Systems | Development | Analytics | API | Testing

Recently, I published a blog on whether self-service BI is attainable, and spoiler alert: it certainly is. Of course, anything of value usually does require a bit of planning, collaboration, and effort. After the article was published, I began having conversations with technical leaders, analysts, and analytics engineers, and the topic of data modeling for self-service analytics came up repeatedly.

Extract, transform, load (ETL) is a critical component of data warehousing, as it enables efficient data transfer between systems. In the current scenario, Python is considered the most popular language for ETL. There are numerous Python-based ETL tools available in the market, which can be used to define data warehouse workflows. However, choosing the right ETL tool or your needs can be a daunting task.

With low-code tools on the rise, building an Application Programming Interface (API) is simpler than ever. Given the ease of development, it is easy to overlook potential problems. Taking a bit more time in the design phase can ensure the API is truly useful, secure, scalable, and stable. Here, we’ll discuss the top ten most common API development mistakes to avoid.

Built with BigQuery: How to Accelerate Data-Centric AI development with Google Cloud and Snorkel AI.

The point of evidence is to guide decisions, so transforming a business into being evidence-based has to start with leaders.

Artificial Intelligence has drastically changed the way we look at things today. What was previously possible only by human intervention can now be done with the help of AI. With AI making inroads into most industries, the world of website creation is not far behind. Many large e-commerce companies like Amazon are using AI technology to better understand their customers, provide product recommendations, and improve the user experience on their websites.

In today's increasingly digital world, businesses of all sizes rely on data to make informed decisions and drive growth. This is why more and more organizations have started using data warehouse platforms. These crucial tools help businesses store, manage, and analyze data in one central location. In addition, a data warehouse platform makes accessing and processing large amounts of data easier, enabling businesses to gain valuable insights and improve their operations.

In the previous part of this series, we optimized our Lambda function. However, our API is open to the public — anyone with the URL can use it and get a response. In this take, we are going to secure our API using a tool called Amazon Cognito. This will only allow authenticated users access to our endpoints. Ready? Let’s go!

Ruby is a wonderful language, made for humans first and machines second. It is easy to read and write. There are plenty of ways to write anything, and you can often guess its standard library by typing the name of the method you would have chosen yourself. Because of this, Ruby's arguments are very flexible, which lets us express our APIs very clearly. But this comes with a drawback: Ruby is quite hard to parse for C extension developers!

Often it can be difficult for smaller studios to focus on administrative tasks when key team members wear multiple hats and are constantly being pulled in different directions. That is why we developed Helix Remote Administration, which lets teams focus on their work while our experts set up, maintain, and optimize their Helix Core server.