With the volume, velocity, and variety of today’s data, we have all started to acknowledge that there is no one-size-fits-all database for all data needs. Instead, many companies shifted towards choosing the right data store for a specific use case or project. The distribution of data across different data stores brought the challenge of consolidating data for analytics.
When you want to stay ahead and on top of things in a fast-moving industry, machine learning (ML) is surely one of the trending solutions. Today, innovative companies already have leading Machine Learning tools well-integrated into their processes. In comparison, your start could seem dreadfully slow. Or maybe you just don’t have the time or resources to invest in running your own Machine Learning training infrastructure.
SQL is great, but sometimes you may need something else. By and large, the prevalent type of data that data engineers deal with on a regular basis is relational. Tables in a data warehouse, transactional data in Online Transactional Processing (OLTP) databases — they can all be queried and accessed using SQL. But does it mean that NoSQL is irrelevant for data engineering?
When using services created by other people, it’s often neither obvious what they mean, let alone how to fix them. One of these error messages you might see when using Amazon API Gateway is “encoding not enabled”. The first question here is, what kind of encoding does this error message refer to? The first thought might go into the video or audio encoding direction and lead to a dead-end since you probably didn’t send any audio or video files.
One of the serverless best practices is one-purpose functions. You should keep your Lambda functions small and solve exactly one use-case. This way, you can optimize them better and keep potential security problems contained. But creating many small functions can get overwhelming quickly. Even small projects can end up with more than 20 Lambda functions.
As Werner Vogels says: “Everything fails all the time.” Data is the new oil. We rely on it not only to make decisions but to operate as a business in general. Data loss can lead to significant financial consequences and damaged reputation. In this article, you can find ten actionable methods to protect your most valuable resources.
Dashbird now scans your serverless infrastructure for industry best practices. It’s the antidote for chaos. We’re excited to introduce the Dashbird Well-Architected Insights – a continuous insights scanner combined with Well-Architected reports. The new feature provides serverless developers with insights and recommendations to continually improve their applications and keep them secure, compliant, optimized, and efficient.
In this article, we’ll be rewinding back to the very beginning of the AWS Well-Architected Framework to understand how and why it came to be, and why is it of utmost importance, but very often underrated, for serverless developers to learn, understand and apply this framework of best-practices. We’ll also be looking into how the framework has evolved and how it should be used in 2021.
When building cloud-based systems and serverless systems, in particular, it’s crucial to stay on top of things. Your infrastructure will be miles away from you and not a device you hold in your hands like when you build a frontend. That’s why adding a monitoring solution to your stack, which offers a pre-configured serverless failure detection, should be one of the first decisions.
A platform-agnostic way of accessing credentials in Python. Even though AWS enables fine-grained access control via IAM roles, sometimes in our scripts we need to use credentials to external resources, not related to AWS, such as API keys, database credentials, or passwords of any kind. There are a myriad of ways of handling such sensitive data. In this article, I’ll show you an incredibly simple and effective way to manage that using AWS and Python.
