15 posts tagged with "kdb/q"

Throughout my career, I've had hundreds of interviews with investment banks, hedge funds, startups, and tech companies. Some were successful, others less so. I've also been on the other side of the table, interviewing candidates. Additionally, I've prepared several candidates for their interviews, all of whom successfully landed jobs. In this blog post, I want to share my tips on how to best prepare for an interview and boost your chances of landing your dream job.

One year ago, on my birthday, I launched DefconQ — a KDB/Q-focused blog aimed at helping developers and KDB/Q newcomers enhance their skills or get started with the language. What began as a small personal project to motivate myself to deepen my KDB/Q knowledge quickly evolved into a valuable resource for the KDB community. I never anticipated the level of popularity, feedback, and support the blog would receive. As DefconQ celebrates its first anniversary today, I want to share some key milestones and extend my gratitude to everyone who has supported DefconQ in any way — whether by reading the posts, liking or sharing the content, providing me with a platform to share my knowledge, or supporting the project in other ways. Read on to discover the milestones DefconQ has achieved and what's in store for the future.

A little over a year ago, KX introduced the KXperts a team of KX developers with deep passion and expertise in KDB/Q and vector databases. Their main goal is to help developers become more proficient with KX technologies, such as KDB/Q, KDB.AI, KDB Insights, PyKX and more. The team currently consists of Michaela Woods, Michael Ryaboy, Ryan Siegler, Laura Kerr, and Megan McParland, and I've had the pleasure of meeting and collaborating with most of them. The KXperts have produced a series of videos, technical blogs, tutorials, and online courses for the KX Academy, and they host live streams and meetups, all aimed at building a robust KX community.

Recently, I had the pleasure of being invited by Michaela Woods to join her and Cynthia Faus Viadé on the KX LiveStream to discuss best practices in designing your KDB/Q Tick Architecture. We began by exploring the new KDB/Q Architecture course launched recently by KX, starting with a plain vanilla tick setup and gradually progressing to more advanced configurations. Our discussion covered a range of topics including Derived Analytics, Load Balancing and Gateways, Disaster Recovery, Slow Consumers and Chained Tickerplants, as well as Intraday Writedowns and Database Best Practices. Below is a summary of our discussion, or you can watch the recording of the livestream on YouTube.

When developers first encounter KDB/Q, they are often intimidated by its "strange" syntax, which differs significantly from most other programming languages they've seen. However, understanding and familiarizing oneself with the syntax is merely the beginning. To truly master any programming language, one needs a deep understanding of its core concepts and paradigms. For instance, when learning object-oriented languages like Java or C++, you should focus on concepts such as inheritance, encapsulation, polymorphism, and data abstraction. Additionally, understanding pointers and memory allocation is crucial for mastering C++. The same principle applies to KDB/Q, a high-performance, in-memory database and programming language. In this blog post, I will share the seven most important concepts that will set you apart and enhance your skills as a KDB/Q developer. Understanding these concepts will provide insight into why KDB/Q is so powerful and favoured among quants and data enthusiasts.

In this blog post, I have a special treat for you. I sat down with Dimitri Bianco a seasoned quant who runs his own podcast and YouTube channel dedicated to all things quant finance. I have been following Dimitri for a while and always found his podcasts incredibly informative and helpful, so when the opportunity to join him on a podcast and discuss KDB/Q came up, I couldn't turn it down. Dimitri’s engagement provides graduates and students at the start of their careers with valuable insights to help them choose the right path. While I had to figure most things out on my own, Dimitri's podcast offers a valuable industry perspective into the field of quantitative finance.

Welcome back, fellow KDB/Q enthusiasts! If you've been following my blog journey, you've likely come across one of my earlier posts where I shared my favorite Go To KDB/Q Learning Resources aimed at beginners for diving into the world of KDB/Q. Congratulations on your progress thus far! Now, as seasoned learners in this domain, it's time to elevate our understanding further. In this latest post, we're shifting gears to focus on more advanced learning material. Whether you're looking to deepen your expertise or explore new horizons, join me as we explore advanced resources to expand your KDB/Q knowledge.

When I first started working as a software developer in an investment bank, I was initially placed in a C# team. After completing all the onboarding, installing Microsoft Visual Studio and gaining access to my team's codebase, I was finally able to have a look at the project I was going to work on. Little did I know, what nightmare I was about to face. Upon opening the project, I discovered that the average length of each class exceeded 15 thousand lines. Yes, you read that correctly; it's not a typo, but the shocking reality I was faced with. Navigating the code base was hard enough, but making a change without breaking anything seemed to be impossible. Luckily, I eventually transitioned to a team that embraced the concept of code organization.

Mid January, I stumbled upon a KDB/Q meetup hosted by Habla Computing in Madrid, boasting an interesting lineup of talks and speakers. Spontaneously, I opted to seize the opportunity and booked a trip to the sunny capital of Spain to attend the event. Following the meetup, during the networking session, I had the pleasure of meeting Juan, a founding partner of Habla Computing, and Jesus, the VP of Research at Habla Computing, who warmly welcomed me into the small, but growing, Spanish KDB/Q community. Engaged in a stimulating conversation about our mutual enthusiasm for KDB/Q, Juan and Jesus extended a kind invitation for me to speak at their upcoming conference, an offer I eagerly embraced. Not only did I feel honoured to have the opportunity to share my expertise on KDB/Q, but I also take every chance to escape the rainy weather of the UK and spend some time in Spain.

Over the weekend, I performed an update on the version of Docusaurus, the documentation framework utilized for my blog. A major update had been released some time ago, and due to my limited enthusiasm for front-end development, I had been postponing this task as long as possible. However, I finally decided to tackle it this weekend. After reviewing the recommended upgrade instructions provided in the official documentation, I prepared all necessary steps and then started the upgrade. Incrementally I went through all steps, running my blog locally on my laptop, ensuring that I wouldn't break anything. After completing the upgrade, I conducted one final verification to confirm that everything was running as expected in my browser. Satisfied with the results, I committed my changes and pushed them to the repository of my blog.

Now, all that remained was deploying the new version to Github Pages. I executed the command USE_SSH=true yarn deploy as I typically do, pressed enter, and awaited the customary [INFO] messages. However, instead of the expected outcome, I was confronted with a bright, red error message.