Apache Airflow was released in 2015, introducing the first popular open source solution to data pipeline orchestration. Since that time, Airflow has been widely adopted for dependency-based data workflows. A developer might orchestrate a pipeline with hundreds of tasks, with dependencies between jobs in Spark, Hadoop, and Snowflake. Since Airflow’s creation, it has powered the

The life cycle of data management includes data cleaning, extraction, integration, analysis and exploration, and machine learning models. It would be great if all of this data management could be handled with automation, but unfortunately that is not an option. For most applications, data management requires a human in the loop. A human in the

Kubernetes continues to mature as a platform for infrastructure management. At this point, many companies have well-developed workflows and deployment patterns for working with applications built on Kubernetes. The complexity of some of these deployments may be daunting, and when a new employee joins a company, that employee needs to get quickly onboarded with the

Uber needs to visualize data on a range of different surfaces. A smartphone user sees cars moving around on a map as they wait for their ride to arrive. Data scientists and operations researchers within Uber study the renderings of traffic moving throughout a city. Data visualization is core to Uber, and the company has

A frontend developer issuing a query to a backend server typically requires the developer to issue that query through an ORM or a raw database query. Prisma is an alternative to both of these data access patterns, allowing for easier database access through auto-generated, type-safe query building tailored to an existing database schema. By integrating

Machine learning workflows have had a problem for a long time: taking a model from the prototyping step and putting it into production is not an easy task. A data scientist who is developing a model is often working with different tools, or a smaller data set, or different hardware than the environment which that

Many data sources produce new data points at a very high rate. With so much data, the issue of data quality emerges. Low quality data can degrade the accuracy of machine learning models that are built around those data sources. Ideally, we would have completely clean data sources, but that’s not very realistic. One alternative

Server infrastructure traditionally consists of monolithic servers containing all of the necessary hardware to run a computer. These different hardware components are located next to each other, and do not need to communicate over a network boundary to connect the CPU and memory. LegoOS is a model for disaggregated, network-attached hardware. LegoOS disseminates the traditional

Kubernetes has become a highly usable platform for deploying and managing distributed systems.  The user experience for Kubernetes is great, but is still not as simple as a full-on serverless implementation–at least, that has been a long-held assumption. Why would you manage your own infrastructure, even if it is Kubernetes? Why not use autoscaling Lambda

Every software company is a distributed system, and distributed systems fail in unexpected ways.  This ever-present tendency for systems to fail has led to the rise of failure testing, otherwise known as chaos engineering. Chaos engineering involves the deliberate failure of subsystems within an overall system to ensure that the system itself can be resilient