their data, and choose from 19 popular graph algorithms to perform complex analyses. These algorithms, which include Betweenness Centrality, Biconnected Components, Bipartite Matching, Bridge Detection, Community Detection, and others, are designed to address various network problems such as node importance, connectivity, and structure. Each algorithm comes with its own GitHub repository for easy integration as query modules within Memgraph. The document provides detailed insights into how each algorithm functions, their computational complexities, and potential applications in fields like recommendation systems, fraud detection, and dynamic networks. For a visual overview, users are encouraged to explore the Advanced Graph Algorithms Infographic, which illustrates the capabilities of these algorithms in Memgraph.

Memgraph's Core team, consisting of engineers Antonio Andelic, Benjamin Antal, Jure Bajic, and Kostantinos Kyrimis, is dedicated to developing Memgraph's distributed graph database engine, primarily using C++ with a mix of Python, Rust, and occasionally JavaScript. The team collaborates on planning releases, implementing features, and addressing the complexities of database development, such as performance optimization and memory management. They face challenges like ensuring seamless integration of new features and scaling the system for distributed workloads. The team values open communication, constructive debates, and a relaxed working environment, which fosters innovation and collaboration. They are currently focused on projects like WebAssembly integration, enhancing streaming capabilities, and improving performance and memory consumption. The Core team appreciates the opportunities for professional growth and the chance to contribute significantly to Memgraph's evolution while encouraging new team members to voice innovative ideas and solutions.

Advent of Code is an annual programming challenge that captivates tens of thousands of software engineers, including a team of eight from Memgraph, who participated in the 2021 edition to hone their coding skills and enjoy the camaraderie of competition. These engineers, driven by a desire to challenge themselves and learn, often woke up as early as 5 or 6 AM to tackle the puzzles before starting their workday, sharing hints and discussing solutions in a dedicated Slack channel. While some participants experimented with languages like Rust and Go, Python was the most popular choice due to its familiarity, and the storyline-rich puzzles varied in complexity, with puzzles 18 and 19 being particularly memorable for their intricate problem-solving requirements. Beyond the competition, the experience fostered strong bonds among colleagues, with engineers reviewing each other's code and engaging in lively discussions, while also connecting with a broader community on platforms like Reddit. The event underscored the excitement of coding and the joy of collaboration, emphasizing both the individual and collective benefits of such programming contests.

Stream processing is a big data architecture that allows for real-time analysis of data, providing crucial insights within milliseconds and operating asynchronously without waiting for responses between the data source and processing. It contrasts with batch processing, which handles large data volumes in groups sequentially, usually at the end of a business cycle. Stream processing infrastructure includes real-time data sources, data processing frameworks like Apache Flink and Kafka, and streaming analytics users who act upon processed data for applications such as fraud detection, IoT data management, and personalized advertising. Companies like Netflix and Amazon use stream processing to handle vast amounts of log data, providing valuable insights and enabling immediate responses to issues or opportunities. The framework supports various use cases, such as real-time analytics, fraud detection, IoT data management, and personalized marketing, by continuously processing data and generating actionable reports or alerts.

Graph Data Zagreb is a newly formed group for enthusiasts of graph databases, algorithms, and applications, created by Memgraph to foster discussion and knowledge exchange in the graph community in Croatia. The group aims to host recurring meetups to explore topics such as graph databases, processing, analytics, and theory, emphasizing the growing significance of graph data science in both research and industry. The inaugural meetup is scheduled for March 2, 2022, at WESPA Spaces in Zagreb, featuring Dominik, Memgraph's CEO, who will provide insights into various graph technologies and their applications. The event also offers an opportunity for participants to share their work and engage with the community through discussions and a dedicated Discord server.