A big part of data teams’ responsibilities is dealing with the unpredictable. Data pipelines don’t always run without incident: you need to rerun processes and fix data processing issues—in other words, put out data fires—to keep stakeholders happy. For every significant roadblock, additional time and effort is given over to investigation and post-mortem reports to make sure the incident doesn’t reoccur. But naturally, they keep happening.

As organizations evolve – onboarding new team members, expanding use cases, and broadening the scope of model development, their compute infrastructure grows increasingly complex. What often begins as a single cloud account using available credits can quickly expand into a hybrid mix of on-prem and cloud resources that come with different associated costs and are tailored to diverse workloads.

Today, Confluent, the data streaming pioneer, is excited to announce its entrance into MongoDB’s new AI Applications Program (MAAP). MAAP is designed to help organizations rapidly build and deploy modern generative AI (GenAI) applications at enterprise scale.

You don't need to constantly create new APIs when you wish to link digital assets to your system. A Reusable API give you the power to integrate multiple applications and platforms with ease. This can help you to save time, optimize performance, and make tracking data a much simpler process.

Hashing is a cornerstone of computer science, used extensively in data structures, cryptography, and distributed systems. Among various hashing algorithms, MurmurHash stands out for its speed, efficiency, and suitability for non-cryptographic applications. In this blog, we will delve into what MurmurHash is, when to use it, and how it compares to other hashing techniques, along with practical examples.

The world of software development is undergoing a huge transformation, due to the emergence of artificial intelligence (AI). AI-powered tools and methodologies are reshaping how we write, test, and deploy code, which is making our programming faster, more efficient, and more accessible. In this blog, we’ll explore the concept of code with AI, its applications, benefits, and its potential to redefine the future of technology. So, let’s dive in!

Snowflake combines unmatched scalability, performance, and ease of use. It simplifies the complexities of traditional data warehousing, enabling businesses to store and analyze data at scale without the overhead of infrastructure management. But to truly unlock the power of Snowflake, businesses need an efficient and secure way to move data into it.

Far too many small businesses, especially those without internal digital marketing teams or those working with external marketing professionals or agencies, overlook one of the most critical aspects of a well-rounded SEO program: data-driven SEO. According to a recent Databox research, 67% of businesses handle content marketing and SEO internally, yet many may not be fully leveraging data to optimize their efforts.

Over the past month, our primary focus has been on fixing bugs (you can check our Wiki for details on those), however, we do also have some new functionality to offer - the ability to apply labels not just to tests, but to test runs as well, as well as some other improvements.

Over the last decade, data has been hailed as the new oil, the new gold, the new currency, the new soil, and even the new oxygen. All these comparisons drive home the same point: data is important. If you’re running a business today, you need data for informed decision-making and strategy development. However, reliably extracting this data is a constant responsibility.